update color palette, dependencies, fix in tools.yaml

Reviewed-on: #19

.astro/settings.json

@@ -1,5 +1,5 @@
 {
 	"_variables": {
-		"lastUpdateCheck": 1754571688630
+		"lastUpdateCheck": 1755901660216
 	}
 }

.env.example

@@ -59,8 +59,7 @@ FORENSIC_AUDIT_RETENTION_HOURS=24
 FORENSIC_AUDIT_MAX_ENTRIES=50
 
 # === AI SEMANTIC SEARCH ===
-# Enable semantic search (highly recommended for better results)
-AI_EMBEDDINGS_ENABLED=true
+# semantic search
 AI_EMBEDDINGS_ENDPOINT=https://api.mistral.ai/v1/embeddings
 AI_EMBEDDINGS_API_KEY=your-embeddings-api-key-here
 AI_EMBEDDINGS_MODEL=mistral-embed
@@ -101,17 +100,11 @@ AI_SOFTWARE_SELECTION_RATIO=0.5  # 50% software tools (increase for more tool re
 
 # AI selection limits
 AI_MAX_SELECTED_ITEMS=25
-AI_MAX_TOOLS_TO_ANALYZE=20
-AI_MAX_CONCEPTS_TO_ANALYZE=10
 
 # Efficiency thresholds
 AI_EMBEDDINGS_MIN_TOOLS=8
 AI_EMBEDDINGS_MAX_REDUCTION_RATIO=0.75
 
-# Fallback limits when embeddings are disabled
-AI_NO_EMBEDDINGS_TOOL_LIMIT=25
-AI_NO_EMBEDDINGS_CONCEPT_LIMIT=10
-
 # === Rate Limiting & Timing ===
 AI_MICRO_TASK_TOTAL_LIMIT=30
 AI_MICRO_TASK_DELAY_MS=500
@@ -121,10 +114,6 @@ AI_RATE_LIMIT_DELAY_MS=2000
 AI_EMBEDDINGS_BATCH_SIZE=10
 AI_EMBEDDINGS_BATCH_DELAY_MS=1000
 
-# === Context Management ===
-AI_MAX_CONTEXT_TOKENS=4000
-AI_MAX_PROMPT_TOKENS=2500
-
 # === Confidence Scoring ===
 CONFIDENCE_SEMANTIC_WEIGHT=0.5
 CONFIDENCE_SUITABILITY_WEIGHT=0.5

find-duplicates.mjs

@@ -0,0 +1,333 @@
+#!/usr/bin/env node
+// find-duplicate-functions.mjs
+// Usage:
+//   node find-duplicate-functions.mjs [rootDir] [--mode exact|struct] [--min-lines N] [--json]
+// Example:
+//   node find-duplicate-functions.mjs . --mode struct --min-lines 3
+
+import fs from "fs";
+import path from "path";
+import * as url from "url";
+import ts from "typescript";
+
+const __dirname = path.dirname(url.fileURLToPath(import.meta.url));
+
+/** -------- CLI OPTIONS -------- */
+const args = process.argv.slice(2);
+let rootDir = ".";
+let mode = "struct"; // "exact" | "struct"
+let minLines = 3;
+let outputJson = false;
+
+for (let i = 0; i < args.length; i++) {
+  const a = args[i];
+  if (!a.startsWith("--") && rootDir === ".") {
+    rootDir = a;
+  } else if (a === "--mode") {
+    mode = (args[++i] || "struct").toLowerCase();
+    if (!["exact", "struct"].includes(mode)) {
+      console.error("Invalid --mode. Use 'exact' or 'struct'.");
+      process.exit(1);
+    }
+  } else if (a === "--min-lines") {
+    minLines = parseInt(args[++i] || "3", 10);
+  } else if (a === "--json") {
+    outputJson = true;
+  }
+}
+
+/** -------- FILE DISCOVERY -------- */
+const DEFAULT_IGNORES = new Set([
+  "node_modules",
+  ".git",
+  ".next",
+  ".vercel",
+  "dist",
+  "build",
+  ".astro", // Astro's generated cache dir
+]);
+
+const VALID_EXTS = new Set([".ts", ".tsx", ".astro", ".mts", ".cts"]);
+
+function walk(dir) {
+  /** @type {string[]} */
+  const out = [];
+  const entries = fs.readdirSync(dir, { withFileTypes: true });
+  for (const e of entries) {
+    const p = path.join(dir, e.name);
+    if (e.isDirectory()) {
+      if (DEFAULT_IGNORES.has(e.name)) continue;
+      out.push(...walk(p));
+    } else if (e.isFile() && VALID_EXTS.has(path.extname(e.name))) {
+      out.push(p);
+    }
+  }
+  return out;
+}
+
+/** -------- ASTRO CODE EXTRACTION --------
+ * Extract TS/JS code from:
+ * - frontmatter: --- ... ---
+ * - <script ...> ... </script>
+ */
+function extractCodeFromAstro(source) {
+  /** @type {{code:string, offset:number}[]} */
+  const blocks = [];
+
+  // Frontmatter (must be at top in Astro)
+  // Match the FIRST pair of --- ... ---
+  const fm = source.startsWith("---")
+    ? (() => {
+        const end = source.indexOf("\n---", 3);
+        if (end !== -1) {
+          const front = source.slice(3, end + 1); // include trailing \n
+          return { start: 0, end: end + 4, code: front };
+        }
+        return null;
+      })()
+    : null;
+  if (fm) {
+    // offset for line numbers is after the first '---\n'
+    blocks.push({ code: fm.code, offset: 4 }); // rough; we’ll fix line numbers via positions later
+  }
+
+  // <script ...> ... </script>
+  const scriptRe = /<script\b[^>]*>([\s\S]*?)<\/script>/gi;
+  let m;
+  while ((m = scriptRe.exec(source))) {
+    const code = m[1] || "";
+    blocks.push({ code, offset: indexToLine(source, m.index) });
+  }
+
+  return blocks;
+}
+
+/** -------- UTIL: index -> 1-based line -------- */
+function indexToLine(text, idx) {
+  let line = 1;
+  for (let i = 0; i < idx && i < text.length; i++) {
+    if (text.charCodeAt(i) === 10) line++;
+  }
+  return line;
+}
+
+/** -------- AST HELPERS -------- */
+function createSourceFile(virtualPath, code) {
+  return ts.createSourceFile(
+    virtualPath,
+    code,
+    ts.ScriptTarget.Latest,
+    /*setParentNodes*/ true,
+    virtualPath.endsWith(".tsx") ? ts.ScriptKind.TSX : ts.ScriptKind.TS
+  );
+}
+
+// Normalize AST to a structural signature string
+function structuralSignature(node) {
+  /** @type {string[]} */
+  const parts = [];
+  const visit = (n) => {
+    // Skip trivia: comments/whitespace are already not in AST
+    const kindName = ts.SyntaxKind[n.kind] || `K${n.kind}`;
+    switch (n.kind) {
+      case ts.SyntaxKind.Identifier:
+        parts.push("Id");
+        return;
+      case ts.SyntaxKind.PrivateIdentifier:
+        parts.push("PrivId");
+        return;
+      case ts.SyntaxKind.StringLiteral:
+      case ts.SyntaxKind.NoSubstitutionTemplateLiteral:
+      case ts.SyntaxKind.TemplateHead:
+      case ts.SyntaxKind.TemplateMiddle:
+      case ts.SyntaxKind.TemplateTail:
+        parts.push("Str");
+        return;
+      case ts.SyntaxKind.NumericLiteral:
+        parts.push("Num");
+        return;
+      case ts.SyntaxKind.TrueKeyword:
+      case ts.SyntaxKind.FalseKeyword:
+        parts.push("Bool");
+        return;
+      case ts.SyntaxKind.NullKeyword:
+      case ts.SyntaxKind.UndefinedKeyword:
+        parts.push("Nil");
+        return;
+      case ts.SyntaxKind.PropertyAssignment:
+      case ts.SyntaxKind.ShorthandPropertyAssignment:
+      case ts.SyntaxKind.MethodDeclaration:
+      case ts.SyntaxKind.MethodSignature:
+        parts.push("Prop");
+        break;
+      default:
+        parts.push(kindName);
+    }
+    n.forEachChild(visit);
+  };
+  visit(node);
+  return parts.join("|");
+}
+
+function getFunctionInfo(sf, filePath) {
+  /** @type {Array<{
+    name: string,
+    bodyText: string,
+    structKey: string,
+    start: number,
+    end: number,
+    startLine: number,
+    endLine: number
+  }>} */
+  const out = [];
+
+  const addFunc = (nameNode, bodyNode) => {
+    if (!bodyNode) return;
+    const bodyText = bodyNode.getText(sf).trim();
+    const start = bodyNode.getStart(sf);
+    const end = bodyNode.getEnd();
+    const { line: startLine } = sf.getLineAndCharacterOfPosition(start);
+    const { line: endLine } = sf.getLineAndCharacterOfPosition(end);
+    const name =
+      nameNode && ts.isIdentifier(nameNode) ? nameNode.escapedText.toString() : "(anonymous)";
+
+    // min-lines filter
+    const lines = bodyText.split(/\r?\n/).filter(Boolean);
+    if (lines.length < minLines) return;
+
+    // structural signature from the body
+    const structKey = structuralSignature(bodyNode);
+
+    out.push({
+      name,
+      bodyText,
+      structKey,
+      start,
+      end,
+      startLine: startLine + 1,
+      endLine: endLine + 1,
+    });
+  };
+
+  const visit = (node) => {
+    if (ts.isFunctionDeclaration(node) && node.body) {
+      addFunc(node.name ?? null, node.body);
+    } else if (
+      ts.isFunctionExpression(node) ||
+      ts.isArrowFunction(node)
+    ) {
+      // find name if it’s assigned: const foo = () => {}
+      let name = null;
+      if (node.parent && ts.isVariableDeclaration(node.parent) && node.parent.name) {
+        name = node.parent.name;
+      } else if (
+        node.parent &&
+        ts.isPropertyAssignment(node.parent) &&
+        ts.isIdentifier(node.parent.name)
+      ) {
+        name = node.parent.name;
+      } else if (node.name) {
+        name = node.name;
+      }
+      if (node.body) addFunc(name, node.body);
+    } else if (ts.isMethodDeclaration(node) && node.body) {
+      addFunc(node.name, node.body);
+    }
+    node.forEachChild(visit);
+  };
+
+  visit(sf);
+  return out;
+}
+
+/** -------- MAIN SCAN -------- */
+const files = walk(path.resolve(process.cwd(), rootDir));
+
+/** Maps from hash -> occurrences */
+const groups = new Map();
+/** Helper for exact hash */
+import crypto from "crypto";
+const exactHash = (text) => crypto.createHash("sha1").update(text.replace(/\s+/g, " ").trim()).digest("hex");
+
+for (const file of files) {
+  try {
+    const ext = path.extname(file).toLowerCase();
+    const raw = fs.readFileSync(file, "utf8");
+
+    /** @type {Array<{virtualPath:string, code:string, lineOffset:number}>} */
+    const codeUnits = [];
+
+    if (ext === ".astro") {
+      const blocks = extractCodeFromAstro(raw);
+      blocks.forEach((b, i) => {
+        codeUnits.push({
+          virtualPath: file + `#astro${i + 1}.ts`,
+          code: b.code,
+          lineOffset: b.offset || 1,
+        });
+      });
+    } else {
+      codeUnits.push({ virtualPath: file, code: raw, lineOffset: 1 });
+    }
+
+    for (const { virtualPath, code, lineOffset } of codeUnits) {
+      const sf = createSourceFile(virtualPath, code);
+      const funcs = getFunctionInfo(sf, file);
+      for (const f of funcs) {
+        const key =
+          mode === "exact" ? exactHash(f.bodyText) : crypto.createHash("sha1").update(f.structKey).digest("hex");
+        const item = {
+          file,
+          where:
+            ext === ".astro"
+              ? `${path.relative(process.cwd(), file)}:${f.startLine + lineOffset - 1}-${f.endLine + lineOffset - 1}`
+              : `${path.relative(process.cwd(), file)}:${f.startLine}-${f.endLine}`,
+          name: f.name,
+          lines: f.endLine - f.startLine + 1,
+          preview: f.bodyText.split(/\r?\n/).slice(0, 5).join("\n") + (f.endLine - f.startLine + 1 > 5 ? "\n..." : ""),
+        };
+        if (!groups.has(key)) groups.set(key, []);
+        groups.get(key).push(item);
+      }
+    }
+  } catch (e) {
+    console.warn(`⚠️ Skipping ${file}: ${e.message}`);
+  }
+}
+
+/** -------- REPORT -------- */
+const dupes = [...groups.entries()]
+  .map(([key, arr]) => ({ key, items: arr }))
+  .filter((g) => g.items.length > 1)
+  .sort((a, b) => b.items.length - a.items.length);
+
+if (outputJson) {
+  console.log(JSON.stringify({ mode, minLines, groups: dupes }, null, 2));
+  process.exit(0);
+}
+
+if (dupes.length === 0) {
+  console.log(`✅ No duplicate functions found (mode=${mode}, min-lines=${minLines}).`);
+  process.exit(0);
+}
+
+console.log(`\nFound ${dupes.length} duplicate group(s) (mode=${mode}, min-lines=${minLines}):\n`);
+dupes.forEach((g, i) => {
+  console.log(`== Group ${i + 1} (${g.items.length} matches) ==`);
+  const example = g.items[0];
+  console.log(`   Sample (${example.lines} lines) from ${example.where}${example.name ? ` [${example.name}]` : ""}`);
+  console.log("   ---");
+  console.log(indent(example.preview, "   "));
+  console.log("   ---");
+  g.items.forEach((it) => {
+    console.log(` • ${it.where}${it.name ? ` [${it.name}]` : ""} (${it.lines} lines)`);
+  });
+  console.log();
+});
+
+function indent(s, pre) {
+  return s
+    .split("\n")
+    .map((l) => pre + l)
+    .join("\n");
+}

package.json

@@ -10,15 +10,14 @@
     "astro": "astro"
   },
   "dependencies": {
-    "@astrojs/node": "^9.3.0",
-    "@aws-sdk/client-s3": "^3.864.0",
-    "@aws-sdk/s3-request-presigner": "^3.864.0",
-    "astro": "^5.12.3",
+    "@astrojs/node": "^9.4.3",
+    "astro": "^5.13.7",
     "cookie": "^1.0.2",
-    "dotenv": "^16.4.5",
-    "jose": "^5.2.0",
+    "dotenv": "^16.6.1",
+    "jose": "^5.10.0",
     "js-yaml": "^4.1.0",
     "jsonwebtoken": "^9.0.2",
+    "simple-boost": "^2.0.2",
     "zod": "^3.25.76"
   },
   "devDependencies": {

src/components/ContributionButton.astro

@@ -1,5 +1,5 @@
 ---
-// src/components/ContributionButton.astro - CLEANED: Removed duplicate auth script
+// src/components/ContributionButton.astro
 export interface Props {
   type: 'edit' | 'new' | 'write';
   toolName?: string;

src/components/ShareButton.astro

@@ -1,5 +1,5 @@
 ---
-import { createToolSlug } from '../utils/toolHelpers.js';
+import { createToolSlug } from '../utils/clientUtils.js';
 
 export interface Props {
   toolName: string;

src/components/TargetedScenarios.astro

@@ -4,7 +4,6 @@ import { getToolsData } from '../utils/dataService.js';
 const data = await getToolsData();
 const scenarios = data.scenarios || [];
 
-// Configuration
 const maxDisplayed = 9;
 const displayedScenarios = scenarios.slice(0, maxDisplayed);
 ---

src/components/ToolFilters.astro

@@ -55,7 +55,7 @@ const sortedTags = Object.entries(tagFrequency)
           
           <!-- Semantic Search Toggle - Inline -->
           <div id="semantic-search-container" class="semantic-search-inline hidden">
-            <label class="semantic-toggle-wrapper" title="Semantische Suche verwendet Embeddings. Dadurch kann mit natürlicher Sprache/Begriffen gesucht werden, die Ergebnisse richten sich nach der euklidischen Distanz.">
+            <label class="semantic-toggle-wrapper" title="Semantische Suche verwendet Embeddings. Dadurch kann mit natürlicher Sprache/Begriffen gesucht werden, die Ergebnisse richten sich nach der cosinus-Distanz.">
               <input type="checkbox" id="semantic-search-enabled" disabled/>
               <div class="semantic-checkbox-custom"></div>
               <span class="semantic-toggle-label">
@@ -306,8 +306,7 @@ const sortedTags = Object.entries(tagFrequency)
 </div>
 
 <script define:vars={{ toolsData: data.tools, tagFrequency, sortedTags }}>
-  window.toolsData = toolsData;
-  
+  window.toolsData = toolsData;  
   document.addEventListener('DOMContentLoaded', () => {
     const elements = {
       searchInput: document.getElementById('search-input'),
@@ -359,7 +358,7 @@ const sortedTags = Object.entries(tagFrequency)
       try {
         const res = await fetch('/api/ai/embeddings-status');
         const { embeddings } = await res.json();
-        semanticSearchAvailable = embeddings?.enabled && embeddings?.initialized;
+        semanticSearchAvailable = embeddings?.initialized;
 
         if (semanticSearchAvailable) {
           elements.semanticContainer.classList.remove('hidden');
@@ -393,6 +392,13 @@ const sortedTags = Object.entries(tagFrequency)
         return null;
       }
     }
+
+    function isToolHosted(tool) {
+      return tool.projectUrl !== undefined && 
+            tool.projectUrl !== null && 
+            tool.projectUrl !== "" && 
+            tool.projectUrl.trim() !== "";
+    }
     
     function toggleCollapsible(toggleBtn, content, storageKey) {
       const isCollapsed = toggleBtn.getAttribute('data-collapsed') === 'true';
@@ -433,13 +439,6 @@ const sortedTags = Object.entries(tagFrequency)
       }
     }
     
-    function isToolHosted(tool) {
-      return tool.projectUrl !== undefined && 
-             tool.projectUrl !== null && 
-             tool.projectUrl !== "" && 
-             tool.projectUrl.trim() !== "";
-    }
-    
     function initTagCloud() {
       const visibleCount = 20;
       elements.tagCloudItems.forEach((item, index) => {

src/config/prompts.ts

@@ -1,203 +1,263 @@
 // src/config/prompts.ts
 
+const RELEVANCE_RUBRIC = `
+TASK RELEVANCE (INTEGER 0–100, NO %):
+- 55–65 = Basis/ok
+- 66–75 = Gut geeignet
+- 76–85 = Sehr gut geeignet
+- >85   = Nur bei nahezu perfekter Übereinstimmung
+`.trim();
+
+const STRICTNESS = `
+STRICTNESS:
+- Output MUST be pure JSON (no prose, no code fences, no trailing commas).
+- Use EXACT item names as provided (casing/spelling must match).
+- Do NOT invent items or fields. If unsure, select fewer.
+`.trim();
+
 export const AI_PROMPTS = {
-  
-  toolSelection: (mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number) => {
-    const modeInstruction = mode === 'workflow' 
-      ? 'Workflow mit 15-25 Items über alle Phasen. PFLICHT: Mindestens 40% Methoden, Rest Tools/Konzepte.'
-      : 'Spezifische Lösung mit 4-10 Items. PFLICHT: Mindestens 30% Methoden wenn verfügbar.';
+  enhancementQuestions: (input: string) => {
+    return `Sie sind DFIR-Experte. Ein Nutzer beschreibt unten ein Szenario/Problem.
 
-    return `Du bist ein DFIR-Experte. Wähle die BESTEN Items aus dem vorgefilterten Set.
+ZIEL:
+- Stellen Sie NUR dann 1–3 präzise Rückfragen, wenn entscheidende forensische Lücken die weitere Analyse/Toolauswahl PHASENREIHENFOLGE oder EVIDENCE-STRATEGIE wesentlich beeinflussen würden.
+- Wenn ausreichend abgedeckt: Geben Sie eine leere Liste [] zurück.
 
-AUSWAHLMETHODE: ${selectionMethod}
-${selectionMethod === 'embeddings_candidates' ? 
-  '✓ Semantisch relevante Items bereits vorgefiltert\n✓ Wähle die BESTEN für die konkrete Aufgabe' :
-  '✓ Vollständige Datenbank verfügbar\n✓ Wähle die relevantesten Items'}
+PRIORITÄT DER THEMEN (in dieser Reihenfolge prüfen):
+1) Available Evidence & Artefakte (z.B. RAM-Dump, Disk-Image, Logs, PCAP, Registry, Cloud/Audit-Logs)
+2) Scope/Systems (konkrete Plattformen/Assets/Identitäten/Netzsegmente)
+3) Investigation Objectives (Ziele: IOC-Extraktion, Timeline, Impact, Attribution)
+4) Timeline/Timeframe (kritische Zeitfenster, Erhalt flüchtiger Daten)
+5) Legal & Compliance (Chain of Custody, Aufbewahrung, DSGVO/Branchenvorgaben)
+6) Technical Constraints (Ressourcen, Zugriffsrechte, Tooling/EDR)
+
+FRAGEN-QUALITÄT:
+- Forensisch spezifisch und entscheidungsrelevant (keine Allgemeinplätze).
+- Eine Frage pro Thema, keine Dopplungen.
+- Antwortbar vom Nutzer (keine Spekulation, keine “Beweise senden”-Aufforderungen).
+- Maximal 18 Wörter, endet mit "?".
+
+VALIDIERUNG:
+- Stellen Sie NUR Fragen zu Themen, die im Nutzertext NICHT hinreichend konkret beantwortet sind (keine Wiederholung bereits gegebener Details).
+- Wenn alle priorisierten Themen ausreichend sind → [].
+
+ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
+[
+  "präzise Frage 1?",
+  "präzise Frage 2?",
+  "präzise Frage 3?"
+]
+
+NUTZER-EINGABE:
+${input}`.trim();
+  },
+  toolSelection: (mode: string, userQuery: string, maxSelectedItems: number) => {
+    const modeInstruction =
+      mode === 'workflow'
+        ? 'Workflow mit 15–25 Items über alle Phasen. Pflicht: ~40% Methoden, Rest Software/Konzepte (falls verfügbar).'
+        : 'Spezifische Lösung mit 4–10 Items. Pflicht: ≥30% Methoden (falls verfügbar).';
+
+    return `Du bist DFIR-Experte. Wähle die BESTEN Items aus dem bereits semantisch vorgefilterten Set für die konkrete Aufgabe.
 
 ${modeInstruction}
 
 ANFRAGE: "${userQuery}"
 
-VERFÜGBARE ITEM-TYPEN:
-- TOOLS (type: "software"/"method") → praktische Anwendungen und Vorgehensweisen  
-- KONZEPTE (type: "concept") → theoretisches Wissen und Methodiken
+ITEM-TYPEN:
+- TOOLS (type: "software" | "method")
+- KONZEPTE (type: "concept")
 
-AUSWAHLSTRATEGIE:
-1. **ERSTE PRIORITÄT: Relevanz zur Anfrage**
-  - Direkt anwendbar auf das Problem
-  - Löst die Kernherausforderung
-
-2. **ZWEITE PRIORITÄT: Ausgewogene Mischung**
-  - Tools/Methoden für praktische Umsetzung → selectedTools
-  - Konzepte für methodisches Verständnis → selectedConcepts
-  - WICHTIG: Auch Konzepte auswählen, nicht nur Tools!
-
-3. **QUALITÄT > QUANTITÄT**
-  - Lieber weniger perfekte Items als viele mittelmäßige
-  - Jedes Item muss begründbar sein
+AUSWAHLPRINZIPIEN:
+1) Relevanz zur Anfrage (direkt anwendbar, adressiert Kernproblem)
+2) Ausgewogene Mischung (Praxis: selectedTools; Methodik: selectedConcepts)
+3) Qualität > Quantität (lieber weniger, dafür passgenau)
+4) Keine Erfindungen. Wenn etwas nicht passt, wähle weniger.
 
 AUSWAHLREGELN:
-- Wähle ${mode === 'workflow' ? '15-25' : '4-10'} Items total, max ${maxSelectedItems}
-- BEIDE Arrays füllen: selectedTools UND selectedConcepts
-- Mindestens 1-2 Konzepte auswählen für methodische Fundierung
-- Tools: 40% Methoden (type="method"), Rest Software (type="software")
+- Wähle ${mode === 'workflow' ? '15–25' : '4–10'} Items total (max ${maxSelectedItems})
+- Fülle BEIDE Arrays: selectedTools UND selectedConcepts
+- Mindestens 1–2 Konzepte (falls verfügbar)
+- Bevorzugt ~40% Methoden (Workflow) bzw. ≥30% Methoden (Tool-Modus), sofern vorhanden
+- Sortiere selectedTools grob nach Eignung (bestes zuerst)
 
-ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT:
+Skalenhinweis (für spätere Schritte – einheitlich):
+${RELEVANCE_RUBRIC}
+
+${STRICTNESS}
+
+ANTWORT (NUR JSON):
 {
-  "selectedTools": ["ToolName1", "MethodName1", ...],
-  "selectedConcepts": ["ConceptName1", "ConceptName2", ...],
-  "reasoning": "Kurze Begründung mit Erwähnung der Tool/Konzept-Balance"
+  "selectedTools": ["ToolName1", "MethodName1", "..."],
+  "selectedConcepts": ["ConceptName1", "ConceptName2", "..."],
+  "reasoning": "Sehr kurz: Balance/Abdeckung begründen"
 }`;
   },
 
   toolSelectionWithData: (basePrompt: string, toolsToSend: any[], conceptsToSend: any[]) => {
     return `${basePrompt}
 
-VERFÜGBARE TOOLS (${toolsToSend.length} Items - Methoden und Software):
+VERFÜGBARE TOOLS (${toolsToSend.length}):
 ${JSON.stringify(toolsToSend, null, 2)}
 
-VERFÜGBARE KONZEPTE (${conceptsToSend.length} Items - theoretisches Wissen):
+VERFÜGBARE KONZEPTE (${conceptsToSend.length}):
 ${JSON.stringify(conceptsToSend, null, 2)}
 
-WICHTIGER HINWEIS: Wähle sowohl aus TOOLS als auch aus KONZEPTEN aus! Konzepte sind essentiell für methodische Fundierung.`;
+WICHTIG:
+- Wähle nur aus obigen Listen. Keine neuen Namen.
+- Nutze exakte Namen. Keine Synonyme/Varianten.
+
+Hinweis zur einheitlichen Relevanz-Skala:
+${RELEVANCE_RUBRIC}
+
+${STRICTNESS}`;
   },
 
   scenarioAnalysis: (isWorkflow: boolean, userQuery: string) => {
     const analysisType = isWorkflow ? 'Szenario' : 'Problem';
-    const focus = isWorkflow ? 
-      'Angriffsvektoren, betroffene Systeme, Zeitkritikalität' :
-      'Kernherausforderung, verfügbare Daten, methodische Anforderungen';
+    const focus = isWorkflow
+      ? 'Angriffsvektoren, betroffene Systeme, Zeitkritikalität'
+      : 'Kernherausforderung, verfügbare Daten, methodische Anforderungen';
 
     return `DFIR-Experte: Analysiere das ${analysisType}.
 
 ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
-
 Fokus: ${focus}
 
-Antwort: Fließtext ohne Listen, max 100 Wörter.`;
+Antwort: Fließtext, max 100 Wörter. Keine Liste, keine Einleitung.`;
   },
 
   investigationApproach: (isWorkflow: boolean, userQuery: string) => {
     const approachType = isWorkflow ? 'Untersuchungsansatz' : 'Lösungsansatz';
-    const focus = isWorkflow ?
-      'Triage-Prioritäten, Phasenabfolge, Kontaminationsvermeidung' :
-      'Methodenauswahl, Validierung, Integration';
+    const focus = isWorkflow
+      ? 'Triage-Prioritäten, Phasenabfolge, Kontaminationsvermeidung'
+      : 'Methodenauswahl, Validierung, Integration';
 
     return `Entwickle einen ${approachType}.
 
 ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
-
 Fokus: ${focus}
 
-Antwort: Fließtext ohne Listen, max 100 Wörter.`;
+Antwort: Fließtext, max 100 Wörter.`;
   },
 
   criticalConsiderations: (isWorkflow: boolean, userQuery: string) => {
-    const focus = isWorkflow ? 
-      'Beweissicherung vs. Gründlichkeit, Chain of Custody' : 
-      'Tool-Validierung, False Positives/Negatives, Qualifikationen';
+    const focus = isWorkflow
+      ? 'Beweissicherung vs. Gründlichkeit, Chain of Custody'
+      : 'Tool-Validierung, False Positives/Negatives, Qualifikationen';
 
     return `Identifiziere kritische Überlegungen.
 
 ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
-
 Fokus: ${focus}
 
-Antwort: Fließtext ohne Listen, max 100 Wörter.`;
+Antwort: Fließtext, max 100 Wörter.`;
   },
 
   phaseToolSelection: (userQuery: string, phase: any, phaseTools: any[]) => {
     const methods = phaseTools.filter(t => t.type === 'method');
     const tools = phaseTools.filter(t => t.type === 'software');
-    
+
     if (phaseTools.length === 0) {
       return `Keine Methoden/Tools für Phase "${phase.name}" verfügbar. Antworte mit leerem Array: []`;
     }
-    
-    return `Du bist ein DFIR-Experte. Wähle die 2-3 BESTEN Items für Phase "${phase.name}".
+
+    return `Wähle die 2–3 BESTEN Items für Phase "${phase.name}".
 
 SZENARIO: "${userQuery}"
-PHASE: ${phase.name} - ${phase.description || ''}
+PHASE: ${phase.name} — ${phase.description || ''}
 
-VERFÜGBARE ITEMS (bereits von KI vorausgewählt):
+VERFÜGBARE ITEMS:
 ${methods.length > 0 ? `
 METHODEN (${methods.length}):
-${methods.map((method: any) => 
-  `- ${method.name}
-  Typ: ${method.type}
-  Beschreibung: ${method.description}
-  Domains: ${method.domains?.join(', ') || 'N/A'}
-  Skill Level: ${method.skillLevel}`
+${methods.map((m: any) =>
+  `- ${m.name}
+  Typ: ${m.type}
+  Beschreibung: ${m.description}
+  Domains: ${m.domains?.join(', ') || 'N/A'}
+  Skill Level: ${m.skillLevel}`
 ).join('\n\n')}
 ` : 'Keine Methoden verfügbar'}
 
 ${tools.length > 0 ? `
-SOFTWARE TOOLS (${tools.length}):
-${tools.map((tool: any) => 
-  `- ${tool.name}
-  Typ: ${tool.type}
-  Beschreibung: ${tool.description}
-  Plattformen: ${tool.platforms?.join(', ') || 'N/A'}
-  Skill Level: ${tool.skillLevel}`
+SOFTWARE (${tools.length}):
+${tools.map((t: any) =>
+  `- ${t.name}
+  Typ: ${t.type}
+  Beschreibung: ${t.description}
+  Plattformen: ${t.platforms?.join(', ') || 'N/A'}
+  Skill Level: ${t.skillLevel}`
 ).join('\n\n')}
 ` : 'Keine Software-Tools verfügbar'}
 
-AUSWAHLREGELN FÜR PHASE "${phase.name}":
-1. Wähle die 2-3 BESTEN Items für diese spezifische Phase
-2. Priorisiere Items, die DIREKT für "${phase.name}" relevant sind
-3. Mindestens 1 Methode wenn verfügbar, Rest Software-Tools
-4. Begründe WARUM jedes Item für diese Phase optimal ist
+REGELN:
+1) 2–3 Items, direkt phasenrelevant; mind. 1 Methode, falls verfügbar
+2) Begründung pro Item (präzise, anwendungsbezogen)
+3) Verwende EXAKTE Namen aus den Listen. Keine Erfindungen.
 
-WICHTIG: Verwende EXAKT die Namen wie oben aufgelistet (ohne Präfixe wie M1./T2.)!
+${RELEVANCE_RUBRIC}
 
-ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB:
+${STRICTNESS}
+
+ANTWORT (NUR JSON):
 [
   {
-    "toolName": "Exakter Name aus der Liste oben",
-    "taskRelevance": 85,
-    "justification": "Detaillierte Begründung (60-80 Wörter) warum optimal für ${phase.name} - erkläre Anwendung, Vorteile und spezifische Relevanz",
-    "limitations": ["Mögliche Einschränkung für diese Phase"]
+    "toolName": "Exakter Name",
+    "taskRelevance": 0,
+    "justification": "60–80 Wörter zur phasenspezifischen Eignung",
+    "limitations": ["Optionale spezifische Einschränkung"]
   }
 ]`;
   },
 
-  toolEvaluation: (userQuery: string, tool: any, rank: number, taskRelevance: number) => {
+  toolEvaluation: (userQuery: string, tool: any, rank: number) => {
     const itemType = tool.type === 'method' ? 'Methode' : 'Tool';
-    
-    return `Erkläre die Anwendung dieser/dieses ${itemType}.
+
+    return `Bewerte diese/diesen ${itemType} ausschließlich bzgl. des PROBLEMS.
 
 PROBLEM: "${userQuery}"
-${itemType.toUpperCase()}: ${tool.name} (${taskRelevance}% Eignung)
+${itemType.toUpperCase()}: ${tool.name}
 TYP: ${tool.type}
 
-Bereits als Rang ${rank} bewertet.
+ANWEISUNGEN:
+- Nur vorhandene Metadaten nutzen (keine Annahmen, keine Websuche).
+- "taskRelevance" als GANZZAHL 0–100 nach einheitlicher Skala vergeben.
+- Realistische Scores i.d.R. 60–80, >85 nur bei nahezu perfektem Fit.
+- Keine Texte außerhalb des JSON.
 
-ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB DER JSON-STRUKTUR:
+${RELEVANCE_RUBRIC}
+${STRICTNESS}
+
+ANTWORT (NUR JSON):
 {
   "detailed_explanation": "Warum und wie einsetzen",
   "implementation_approach": "Konkrete Schritte",
   "pros": ["Vorteil 1", "Vorteil 2"],
   "limitations": ["Einschränkung 1"],
-  "alternatives": "Alternative Ansätze"
+  "alternatives": "Kurz zu sinnvollen Alternativen",
+  "taskRelevance": 0
 }`;
   },
 
   backgroundKnowledgeSelection: (userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]) => {
-    return `Wähle 2-4 relevante Konzepte.
+    return `Wähle 2–4 Konzepte, die das Verständnis/den Einsatz der ausgewählten Tools verbessern.
 
 ${mode === 'workflow' ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
 AUSGEWÄHLTE TOOLS: ${selectedToolNames.join(', ')}
 
-VERFÜGBARE KONZEPTE (${availableConcepts.length} KI-kuratiert):
-${availableConcepts.map((c: any) => 
-  `- ${c.name}: ${c.description}...`
-).join('\n')}
+VERFÜGBARE KONZEPTE (${availableConcepts.length}):
+${availableConcepts.map((c: any) => `- ${c.name}: ${c.description}...`).join('\n')}
 
-ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB DER JSON-STRUKTUR:
+REGELN:
+- Nur Konzepte aus obiger Liste wählen.
+- Relevanz kurz und konkret begründen.
+
+${STRICTNESS}
+
+ANTWORT (NUR JSON):
 [
   {
-    "conceptName": "Name",
-    "relevance": "Warum kritisch für Methodik"
+    "conceptName": "Exakter Name",
+    "relevance": "Warum dieses Konzept hier methodisch wichtig ist"
   }
 ]`;
   },
@@ -209,27 +269,14 @@ ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT OHNE JEGLICHEN TEXT AUSSERHALB DER JSON-S
     tool: any,
     completionContext: string
   ) => {
-    return `Du bist ein DFIR-Experte. Erkläre warum dieses Tool nachträglich zur Vervollständigung hinzugefügt wurde.
-
-KONTEXT DER NACHTRÄGLICHEN ERGÄNZUNG:
-- Ursprüngliche KI-Auswahl war zu spezifisch/eng gefasst
-- Phase "${phase.name}" war unterrepräsentiert in der initialen Auswahl
-- Semantische Suche fand zusätzlich relevante Tools für diese Phase
-- Tool wird nachträglich hinzugefügt um Vollständigkeit zu gewährleisten
+    return `Begründe knapp die Nachergänzung für Phase "${phase.name}".
 
 URSPRÜNGLICHE ANFRAGE: "${originalQuery}"
-PHASE ZU VERVOLLSTÄNDIGEN: ${phase.name} - ${phase.description || ''}
+PHASE: ${phase.name} — ${phase.description || ''}
 HINZUGEFÜGTES TOOL: ${selectedToolName} (${tool.type})
-TOOL-BESCHREIBUNG: ${tool.description}
+KONTEXT: ${completionContext}
 
-BEGRÜNDUNGSKONTEXT: ${completionContext}
-
-Erstelle eine präzise Begründung (max. 40 Wörter), die erklärt:
-1. WARUM dieses Tool nachträglich hinzugefügt wurde
-2. WIE es die ${phase.name}-Phase ergänzt
-3. DASS es die ursprünglich zu spezifische Auswahl erweitert
-
-Antwort: Prägnanter Fließtext, knappe Begründung für Nachergänzung. Vermeide Begriffe wie "Das Tool" und gib keinen einleitenden Text wie "Begründung (40 Wörter):" an.`;
+Antwort: Prägnanter Fließtext, max 40 Wörter, keine Einleitung, keine Liste.`;
   },
 
   generatePhaseCompletionPrompt(
@@ -238,47 +285,48 @@ Antwort: Prägnanter Fließtext, knappe Begründung für Nachergänzung. Vermeid
     candidateTools: any[],
     candidateConcepts: any[]
   ): string {
-    return `Du bist ein DFIR-Experte. Die initiale KI-Auswahl war zu spezifisch - die Phase "${phase.name}" ist unterrepräsentiert.
+    return `Unterrepräsentierte Phase: "${phase.name}". Ergänze 1–2 passende Items aus der semantischen Nachsuche.
 
-KONTEXT: Die Hauptauswahl hat zu wenige Tools für "${phase.name}" identifiziert. Wähle jetzt ergänzende Tools aus semantischer Nachsuche.
+ORIGINALANFRAGE: "${originalQuery}"
+PHASE: ${phase.name} — ${phase.description || ''}
 
-ORIGINAL ANFRAGE: "${originalQuery}"
-UNTERREPRÄSENTIERTE PHASE: ${phase.name} - ${phase.description || ''}
-
-SEMANTISCH GEFUNDENE KANDIDATEN für Nachergänzung:
-
-VERFÜGBARE TOOLS (${candidateTools.length}):
-${candidateTools.map((tool: any) => `
-- ${tool.name} (${tool.type})
-  Beschreibung: ${tool.description}
-  Skill Level: ${tool.skillLevel}
+KANDIDATEN — TOOLS (${candidateTools.length}):
+${candidateTools.map((t: any) => `
+- ${t.name} (${t.type})
+  Beschreibung: ${t.description}
+  Skill Level: ${t.skillLevel}
 `).join('')}
 
 ${candidateConcepts.length > 0 ? `
-VERFÜGBARE KONZEPTE (${candidateConcepts.length}):
-${candidateConcepts.map((concept: any) => `
-- ${concept.name}
-  Beschreibung: ${concept.description}
+KANDIDATEN — KONZEPTE (${candidateConcepts.length}):
+${candidateConcepts.map((c: any) => `
+- ${c.name}
+  Beschreibung: ${c.description}
 `).join('')}
 ` : ''}
 
-AUSWAHLREGELN FÜR NACHERGÄNZUNG:
-1. Wähle 1-2 BESTE Methoden/Tools die die ${phase.name}-Phase optimal ergänzen
-2. Methoden/Tools müssen für die ursprüngliche Anfrage relevant sein
-3. Ergänzen, nicht ersetzen - erweitere die zu spezifische Erstauswahl
+REGELN:
+- Wähle 1–2 Tools/Methoden, die ${phase.name} sinnvoll ergänzen (keine Ersetzung).
+- Nur aus obigen Kandidaten wählen; exakte Namen verwenden.
+- Kurze Begründung, warum diese Ergänzung nötig ist.
 
-ANTWORT AUSSCHLIESSLICH IM JSON-FORMAT:
+Skalenhinweis (einheitlich):
+${RELEVANCE_RUBRIC}
+
+${STRICTNESS}
+
+ANTWORT (NUR JSON):
 {
   "selectedTools": ["ToolName1", "ToolName2"],
   "selectedConcepts": ["ConceptName1"],
-  "completionReasoning": "Kurze Erklärung warum diese Nachergänzung für ${phase.name} notwendig war"
+  "completionReasoning": "Kurze Erklärung zur Ergänzung der ${phase.name}-Phase"
 }`;
   },
 
   finalRecommendations: (isWorkflow: boolean, userQuery: string, selectedToolNames: string[]) => {
-    const focus = isWorkflow ? 
-      'Workflow-Schritte, Best Practices, Objektivität' :
-      'Methodische Überlegungen, Validierung, Qualitätssicherung';
+    const focus = isWorkflow
+      ? 'Knappe Workflow-Schritte & Best Practices; neutral formulieren'
+      : 'Methodische Überlegungen, Validierung, Qualitätssicherung';
 
     return `Erstelle ${isWorkflow ? 'Workflow-Empfehlung' : 'methodische Überlegungen'}.
 
@@ -286,33 +334,31 @@ ${isWorkflow ? 'SZENARIO' : 'PROBLEM'}: "${userQuery}"
 AUSGEWÄHLT: ${selectedToolNames.join(', ')}${selectedToolNames.length > 5 ? '...' : ''}
 
 Fokus: ${focus}
-
-Antwort: Fließtext ohne Listen, max ${isWorkflow ? '100' : '80'} Wörter.`;
+Antwort: Fließtext, max ${isWorkflow ? '100' : '80'} Wörter. Keine Liste.`;
   }
 } as const;
 
-export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, selectionMethod: string, maxSelectedItems: number): string;
+export function getPrompt(key: 'enhancementQuestions', input: string): string;
+export function getPrompt(key: 'toolSelection', mode: string, userQuery: string, maxSelectedItems: number): string;
 export function getPrompt(key: 'toolSelectionWithData', basePrompt: string, toolsToSend: any[], conceptsToSend: any[]): string;
 export function getPrompt(key: 'scenarioAnalysis', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'investigationApproach', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'criticalConsiderations', isWorkflow: boolean, userQuery: string): string;
 export function getPrompt(key: 'phaseToolSelection', userQuery: string, phase: any, phaseTools: any[]): string;
-export function getPrompt(key: 'toolEvaluation', userQuery: string, tool: any, rank: number, taskRelevance: number): string;
+export function getPrompt(key: 'toolEvaluation', userQuery: string, tool: any, rank: number): string;
 export function getPrompt(key: 'backgroundKnowledgeSelection', userQuery: string, mode: string, selectedToolNames: string[], availableConcepts: any[]): string;
 export function getPrompt(key: 'phaseCompletionReasoning', originalQuery: string, phase: any, selectedToolName: string, tool: any, completionContext: string): string;
 export function getPrompt(key: 'finalRecommendations', isWorkflow: boolean, userQuery: string, selectedToolNames: string[]): string;
 export function getPrompt(key: 'generatePhaseCompletionPrompt', originalQuery: string, phase: any, candidateTools: any[], candidateConcepts: any[]): string;
+
 export function getPrompt(promptKey: keyof typeof AI_PROMPTS, ...args: any[]): string {
   try {
-    const promptFunction = AI_PROMPTS[promptKey];
-    if (typeof promptFunction === 'function') {
-      return (promptFunction as (...args: any[]) => string)(...args);
-    } else {
-      console.error(`[PROMPTS] Invalid prompt key: ${promptKey}`);
-      return 'Error: Invalid prompt configuration';
-    }
-  } catch (error) {
-    console.error(`[PROMPTS] Error generating prompt ${promptKey}:`, error);
+    const f = AI_PROMPTS[promptKey];
+    if (typeof f === 'function') return (f as (...a: any[]) => string)(...args);
+    console.error(`[PROMPTS] Invalid prompt key: ${promptKey}`);
+    return 'Error: Invalid prompt configuration';
+  } catch (err) {
+    console.error(`[PROMPTS] Error generating prompt ${promptKey}:`, err);
     return 'Error: Failed to generate prompt';
   }
-}
+}

src/content/config.ts

@@ -16,7 +16,7 @@ const knowledgebaseCollection = defineCollection({
     tags: z.array(z.string()).default([]),
     
     published: z.boolean().default(true),
-    gated_content: z.boolean().default(false), // NEW: Gated content flag
+    gated_content: z.boolean().default(false),
     
   })
 });

src/data/tools.yaml

@@ -57,6 +57,44 @@ tools:
     accessType: download
     license: Apache-2.0
     knowledgebase: false
+  - name: Thorium
+    icon: ⚛️
+    type: software
+    description: >-
+      CISAs portable Hybrid-Analyse-Tool für die schnelle Untersuchung von Windows-
+      Systemen auf bösartige Aktivitäten. Scannt mit kuratierten YARA- und
+      Sigma-Regeln Arbeitsspeicher, Prozesse, Dateisystem, Netzwerkverbindungen und
+      Systemprotokolle. Ideal für schnelle Triage im Incident Response, sowohl live als auch
+      auf gemounteten Images. Die Ausgabe erfolgt in strukturierten JSON-Reports.
+    domains:
+      - incident-response
+      - malware-analysis
+    phases:
+      - examination
+      - analysis
+    platforms:
+      - Linux
+    related_software:
+      - Loki
+      - YARA
+      - Velociraptor
+    skillLevel: intermediate
+    accessType: download
+    url: https://github.com/cisagov/thorium
+    license: MIT
+    knowledgebase: false
+    tags:
+    - cli
+      - triage
+      - fast-scan
+      - ioc-matching
+      - yara-scan
+      - sigma-rules
+      - memory-analysis
+      - process-analysis
+      - filesystem-scanning
+      - log-analysis
+      - portable
   - name: Volatility 3
     type: software
     description: >-

src/pages/about.astro

@@ -184,7 +184,7 @@ import BaseLayout from '../layouts/BaseLayout.astro';
 
       <div style="display: grid; gap: 1.25rem;">
         <div style="background-color: var(--color-bg-secondary); padding: 1.25rem; border-radius: 0.5rem;">
-          <h4 style="margin: 0 0 0.5rem 0; color: var(--color-accent);">🔍 Vorschläge</h4>
+          <h4 style="margin: 0 0 0.5rem 0; color: var(--color-accent);">📝 Vorschläge</h4>
           <p style="margin: 0;">
             Du hast eine Idee, wie wir den Hub erweitern können? Reiche deinen Vorschlag unkompliziert
             über unsere <a href="/contribute#vorschlaege">/contribute</a>-Seite ein.
@@ -210,15 +210,54 @@ import BaseLayout from '../layouts/BaseLayout.astro';
             <svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor">
               <path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z" />
             </svg>
-            Git‑Repository besuchen
+            Git-Repository besuchen
           </a>
-        </div>        
+        </div>
+        
+        <!-- Lightning Support Section with simple-boost integration -->
         <div style="background-color: var(--color-bg-secondary); padding: 1.25rem; border-radius: 0.5rem;">
-          <h4 style="margin: 0 0 0.5rem 0; color: var(--color-accent);">⚡ Unterstützung</h4>
-          <p style="margin: 0;">
-            Kleine Spenden zur Infrastruktur-Finanzierung nehme ich auch gerne an, wenn es sein muss. 
-            Fragt einfach nach der Lightning-Adresse oder BTC-Adresse!
+          <h4 style="margin: 0 0 0.75rem 0; color: var(--color-accent); display: flex; align-items: center; gap: 0.5rem;">
+            <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
+              <polygon points="13,2 3,14 12,14 11,22 21,10 12,10 13,2"/>
+            </svg>
+            ⚡ Unterstützung
+          </h4>
+          <p style="margin: 0 0 1rem 0; font-size: 0.875rem; line-height: 1.5;">
+            Kleine Spenden zur Server-Finanzierung sind willkommen.
           </p>
+          
+          <div style="margin-bottom: 1rem;">
+            <!-- Simple EUR Payment -->
+            <div style="display: flex; gap: 0.75rem; align-items: center; justify-content: center; max-width: 300px; margin: 0 auto;">
+              <input 
+                type="number" 
+                id="eur-amount" 
+                min="0.01" 
+                step="0.01" 
+                placeholder="0,50" 
+                value="0.5"
+                style="width: 80px; padding: 0.5rem; border: 1px solid var(--color-border); border-radius: 0.375rem; font-size: 0.875rem; text-align: center;">
+              <span style="font-size: 0.875rem; color: var(--color-text-secondary);">€</span>
+              <simple-boost 
+                id="eur-boost"
+                class="bootstrap" 
+                nwc="nostr+walletconnect://4fe05896e1faf09d1902ea24ef589f65a9606d1710420a9574ce331e3c7f486b?relay=wss://nostr.mikoshi.de&secret=bdfc861fe71e8d9e375b7a2484052e92def7caf4b317d8f6537b784d3cd6eb3b"
+                amount="0.5" 
+                currency="eur" 
+                memo="ForensicPathways Unterstützung - Vielen Dank!"
+                style="background-color: var(--color-accent); color: white; border: none; border-radius: 0.375rem; padding: 0.5rem 1rem; font-size: 0.875rem; cursor: pointer;">
+                ⚡ Senden
+              </simple-boost>
+            </div>
+          </div>
+          
+          <div style="margin-top: 1rem; padding: 0.75rem; background-color: var(--color-bg); border-radius: 0.375rem; border-left: 3px solid var(--color-accent);">
+            <p style="margin: 0; font-size: 0.75rem; color: var(--color-text-secondary); line-height: 1.4; text-align: center;">
+              <strong>⚡ Lightning-Unterstützung:</strong> Betrag eingeben und senden. 
+              Benötigt eine Lightning-Wallet wie <a href="https://getalby.com" target="_blank" rel="noopener" style="color: var(--color-accent);">Alby</a> oder 
+              <a href="https://phoenix.acinq.co" target="_blank" rel="noopener" style="color: var(--color-accent);">Phoenix</a>.
+            </p>
+          </div>
         </div>
       </div>
     </div>
@@ -231,4 +270,70 @@ import BaseLayout from '../layouts/BaseLayout.astro';
       </p>
     </div>
   </section>
-</BaseLayout>
+</BaseLayout>
+
+<script>
+  // TODO: cleanup
+  import('simple-boost').then(() => {
+    console.log('Simple-boost loaded successfully from local dependencies');
+    
+    setupDynamicAmounts();
+  }).catch(error => {
+    console.error('Failed to load simple-boost:', error);
+    const script = document.createElement('script');
+    script.type = 'module';
+    script.src = '/node_modules/simple-boost/dist/simple-boost.js';
+    script.onload = () => {
+      console.log('Simple-boost fallback loaded');
+      setupDynamicAmounts();
+    };
+    script.onerror = () => console.error('Simple-boost fallback failed');
+    document.head.appendChild(script);
+  });
+
+  function setupDynamicAmounts() {
+    const eurBoost = document.getElementById('eur-boost');
+    const eurInput = document.getElementById('eur-amount') as HTMLInputElement;
+    
+    if (eurBoost && eurInput) {
+      eurBoost.addEventListener('click', (e) => {
+        const amount = parseFloat(eurInput.value) || 0.5;
+        eurBoost.setAttribute('amount', amount.toString());
+        console.log('EUR amount set to:', amount);
+      });
+      
+      eurInput.addEventListener('input', () => {
+        const amount = parseFloat(eurInput.value) || 0.5;
+        eurBoost.setAttribute('amount', amount.toString());
+      });
+    }
+  }
+</script>
+
+<style>
+  simple-boost {
+    --simple-boost-primary: var(--color-warning);
+    --simple-boost-primary-hover: var(--color-accent);
+    --simple-boost-text: white;
+    transition: all 0.2s ease;
+  }
+  
+  simple-boost:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 4px 8px rgba(0,0,0,0.15) !important;
+  }
+  
+  simple-boost .simple-boost-button {
+    display: flex;
+    align-items: center;
+    gap: 0.5rem;
+    font-family: inherit;
+    font-size: 0.875rem;
+  }
+  
+  /* Loading state styling */
+  simple-boost[loading] {
+    opacity: 0.7;
+    cursor: not-allowed;
+  }
+</style>

src/pages/api/ai/embeddings-status.ts

@@ -1,16 +1,18 @@
 // src/pages/api/ai/embeddings-status.ts
 import type { APIRoute } from 'astro';
+import { embeddingsService } from '../../../utils/embeddings.js';
 
 export const prerender = false;
 
 export const GET: APIRoute = async () => {
   try {
-    const { embeddingsService } = await import('../../../utils/embeddings.js');
     await embeddingsService.waitForInitialization();
     
     const stats = embeddingsService.getStats();
-    const status = stats.enabled && stats.initialized ? 'ready' : 
-                  stats.enabled && !stats.initialized ? 'initializing' : 'disabled';
+    const status = stats.initialized ? 'ready' : 
+                  !stats.initialized ? 'initializing' : 'disabled';
+    
+    console.log(`[EMBEDDINGS-STATUS-API] Service status: ${status}, stats:`, stats);
     
     return new Response(JSON.stringify({
       success: true,
@@ -23,6 +25,8 @@ export const GET: APIRoute = async () => {
     });
     
   } catch (error) {
+    console.error('[EMBEDDINGS-STATUS-API] Error checking embeddings status:', error);
+    
     return new Response(JSON.stringify({
       success: false,
       embeddings: { enabled: false, initialized: false, count: 0 },

src/pages/api/ai/enhance-input.ts

@@ -1,27 +1,57 @@
-// src/pages/api/ai/enhance-input.ts - Enhanced AI service compatibility
+// src/pages/api/ai/enhance-input.ts
 import type { APIRoute } from 'astro';
 import { withAPIAuth } from '../../../utils/auth.js';
 import { apiError, apiServerError, createAuthErrorResponse } from '../../../utils/api.js';
 import { enqueueApiCall } from '../../../utils/rateLimitedQueue.js';
+import { aiService } from '../../../utils/aiService.js';
+import { JSONParser } from '../../../utils/jsonUtils.js';
+import { getPrompt } from '../../../config/prompts.js';
 
 export const prerender = false;
 
-function getEnv(key: string): string {
-  const value = process.env[key];
-  if (!value) {
-    throw new Error(`Missing environment variable: ${key}`);
-  }
-  return value;
-}
+const RATE_LIMIT_WINDOW_MS =
+  Number.isFinite(parseInt(process.env.RATE_LIMIT_WINDOW_MS ?? '', 10))
+    ? parseInt(process.env.RATE_LIMIT_WINDOW_MS!, 10)
+    : 60_000; 
 
-const AI_ENDPOINT = getEnv('AI_ANALYZER_ENDPOINT');
-const AI_ANALYZER_API_KEY = getEnv('AI_ANALYZER_API_KEY');
-const AI_ANALYZER_MODEL = getEnv('AI_ANALYZER_MODEL');
+const RATE_LIMIT_MAX =
+  Number.isFinite(parseInt(process.env.AI_RATE_LIMIT_MAX_REQUESTS ?? '', 10))
+    ? parseInt(process.env.AI_RATE_LIMIT_MAX_REQUESTS!, 10)
+    : 5;
+
+const INPUT_MIN_CHARS = 40;
+const INPUT_MAX_CHARS = 1000;
+const Q_MIN_LEN = 15;
+const Q_MAX_LEN = 160;
+const Q_MAX_COUNT = 3;
+const AI_TEMPERATURE = 0.3;
+const CLEANER_TEMPERATURE = 0.0;
 
 const rateLimitStore = new Map<string, { count: number; resetTime: number }>();
-const RATE_LIMIT_WINDOW = 60 * 1000;
-const RATE_LIMIT_MAX = 5;
 
+function checkRateLimit(userId: string): boolean {
+  const now = Date.now();
+  const entry = rateLimitStore.get(userId);
+  if (!entry || now > entry.resetTime) {
+    rateLimitStore.set(userId, { count: 1, resetTime: now + RATE_LIMIT_WINDOW_MS });
+    return true;
+  }
+  if (entry.count >= RATE_LIMIT_MAX) return false;
+  entry.count++;
+  return true;
+}
+
+function cleanupExpiredRateLimits(): void {
+  const now = Date.now();
+  for (const [userId, entry] of rateLimitStore.entries()) {
+    if (now > entry.resetTime) rateLimitStore.delete(userId);
+  }
+}
+setInterval(cleanupExpiredRateLimits, 5 * 60 * 1000);
+
+/**
+ * Helpers
+ */
 function sanitizeInput(input: string): string {
   return input
     .replace(/```[\s\S]*?```/g, '[CODE_BLOCK_REMOVED]')
@@ -29,112 +59,24 @@ function sanitizeInput(input: string): string {
     .replace(/\b(system|assistant|user)\s*[:]/gi, '[ROLE_REMOVED]')
     .replace(/\b(ignore|forget|disregard)\s+(previous|all|your)\s+(instructions?|context|rules?)/gi, '[INSTRUCTION_REMOVED]')
     .trim()
-    .slice(0, 1000); 
+    .slice(0, INPUT_MAX_CHARS);
 }
 
-function checkRateLimit(userId: string): boolean {
-  const now = Date.now();
-  const userLimit = rateLimitStore.get(userId);
-  
-  if (!userLimit || now > userLimit.resetTime) {
-    rateLimitStore.set(userId, { count: 1, resetTime: now + RATE_LIMIT_WINDOW });
-    return true;
-  }
-  
-  if (userLimit.count >= RATE_LIMIT_MAX) {
-    return false;
-  }
-  
-  userLimit.count++;
-  return true;
-}
-
-function cleanupExpiredRateLimits() {
-  const now = Date.now();
-  for (const [userId, limit] of rateLimitStore.entries()) {
-    if (now > limit.resetTime) {
-      rateLimitStore.delete(userId);
-    }
-  }
-}
-
-setInterval(cleanupExpiredRateLimits, 5 * 60 * 1000);
-
-function createEnhancementPrompt(input: string): string {
-  return `Sie sind ein DFIR-Experte mit Spezialisierung auf forensische Methodik. Ein Nutzer beschreibt ein forensisches Szenario oder Problem. Analysieren Sie die Eingabe auf Vollständigkeit für eine wissenschaftlich fundierte forensische Untersuchung.
-
-ANALYSIEREN SIE DIESE FORENSISCHEN KATEGORIEN:
-1. **Incident Context**: Was ist passiert? Welche Angriffsvektoren oder technischen Probleme liegen vor?
-2. **Affected Systems**: Welche spezifischen Technologien/Plattformen sind betroffen? (Windows/Linux/ICS/SCADA/Mobile/Cloud/Network Infrastructure)
-3. **Available Evidence**: Welche forensischen Datenquellen stehen zur Verfügung? (RAM-Dumps, Disk-Images, Log-Files, Network-Captures, Registry-Hives)
-4. **Investigation Objectives**: Was soll erreicht werden? (IOC-Extraktion, Timeline-Rekonstruktion, Attribution, Impact-Assessment)
-5. **Timeline Constraints**: Wie zeitkritisch ist die Untersuchung?
-6. **Legal & Compliance**: Rechtliche Anforderungen, Chain of Custody, Compliance-Rahmen (DSGVO, sector-specific regulations)
-7. **Technical Constraints**: Verfügbare Ressourcen, Skills, Infrastrukturbeschränkungen
-
-WENN die Beschreibung alle kritischen forensischen Aspekte abdeckt: Geben Sie eine leere Liste [] zurück.
-
-WENN wichtige forensische Details fehlen: Formulieren Sie 2-3 präzise Fragen, die die kritischsten Lücken für eine wissenschaftlich fundierte forensische Analyse schließen.
-
-QUALITÄTSKRITERIEN FÜR FRAGEN:
-- Forensisch spezifisch, nicht allgemein (NICHT: "Mehr Details?")
-- Methodisch relevant (NICHT: "Wann passierte das?")
-- Priorisiert nach Auswirkung auf die forensische Untersuchungsqualität
-- Die Frage soll maximal 20 Wörter umfassen
-
-ANTWORTFORMAT (NUR JSON, KEIN ZUSÄTZLICHER TEXT):
-[
-  "spezifische Frage 1?",
-  "spezifische Frage 2?",
-  "spezifische Frage 3?"
-]
-
-NUTZER-EINGABE:
-${input}
-  `.trim();
-}
-
-async function callAIService(prompt: string): Promise<Response> {
-  const endpoint = AI_ENDPOINT;
-  const apiKey = AI_ANALYZER_API_KEY;
-  const model = AI_ANALYZER_MODEL;
-  
-  let headers: Record<string, string> = {
-    'Content-Type': 'application/json'
-  };
-  
-  if (apiKey) {
-    headers['Authorization'] = `Bearer ${apiKey}`;
-    console.log('[ENHANCE API] Using API key authentication');
-  } else {
-    console.log('[ENHANCE API] No API key - making request without authentication');
-  }
-  
-  const requestBody = {
-    model,
-    messages: [{ role: 'user', content: prompt }],
-    max_tokens: 300,
-    temperature: 0.7,
-    top_p: 0.9,
-    frequency_penalty: 0.2,
-    presence_penalty: 0.1
-  };
-  
-  return fetch(`${endpoint}/v1/chat/completions`, {
-    method: 'POST',
-    headers,
-    body: JSON.stringify(requestBody)
-  });
+function stripJsonFences(s: string): string {
+  return s.replace(/^```json\s*/i, '')
+          .replace(/^```\s*/i, '')
+          .replace(/\s*```\s*$/, '')
+          .trim();
 }
 
+/**
+ * Handler
+ */
 export const POST: APIRoute = async ({ request }) => {
   try {
-    const authResult = await withAPIAuth(request, 'ai');
-    if (!authResult.authenticated) {
-      return createAuthErrorResponse();
-    }
-    
-    const userId = authResult.userId;
+    const auth = await withAPIAuth(request, 'ai');
+    if (!auth.authenticated) return createAuthErrorResponse();
+    const userId = auth.userId;
 
     if (!checkRateLimit(userId)) {
       return apiError.rateLimit('Enhancement rate limit exceeded');
@@ -143,79 +85,53 @@ export const POST: APIRoute = async ({ request }) => {
     const body = await request.json();
     const { input } = body;
 
-    if (!input || typeof input !== 'string' || input.length < 40) {
-      return apiError.badRequest('Input too short for enhancement (minimum 40 characters)');
+    if (!input || typeof input !== 'string' || input.length < INPUT_MIN_CHARS) {
+      return apiError.badRequest(`Input too short for enhancement (minimum ${INPUT_MIN_CHARS} characters)`);
     }
 
     const sanitizedInput = sanitizeInput(input);
-    if (sanitizedInput.length < 40) {
+    if (sanitizedInput.length < INPUT_MIN_CHARS) {
       return apiError.badRequest('Input too short after sanitization');
     }
 
-    const systemPrompt = createEnhancementPrompt(sanitizedInput);
-    const taskId = `enhance_${userId}_${Date.now()}_${Math.random().toString(36).substr(2, 4)}`;
-    
-    const aiResponse = await enqueueApiCall(() => callAIService(systemPrompt), taskId);
+    const taskId = `enhance_${userId}_${Date.now()}_${Math.random().toString(36).slice(2, 6)}`;
+    const questionsPrompt = getPrompt('enhancementQuestions', sanitizedInput);
 
-    if (!aiResponse.ok) {
-      const errorText = await aiResponse.text();
-      console.error('[ENHANCE API] AI enhancement error:', errorText, 'Status:', aiResponse.status);
-      return apiServerError.unavailable('Enhancement service unavailable');
-    }
+    console.log(`[ENHANCE-API] Processing enhancement request for user: ${userId}`);
 
-    const aiData = await aiResponse.json();
-    const aiContent = aiData.choices?.[0]?.message?.content;
+    const aiResponse = await enqueueApiCall(
+      () => aiService.callAI(questionsPrompt, { temperature: AI_TEMPERATURE }),
+      taskId
+    );
 
-    if (!aiContent) {
+    if (!aiResponse?.content) {
       return apiServerError.unavailable('No enhancement response');
     }
 
-    let questions;
-    try {
-      const cleanedContent = aiContent
-        .replace(/^```json\s*/i, '')
-        .replace(/\s*```\s*$/, '')
-        .trim();
-      questions = JSON.parse(cleanedContent);
-      
-      if (!Array.isArray(questions)) {
-        throw new Error('Response is not an array');
-      }
-      
-      questions = questions
-        .filter(q => typeof q === 'string' && q.length > 20 && q.length < 200)
-        .filter(q => q.includes('?')) 
-        .filter(q => {
-          const forensicsTerms = ['forensisch', 'log', 'dump', 'image', 'artefakt', 'evidence', 'incident', 'system', 'netzwerk', 'zeitraum', 'verfügbar'];
-          const lowerQ = q.toLowerCase();
-          return forensicsTerms.some(term => lowerQ.includes(term));
-        })
-        .map(q => q.trim())
-        .slice(0, 3); 
-        
-      if (questions.length === 0) {
-        questions = [];
-      }
+    let parsed: unknown = JSONParser.safeParseJSON(stripJsonFences(aiResponse.content), null);
 
-    } catch (error) {
-      console.error('Failed to parse enhancement response:', aiContent);
-      questions = [];
-    }
+    let questions: string[] = Array.isArray(parsed) ? parsed : [];
+    questions = questions
+      .filter(q => typeof q === 'string')
+      .map(q => q.trim())
+      .filter(q => q.endsWith('?'))
+      .filter(q => q.length >= Q_MIN_LEN && q.length <= Q_MAX_LEN)
+      .slice(0, Q_MAX_COUNT);
 
-    console.log(`[ENHANCE API] User: ${userId}, Forensics Questions: ${questions.length}, Input length: ${sanitizedInput.length}`);
+    console.log(`[ENHANCE-API] User: ${userId}, Questions generated: ${questions.length}, Input length: ${sanitizedInput.length}`);
 
     return new Response(JSON.stringify({
       success: true,
       questions,
       taskId,
-      inputComplete: questions.length === 0 
+      inputComplete: questions.length === 0
     }), {
       status: 200,
       headers: { 'Content-Type': 'application/json' }
     });
 
-  } catch (error) {
-    console.error('Enhancement error:', error);
+  } catch (err) {
+    console.error('[ENHANCE-API] Enhancement error:', err);
     return apiServerError.internal('Enhancement processing failed');
   }
-};
+};

src/pages/api/ai/query.ts

@@ -20,15 +20,14 @@ const MAIN_RATE_LIMIT_MAX = parseInt(process.env.AI_RATE_LIMIT_MAX_REQUESTS || '
 const MICRO_TASK_TOTAL_LIMIT = parseInt(process.env.AI_MICRO_TASK_TOTAL_LIMIT || '50', 10); 
 
 function sanitizeInput(input: string): string {
-  let sanitized = input
+  return input
     .replace(/```[\s\S]*?```/g, '[CODE_BLOCK_REMOVED]')
     .replace(/\<\/?[^>]+(>|$)/g, '')
     .replace(/\b(system|assistant|user)\s*[:]/gi, '[ROLE_REMOVED]')
     .replace(/\b(ignore|forget|disregard)\s+(previous|all|your)\s+(instructions?|context|rules?)/gi, '[INSTRUCTION_REMOVED]')
-    .trim();
-  
-  sanitized = sanitized.slice(0, 2000).replace(/\s+/g, ' ');
-  return sanitized;
+    .trim()
+    .slice(0, 2000)
+    .replace(/\s+/g, ' ');
 }
 
 function checkRateLimit(userId: string): { allowed: boolean; reason?: string; microTasksRemaining?: number } {
@@ -77,7 +76,7 @@ function incrementMicroTaskCount(userId: string, aiCallsMade: number): void {
   }
 }
 
-function cleanupExpiredRateLimits() {
+function cleanupExpiredRateLimits(): void {
   const now = Date.now();
   const maxStoreSize = 1000; 
   
@@ -117,51 +116,52 @@ export const POST: APIRoute = async ({ request }) => {
     const body = await request.json();
     const { query, mode = 'workflow', taskId: clientTaskId } = body;
 
-    console.log(`[MICRO-TASK API] Received request - TaskId: ${clientTaskId}, Mode: ${mode}, Query length: ${query?.length || 0}`);
-    console.log(`[MICRO-TASK API] Micro-task calls remaining: ${rateLimitResult.microTasksRemaining}`);
+    console.log(`[AI-API] Received request - TaskId: ${clientTaskId}, Mode: ${mode}, Query length: ${query?.length || 0}`);
+    console.log(`[AI-API] Micro-task calls remaining: ${rateLimitResult.microTasksRemaining}`);
 
     if (!query || typeof query !== 'string') {
-      console.log(`[MICRO-TASK API] Invalid query for task ${clientTaskId}`);
+      console.log(`[AI-API] Invalid query for task ${clientTaskId}`);
       return apiError.badRequest('Query required');
     }
 
     if (!['workflow', 'tool'].includes(mode)) {
-      console.log(`[MICRO-TASK API] Invalid mode for task ${clientTaskId}: ${mode}`);
+      console.log(`[AI-API] Invalid mode for task ${clientTaskId}: ${mode}`);
       return apiError.badRequest('Invalid mode. Must be "workflow" or "tool"');
     }
 
     const sanitizedQuery = sanitizeInput(query);
     if (sanitizedQuery.includes('[FILTERED]')) {
-      console.log(`[MICRO-TASK API] Filtered input detected for task ${clientTaskId}`);
+      console.log(`[AI-API] Filtered input detected for task ${clientTaskId}`);
       return apiError.badRequest('Invalid input detected');
     }
 
     const taskId = clientTaskId || `ai_${userId}_${Date.now()}_${Math.random().toString(36).substr(2, 6)}`;
     
-    console.log(`[MICRO-TASK API] About to enqueue micro-task pipeline ${taskId}`);
+    console.log(`[AI-API] Enqueueing pipeline task ${taskId}`);
     
     const result = await enqueueApiCall(() => 
       aiPipeline.processQuery(sanitizedQuery, mode)
     , taskId);
 
     if (!result || !result.recommendation) {
-      return apiServerError.unavailable('No response from micro-task AI pipeline');
+      return apiServerError.unavailable('No response from AI pipeline');
     }
 
     const stats = result.processingStats;
     const estimatedAICallsMade = stats.microTasksCompleted + stats.microTasksFailed;
     incrementMicroTaskCount(userId, estimatedAICallsMade);
 
-    console.log(`[MICRO-TASK API] Pipeline completed for ${taskId}:`);
-    console.log(`  - Mode: ${mode}`);
-    console.log(`  - User: ${userId}`);
-    console.log(`  - Query length: ${sanitizedQuery.length}`);
-    console.log(`  - Processing time: ${stats.processingTimeMs}ms`);
-    console.log(`  - Micro-tasks completed: ${stats.microTasksCompleted}`);
-    console.log(`  - Micro-tasks failed: ${stats.microTasksFailed}`);
-    console.log(`  - Estimated AI calls: ${estimatedAICallsMade}`);
-    console.log(`  - Embeddings used: ${stats.embeddingsUsed}`);
-    console.log(`  - Final items: ${stats.finalSelectedItems}`);
+    console.log(`[AI-API] Pipeline completed for ${taskId}:`, {
+      mode,
+      user: userId,
+      queryLength: sanitizedQuery.length,
+      processingTime: stats.processingTimeMs,
+      microTasksCompleted: stats.microTasksCompleted,
+      microTasksFailed: stats.microTasksFailed,
+      estimatedAICalls: estimatedAICallsMade,
+      embeddingsUsed: stats.embeddingsUsed,
+      finalItems: stats.finalSelectedItems
+    });
 
     const currentLimit = rateLimitStore.get(userId);
     const remainingMicroTasks = currentLimit ? 
@@ -175,7 +175,7 @@ export const POST: APIRoute = async ({ request }) => {
       query: sanitizedQuery,
       processingStats: {
         ...result.processingStats,
-        pipelineType: 'micro-task',
+        pipelineType: 'refactored',
         microTasksSuccessRate: stats.microTasksCompleted / (stats.microTasksCompleted + stats.microTasksFailed),
         averageTaskTime: stats.processingTimeMs / (stats.microTasksCompleted + stats.microTasksFailed),
         estimatedAICallsMade
@@ -191,18 +191,16 @@ export const POST: APIRoute = async ({ request }) => {
     });
 
   } catch (error) {
-    console.error('[MICRO-TASK API] Pipeline error:', error);
+    console.error('[AI-API] Pipeline error:', error);
     
     if (error.message.includes('embeddings')) {
-      return apiServerError.unavailable('Embeddings service error - using AI fallback');
-    } else if (error.message.includes('micro-task')) {
-      return apiServerError.unavailable('Micro-task pipeline error - some analysis steps failed');
-    } else if (error.message.includes('selector')) {
-      return apiServerError.unavailable('AI selector service error');
+      return apiServerError.unavailable('Embeddings service error');
+    } else if (error.message.includes('AI')) {
+      return apiServerError.unavailable('AI service error');
     } else if (error.message.includes('rate limit')) {
-      return apiError.rateLimit('AI service rate limits exceeded during micro-task processing');
+      return apiError.rateLimit('AI service rate limits exceeded');
     } else {
-      return apiServerError.internal('Micro-task AI pipeline error');
+      return apiServerError.internal('AI pipeline error');
     }
   }
 };

src/pages/api/search/semantic.ts

@@ -37,13 +37,6 @@ export const POST: APIRoute = async ({ request }) => {
 
     const { embeddingsService } = await import('../../../utils/embeddings.js');
 
-    if (!embeddingsService.isEnabled()) {
-      return new Response(
-        JSON.stringify({ success: false, error: 'Semantic search not available' }),
-        { status: 400, headers: { 'Content-Type': 'application/json' } }
-      );
-    }
-
     await embeddingsService.waitForInitialization();
 
     const similarItems = await embeddingsService.findSimilar(

src/pages/contribute/index.astro

@@ -1,5 +1,5 @@
 ---
-// src/pages/contribute/index.astro - Consolidated Auth
+// src/pages/contribute/index.astro
 import BaseLayout from '../../layouts/BaseLayout.astro';
 import { withAuth } from '../../utils/auth.js'; 
 

src/pages/index.astro

@@ -510,9 +510,7 @@ if (aiAuthRequired) {
       }, 500);
     };
 
-    function handleSharedURL() {
-      console.log('[SHARE] Handling shared URL:', window.location.search);
-      
+    function handleSharedURL() {     
       const urlParams = new URLSearchParams(window.location.search);
       const toolParam = urlParams.get('tool');
       const viewParam = urlParams.get('view');

src/styles/global.css

@@ -675,6 +675,7 @@ input[type="checkbox"] {
   border-radius: 0.25rem;
   font-size: 0.75rem;
   margin: 0.125rem;
+  max-height: 1.5rem;
 }
 
 /* ===================================================================
@@ -1806,11 +1807,44 @@ input[type="checkbox"] {
 .ai-textarea-section {
   flex: 1;
   min-width: 0;
+  display: flex;
+  flex-direction: column;
+}
+
+.ai-textarea-section textarea {
+  width: 100%;
+  height: 180px;
+  min-height: 180px;
+  max-height: 300px;
+  resize: vertical;
+  font-size: 0.9375rem;
+  line-height: 1.5;
+  padding: 0.75rem;
+  border: 1px solid var(--color-border);
+  border-radius: 0.375rem;
+  background-color: var(--color-bg);
+  color: var(--color-text);
+  transition: var(--transition-fast);
+  flex: 1;
+}
+
+.confidence-tooltip {
+  background: var(--color-bg) !important;
+  border: 2px solid var(--color-border) !important;
+  box-shadow: 0 8px 25px rgba(0, 0, 0, 0.15) !important;
+  z-index: 2000 !important;
+}
+
+.ai-textarea-section textarea:focus {
+  outline: none;
+  border-color: var(--color-primary);
+  box-shadow: 0 0 0 3px rgb(37 99 235 / 10%);
 }
 
 .ai-suggestions-section {
   flex: 0 0 320px;
-  min-height: 120px;
+  min-height: 180px;
+  height: auto;
 }
 
 .ai-input-container textarea {
@@ -2186,12 +2220,20 @@ input[type="checkbox"] {
   border-radius: 1rem;
   font-weight: 500;
   text-transform: uppercase;
+  position: relative;
+  z-index: 1;
 }
 
 .tool-rec-priority.high { background-color: var(--color-error); color: white; }
 .tool-rec-priority.medium { background-color: var(--color-warning); color: white; }
 .tool-rec-priority.low { background-color: var(--color-accent); color: white; }
 
+[data-theme="dark"] .confidence-tooltip {
+  background: var(--color-bg-secondary) !important;
+  border-color: var(--color-border) !important;
+  box-shadow: 0 8px 25px rgba(0, 0, 0, 0.4) !important;
+}
+
 .tool-rec-justification {
   font-size: 0.875rem;
   line-height: 1.5;
@@ -2610,7 +2652,8 @@ footer {
    ================================================================= */
 
 .smart-prompting-container {
-  height: 100%;
+  height: auto;
+  min-height: 180px;
   animation: smartPromptSlideIn 0.4s cubic-bezier(0.4, 0, 0.2, 1);
 }
 
@@ -2619,8 +2662,10 @@ footer {
   border: 1px solid var(--color-border);
   border-radius: 0.5rem;
   padding: 1rem;
-  height: 100%;
-  min-height: 120px;
+  height: auto;
+  min-height: 180px;
+  max-height: 400px;
+  overflow-y: auto;
   display: flex;
   flex-direction: column;
   opacity: 0.85;
@@ -2660,8 +2705,8 @@ footer {
 
 /* Smart Prompting Hint */
 .smart-prompting-hint {
-  height: 100%;
-  min-height: 120px;
+  height: 180px;
+  min-height: 180px;
   display: flex;
   align-items: center;
   animation: hintFadeIn 0.3s ease-in-out;
@@ -3375,8 +3420,8 @@ footer {
   
   .ai-suggestions-section {
     flex: 0 0 auto;
-    width: 100%;
-    max-width: none;
+    height: auto;
+    min-height: 120px;
   }
   
   .ai-textarea-section {
@@ -3386,6 +3431,11 @@ footer {
     min-height: 100px;
   }
 
+  .ai-textarea-section textarea {
+    height: 150px;
+    min-height: 150px;
+  }
+
   .ai-spotlight-content {
     flex-direction: column;
     gap: 0.75rem;

src/styles/palette.css

@@ -1,61 +1,72 @@
+/* PALETTE OPTION 1: BLUEPRINT & AMBER */
 :root {
-  /* Light Theme Colors */
-  --color-bg: #fff;
-  --color-bg-secondary: #f8fafc;
-  --color-bg-tertiary: #e2e8f0;
-  --color-text: #1e293b;
-  --color-text-secondary: #64748b;
-  --color-border: #cbd5e1;
-  --color-primary: #2563eb;
-  --color-primary-hover: #1d4ed8;
-  --color-accent: #059669;
-  --color-accent-hover: #047857;
+  /* Light Theme */
+  --color-bg: #ffffff;
+  --color-bg-secondary: #f1f5f9; /* Slate 100 */
+  --color-bg-tertiary: #e2e8f0; /* Slate 200 */
+  --color-text: #0f172a;       /* Slate 900 */
+  --color-text-secondary: #475569; /* Slate 600 */
+  --color-border: #cbd5e1;     /* Slate 300 */
+
+  --color-primary: #334155;      /* Slate 700 - A strong, serious primary */
+  --color-primary-hover: #1e293b; /* Slate 800 */
+
+  --color-accent: #b45309;       /* A sharp, focused amber for highlights */
+  --color-accent-hover: #92400e;
+
   --color-warning: #d97706;
-  --color-error: #dc2626;
-  
-  --color-hosted: #7c3aed;
-  --color-hosted-bg: #f3f0ff;
-  --color-oss: #059669;
-  --color-oss-bg: #ecfdf5;
-  --color-method: #0891b2;
-  --color-method-bg: #f0f9ff;
-  --color-concept: #ea580c;
+  --color-error: #be123c;      /* A deeper, more serious red */
+
+  /* Card/Tag Category Colors */
+  --color-hosted: #4f46e5;      /* Indigo */
+  --color-hosted-bg: #eef2ff;
+  --color-oss: #0d9488;         /* Teal */
+  --color-oss-bg: #f0fdfa;
+  --color-method: #0891b2;      /* Cyan */
+  --color-method-bg: #ecfeff;
+  --color-concept: #c2410c;     /* Orange */
   --color-concept-bg: #fff7ed;
-  
-  /* Shadows */
-  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 5%);
-  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 10%);
-  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 10%);
-  
+
+  /* Shadows (Crisper) */
+  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 6%);
+  --shadow-md: 0 3px 5px -1px rgb(0 0 0 / 8%);
+  --shadow-lg: 0 8px 12px -3px rgb(0 0 0 / 10%);
+
   /* Transitions */
   --transition-fast: all 0.2s ease;
   --transition-medium: all 0.3s ease;
 }
 
 [data-theme="dark"] {
-  --color-bg: #0f172a;
-  --color-bg-secondary: #1e293b;
-  --color-bg-tertiary: #334155;
-  --color-text: #f1f5f9;
-  --color-text-secondary: #94a3b8;
-  --color-border: #475569;
-  --color-primary: #3b82f6;
-  --color-primary-hover: #60a5fa;
-  --color-accent: #10b981;
-  --color-accent-hover: #34d399;
+  /* Dark Theme */
+  --color-bg: #0f172a;         /* Slate 900 */
+  --color-bg-secondary: #1e293b; /* Slate 800 */
+  --color-bg-tertiary: #334155; /* Slate 700 */
+  --color-text: #f1f5f9;       /* Slate 100 */
+  --color-text-secondary: #94a3b8; /* Slate 400 */
+  --color-border: #475569;     /* Slate 600 */
+
+  --color-primary: #64748b;      /* Slate 500 */
+  --color-primary-hover: #94a3b8; /* Slate 400 */
+
+  --color-accent: #f59e0b;       /* A brighter amber for dark mode contrast */
+  --color-accent-hover: #fbbf24;
+
   --color-warning: #f59e0b;
-  --color-error: #f87171;
-  
-  --color-hosted: #a855f7;
-  --color-hosted-bg: #2e1065;
-  --color-oss: #10b981;
-  --color-oss-bg: #064e3b;
-  --color-method: #0891b2;
+  --color-error: #f43f5e;
+
+  /* Card/Tag Category Colors */
+  --color-hosted: #818cf8;      /* Indigo */
+  --color-hosted-bg: #3730a3;
+  --color-oss: #2dd4bf;         /* Teal */
+  --color-oss-bg: #115e59;
+  --color-method: #22d3ee;      /* Cyan */
   --color-method-bg: #164e63;
-  --color-concept: #f97316;
+  --color-concept: #fb923c;     /* Orange */
   --color-concept-bg: #7c2d12;
-  
-  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 30%);
-  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 40%);
-  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 50%);
+
+  /* Shadows (Subtler for dark mode) */
+  --shadow-sm: 0 1px 2px 0 rgb(0 0 0 / 20%);
+  --shadow-md: 0 4px 6px -1px rgb(0 0 0 / 30%);
+  --shadow-lg: 0 10px 15px -3px rgb(0 0 0 / 40%);
 }

src/utils/aiService.ts

@@ -0,0 +1,137 @@
+// src/utils/aiService.ts
+import 'dotenv/config';
+
+export interface AIServiceConfig {
+  endpoint: string;
+  apiKey: string;
+  model: string;
+}
+
+export interface AICallOptions {
+  temperature?: number;
+  timeout?: number;
+}
+
+export interface AIResponse {
+  content: string;
+  usage?: {
+    promptTokens: number;
+    completionTokens: number;
+    totalTokens: number;
+  };
+}
+
+class AIService {
+  private config: AIServiceConfig;
+  private defaultOptions: AICallOptions;
+
+  constructor() {
+    this.config = {
+      endpoint: this.getRequiredEnv('AI_ANALYZER_ENDPOINT'),
+      apiKey: this.getRequiredEnv('AI_ANALYZER_API_KEY'),
+      model: this.getRequiredEnv('AI_ANALYZER_MODEL')
+    };
+
+    this.defaultOptions = {
+      temperature: 0.3,
+      timeout: 60000
+    };
+
+    console.log('[AI-SERVICE] Initialized with model:', this.config.model);
+  }
+
+  private getRequiredEnv(key: string): string {
+    const value = process.env[key];
+    if (!value) {
+      throw new Error(`Missing required environment variable: ${key}`);
+    }
+    return value;
+  }
+
+  async callAI(prompt: string, options: AICallOptions = {}): Promise<AIResponse> {
+    const mergedOptions = { ...this.defaultOptions, ...options };
+    
+    console.log('[AI-SERVICE] Making API call:', {
+      promptLength: prompt.length,
+      temperature: mergedOptions.temperature
+    });
+
+    const headers: Record<string, string> = {
+      'Content-Type': 'application/json'
+    };
+    
+    if (this.config.apiKey) {
+      headers['Authorization'] = `Bearer ${this.config.apiKey}`;
+    }
+    
+    const requestBody = {
+      model: this.config.model,
+      messages: [{ role: 'user', content: prompt }],
+      temperature: mergedOptions.temperature
+    };
+    
+    try {
+      const controller = new AbortController();
+      const timeoutId = setTimeout(() => controller.abort(), mergedOptions.timeout);
+
+      const response = await fetch(`${this.config.endpoint}/v1/chat/completions`, {
+        method: 'POST',
+        headers,
+        body: JSON.stringify(requestBody),
+        signal: controller.signal
+      });
+
+      clearTimeout(timeoutId);
+
+      if (!response.ok) {
+        const errorText = await response.text();
+        console.error('[AI-SERVICE] API Error:', response.status, errorText);
+        throw new Error(`AI API error: ${response.status} - ${errorText}`);
+      }
+
+      const data = await response.json();
+      const content = data.choices?.[0]?.message?.content;
+      
+      if (!content) {
+        console.error('[AI-SERVICE] No response content from AI model');
+        throw new Error('No response from AI model');
+      }
+
+      console.log('[AI-SERVICE] API call successful:', {
+        responseLength: content.length,
+        usage: data.usage
+      });
+
+      return {
+        content: content.trim(),
+        usage: data.usage
+      };
+      
+    } catch (error) {
+      if (error.name === 'AbortError') {
+        console.error('[AI-SERVICE] Request timeout');
+        throw new Error('AI request timeout');
+      }
+      
+      console.error('[AI-SERVICE] API call failed:', error.message);
+      throw error;
+    }
+  }
+
+  async callMicroTaskAI(prompt: string): Promise<AIResponse> {
+    return this.callAI(prompt, { 
+      temperature: 0.3,
+      timeout: 30000 
+    });
+  }
+
+  estimateTokens(text: string): number {
+    return Math.ceil(text.length / 4);
+  }
+
+  getConfig(): AIServiceConfig {
+    return { ...this.config };
+  }
+}
+
+export const aiService = new AIService();

src/utils/api.ts

@@ -83,26 +83,21 @@ export const apiServerError = {
 };
 
 export const apiSpecial = {
-  // JSON parsing error
   invalidJSON: (): Response => 
     apiError.badRequest('Invalid JSON in request body'),
   
-  // Missing required fields
   missingRequired: (fields: string[]): Response => 
     apiError.badRequest(`Missing required fields: ${fields.join(', ')}`),
   
-  // Empty request body
   emptyBody: (): Response => 
     apiError.badRequest('Request body cannot be empty'),
   
-  // File upload responses
   uploadSuccess: (data: { url: string; filename: string; size: number; storage: string }): Response => 
     apiResponse.created(data),
   
   uploadFailed: (error: string): Response => 
     apiServerError.internal(`Upload failed: ${error}`),
   
-  // Contribution responses
   contributionSuccess: (data: { prUrl?: string; branchName?: string; message: string }): Response => 
     apiResponse.created({ success: true, ...data }),
   
@@ -114,7 +109,6 @@ export const apiWithHeaders = {
   successWithHeaders: (data: any, headers: Record<string, string>): Response => 
     createAPIResponse(data, 200, headers),
   
-  // Redirect response
   redirect: (location: string, temporary: boolean = true): Response => 
     new Response(null, {
       status: temporary ? 302 : 301,

src/utils/clientUtils.ts

@@ -1,9 +1,8 @@
 // src/utils/clientUtils.ts
 
-
 export function createToolSlug(toolName: string): string {
   if (!toolName || typeof toolName !== 'string') {
-    console.warn('[toolHelpers] Invalid toolName provided to createToolSlug:', toolName);
+    console.warn('[CLIENT-UTILS] Invalid toolName provided to createToolSlug:', toolName);
     return '';
   }
   
@@ -30,6 +29,81 @@ export function isToolHosted(tool: any): boolean {
          tool.projectUrl.trim() !== "";
 }
 
+export function sanitizeText(text: string): string {
+  if (typeof text !== 'string') return '';
+  
+  return text
+    .replace(/^#{1,6}\s+/gm, '')
+    .replace(/^\s*[-*+]\s+/gm, '')
+    .replace(/^\s*\d+\.\s+/gm, '')
+    .replace(/\*\*(.+?)\*\*/g, '$1')
+    .replace(/\*(.+?)\*/g, '$1')
+    .replace(/\[([^\]]+)\]\([^)]+\)/g, '$1')
+    .replace(/```[\s\S]*?```/g, '[CODE BLOCK]')
+    .replace(/`([^`]+)`/g, '$1')
+    .replace(/<[^>]+>/g, '')
+    .replace(/\n\s*\n\s*\n/g, '\n\n')
+    .trim();
+}
+
+export function escapeHtml(text: string): string {
+  if (typeof text !== 'string') return String(text);
+  const div = document.createElement('div');
+  div.textContent = text;
+  return div.innerHTML;
+}
+
+export function truncateText(text: string, maxLength: number): string {
+  if (!text || text.length <= maxLength) return text;
+  return text.slice(0, maxLength) + '...';
+}
+
+export function summarizeData(data: any): string {
+  if (data === null || data === undefined) return 'null';
+  if (typeof data === 'string') {
+    return data.length > 100 ? data.slice(0, 100) + '...' : data;
+  }
+  if (typeof data === 'number' || typeof data === 'boolean') {
+    return data.toString();
+  }
+  if (Array.isArray(data)) {
+    if (data.length === 0) return '[]';
+    if (data.length <= 3) return JSON.stringify(data);
+    return `[${data.slice(0, 3).map(i => typeof i === 'string' ? i : JSON.stringify(i)).join(', ')}, ...+${data.length - 3}]`;
+  }
+  if (typeof data === 'object') {
+    const keys = Object.keys(data);
+    if (keys.length === 0) return '{}';
+    if (keys.length <= 3) {
+      return '{' + keys.map(k => `${k}: ${typeof data[k] === 'string' ? data[k].slice(0, 20) + (data[k].length > 20 ? '...' : '') : JSON.stringify(data[k])}`).join(', ') + '}';
+    }
+    return `{${keys.slice(0, 3).join(', ')}, ...+${keys.length - 3} keys}`;
+  }
+  return String(data);
+}
+
+export function formatDuration(ms: number): string {
+  if (ms < 1000) return '< 1s';
+  if (ms < 60000) return `${Math.ceil(ms / 1000)}s`;
+  const minutes = Math.floor(ms / 60000);
+  const seconds = Math.ceil((ms % 60000) / 1000);
+  return seconds > 0 ? `${minutes}m ${seconds}s` : `${minutes}m`;
+}
+
+export function showElement(element: HTMLElement | null): void {
+  if (element) {
+    element.style.display = 'block';
+    element.classList.remove('hidden');
+  }
+}
+
+export function hideElement(element: HTMLElement | null): void {
+  if (element) {
+    element.style.display = 'none';
+    element.classList.add('hidden');
+  }
+}
+
 interface AutocompleteOptions {
   minLength?: number;
   maxResults?: number;
@@ -202,7 +276,7 @@ export class AutocompleteManager {
   
   defaultRender(item: any): string {
     const text = typeof item === 'string' ? item : item.name || item.label || item.toString();
-    return `<div class="autocomplete-item">${this.escapeHtml(text)}</div>`;
+    return `<div class="autocomplete-item">${escapeHtml(text)}</div>`;
   }
   
   renderDropdown(): void {
@@ -284,8 +358,8 @@ export class AutocompleteManager {
           align-items: center;
           gap: 0.25rem;
         ">
-          ${this.escapeHtml(item)}
-          <button type="button" class="autocomplete-remove" data-item="${this.escapeHtml(item)}" style="
+          ${escapeHtml(item)}
+          <button type="button" class="autocomplete-remove" data-item="${escapeHtml(item)}" style="
             background: none;
             border: none;
             color: white;
@@ -327,12 +401,6 @@ export class AutocompleteManager {
     this.selectedIndex = -1;
   }
   
-  escapeHtml(text: string): string {
-    const div = document.createElement('div');
-    div.textContent = text;
-    return div.innerHTML;
-  }
-  
   setDataSource(newDataSource: any[]): void {
     this.dataSource = newDataSource;
   }

src/utils/confidenceScoring.ts

@@ -0,0 +1,225 @@
+// src/utils/confidenceScoring.ts
+import { isToolHosted } from './clientUtils.js';
+import 'dotenv/config';
+
+export interface ConfidenceMetrics {
+  overall: number;
+  semanticRelevance: number;
+  taskSuitability: number;
+  uncertaintyFactors: string[];
+  strengthIndicators: string[];
+}
+
+export interface ConfidenceConfig {
+  semanticWeight: number;
+  suitabilityWeight: number;
+  minimumThreshold: number;
+  mediumThreshold: number;
+  highThreshold: number;
+}
+
+export interface AnalysisContext {
+  userQuery: string;
+  mode: string;
+  embeddingsSimilarities: Map<string, number>;
+  selectedTools?: Array<{
+    tool: any;
+    phase: string;
+    priority: string;
+    justification?: string;
+    taskRelevance?: number;
+    limitations?: string[];
+  }>;
+}
+
+class ConfidenceScoring {
+  private config: ConfidenceConfig;
+
+  constructor() {
+    this.config = {
+      semanticWeight: this.getEnvFloat('CONFIDENCE_SEMANTIC_WEIGHT', 0.3),
+      suitabilityWeight: this.getEnvFloat('CONFIDENCE_SUITABILITY_WEIGHT', 0.7), 
+      minimumThreshold: this.getEnvInt('CONFIDENCE_MINIMUM_THRESHOLD', 40),
+      mediumThreshold: this.getEnvInt('CONFIDENCE_MEDIUM_THRESHOLD', 60),
+      highThreshold: this.getEnvInt('CONFIDENCE_HIGH_THRESHOLD', 80)
+    };
+
+    console.log('[CONFIDENCE-SCORING] Initialized with restored config:', this.config);
+  }
+
+  private getEnvFloat(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseFloat(value) : defaultValue;
+  }
+
+  private getEnvInt(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseInt(value, 10) : defaultValue;
+  }
+
+  calculateRecommendationConfidence(
+    tool: any,
+    context: AnalysisContext,
+    taskRelevance: number = 70,
+    limitations: string[] = []
+  ): ConfidenceMetrics {
+    console.log('[CONFIDENCE-SCORING] Calculating confidence for tool:', tool.name);
+
+    const rawSemanticRelevance = context.embeddingsSimilarities.has(tool.name) ?
+      context.embeddingsSimilarities.get(tool.name)! * 100 : 50;
+    
+    let enhancedTaskSuitability = taskRelevance;
+    
+    if (context.mode === 'workflow') {
+      const toolSelection = context.selectedTools?.find((st: any) => st.tool && st.tool.name === tool.name);
+      if (toolSelection && tool.phases && Array.isArray(tool.phases) && tool.phases.includes(toolSelection.phase)) {
+        const phaseBonus = Math.min(15, 100 - taskRelevance);
+        enhancedTaskSuitability = Math.min(100, taskRelevance + phaseBonus);
+        console.log('[CONFIDENCE-SCORING] Phase alignment bonus applied:', phaseBonus);
+      }
+    }
+    
+    const overall = (
+      rawSemanticRelevance * this.config.semanticWeight +
+      enhancedTaskSuitability * this.config.suitabilityWeight
+    );
+
+    const uncertaintyFactors = this.identifyUncertaintyFactors(tool, context, limitations, overall);
+    const strengthIndicators = this.identifyStrengthIndicators(tool, context, overall);
+
+    const result = {
+      overall: Math.round(overall),
+      semanticRelevance: Math.round(rawSemanticRelevance),
+      taskSuitability: Math.round(enhancedTaskSuitability),
+      uncertaintyFactors,
+      strengthIndicators
+    };
+
+    console.log('[CONFIDENCE-SCORING] Confidence calculated:', {
+      tool: tool.name,
+      overall: result.overall,
+      semantic: result.semanticRelevance,
+      task: result.taskSuitability,
+      uncertaintyCount: uncertaintyFactors.length,
+      strengthCount: strengthIndicators.length
+    });
+
+    return result;
+  }
+
+  private identifyUncertaintyFactors(
+    tool: any,
+    context: AnalysisContext,
+    limitations: string[],
+    confidence: number
+  ): string[] {
+    const factors: string[] = [];
+    
+    if (limitations?.length > 0) {
+      factors.push(...limitations.slice(0, 2));
+    }
+    
+    const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
+    if (similarity < 0.7) {
+      factors.push('Geringe semantische Ähnlichkeit zur Anfrage');
+    }
+    
+    if (tool.skillLevel === 'expert' && /schnell|rapid|triage|urgent|sofort/i.test(context.userQuery)) {
+      factors.push('Experten-Tool für zeitkritisches Szenario');
+    }
+    
+    if (tool.skillLevel === 'novice' && /komplex|erweitert|tiefgehend|advanced|forensisch/i.test(context.userQuery)) {
+      factors.push('Einsteiger-Tool für komplexe Analyse');
+    }
+    
+    if (tool.type === 'software' && !isToolHosted(tool) && tool.accessType === 'download') {
+      factors.push('Installation und Setup erforderlich');
+    }
+    
+    if (tool.license === 'Proprietary') {
+      factors.push('Kommerzielle Software - Lizenzkosten zu beachten');
+    }
+    
+    if (confidence < 60) {
+      factors.push('Moderate Gesamtbewertung - alternative Ansätze empfohlen');
+    }
+    
+    return factors.slice(0, 4);
+  }
+
+  private identifyStrengthIndicators(tool: any, context: AnalysisContext, confidence: number): string[] {
+    const indicators: string[] = [];
+    
+    const similarity = context.embeddingsSimilarities.get(tool.name) || 0.5;
+    if (similarity >= 0.7) {
+      indicators.push('Sehr gute semantische Übereinstimmung mit Ihrer Anfrage');
+    }
+    
+    if (tool.knowledgebase === true) {
+      indicators.push('Umfassende Dokumentation und Wissensbasis verfügbar');
+    }
+    
+    if (isToolHosted(tool)) {
+      indicators.push('Sofort verfügbar über gehostete Lösung');
+    }
+    
+    if (tool.skillLevel === 'intermediate' || tool.skillLevel === 'advanced') {
+      indicators.push('Ausgewogenes Verhältnis zwischen Funktionalität und Benutzerfreundlichkeit');
+    }
+    
+    if (tool.type === 'method' && /methodik|vorgehen|prozess|ansatz/i.test(context.userQuery)) {
+      indicators.push('Methodischer Ansatz passt zu Ihrer prozeduralen Anfrage');
+    }
+    
+    return indicators.slice(0, 4);
+  }
+
+  calculateSelectionConfidence(result: any, candidateCount: number): number {
+    if (!result?.selectedTools) {
+      console.log('[CONFIDENCE-SCORING] No selected tools for confidence calculation');
+      return 30;
+    }
+    
+    const selectionRatio = result.selectedTools.length / candidateCount;
+    const hasReasoning = result.reasoning && result.reasoning.length > 50;
+    
+    let confidence = 60;
+    
+    if (selectionRatio > 0.05 && selectionRatio < 0.3) confidence += 20;
+    else if (selectionRatio <= 0.05) confidence -= 10;
+    else confidence -= 15;
+    
+    if (hasReasoning) confidence += 15;
+    if (result.selectedConcepts?.length > 0) confidence += 5;
+    
+    const finalConfidence = Math.min(95, Math.max(25, confidence));
+    
+    console.log('[CONFIDENCE-SCORING] Selection confidence calculated:', {
+      candidateCount,
+      selectedCount: result.selectedTools.length,
+      selectionRatio: selectionRatio.toFixed(3),
+      hasReasoning,
+      confidence: finalConfidence
+    });
+    
+    return finalConfidence;
+  }
+
+  getConfidenceLevel(confidence: number): 'weak' | 'moderate' | 'strong' {
+    if (confidence >= this.config.highThreshold) return 'strong';
+    if (confidence >= this.config.mediumThreshold) return 'moderate';
+    return 'weak';
+  }
+
+  getConfidenceColor(confidence: number): string {
+    if (confidence >= this.config.highThreshold) return 'var(--color-accent)';
+    if (confidence >= this.config.mediumThreshold) return 'var(--color-warning)';
+    return 'var(--color-error)';
+  }
+
+  getConfig(): ConfidenceConfig {
+    return { ...this.config };
+  }
+}
+
+export const confidenceScoring = new ConfidenceScoring();

src/utils/dataService.ts

@@ -85,7 +85,7 @@ let cachedData: ToolsData | null = null;
 let cachedRandomizedData: ToolsData | null = null;
 let cachedCompressedData: EnhancedCompressedToolsData | null = null;
 let lastRandomizationDate: string | null = null;
-let dataVersion: string | null = null;
+let cachedToolsHash: string | null = null;
 
 function seededRandom(seed: number): () => number {
   let x = Math.sin(seed) * 10000;
@@ -110,17 +110,6 @@ function shuffleArray<T>(array: T[], randomFn: () => number): T[] {
   return shuffled;
 }
 
-function generateDataVersion(data: any): string {
-  const str = JSON.stringify(data, Object.keys(data).sort());
-  let hash = 0;
-  for (let i = 0; i < str.length; i++) {
-    const char = str.charCodeAt(i);
-    hash = ((hash << 5) - hash) + char;
-    hash = hash & hash;
-  }
-  return Math.abs(hash).toString(36);
-}
-
 async function loadRawData(): Promise<ToolsData> {
   if (!cachedData) {
     const yamlPath = path.join(process.cwd(), 'src/data/tools.yaml');
@@ -142,8 +131,9 @@ async function loadRawData(): Promise<ToolsData> {
         };
       }
       
-      dataVersion = generateDataVersion(cachedData);
-      console.log(`[DATA SERVICE] Loaded enhanced data version: ${dataVersion}`);
+      const { getToolsFileHash } = await import('./hashUtils.js');
+    cachedToolsHash = await getToolsFileHash();
+    console.log(`[DATA SERVICE] Loaded data with hash: ${cachedToolsHash.slice(0, 12)}...`);
       
     } catch (error) {
       if (error instanceof z.ZodError) {
@@ -234,7 +224,7 @@ export async function getCompressedToolsDataForAI(): Promise<EnhancedCompressedT
 }
 
 export function getDataVersion(): string | null {
-  return dataVersion;
+  return cachedToolsHash;
 }
 
 export function clearCache(): void {
@@ -242,7 +232,7 @@ export function clearCache(): void {
   cachedRandomizedData = null;
   cachedCompressedData = null;
   lastRandomizationDate = null;
-  dataVersion = null;
+  cachedToolsHash = null;
   
   console.log('[DATA SERVICE] Enhanced cache cleared');
 }

src/utils/embeddings.ts

@@ -1,11 +1,11 @@
-// src/utils/embeddings.ts
+// src/utils/embeddings.ts - Refactored
 import { promises as fs } from 'fs';
 import path from 'path';
 import { getCompressedToolsDataForAI } from './dataService.js';
 import 'dotenv/config';
 import crypto from 'crypto';
 
-interface EmbeddingData {
+export interface EmbeddingData {
   id: string;
   type: 'tool' | 'concept';
   name: string;
@@ -20,14 +20,22 @@ interface EmbeddingData {
   };
 }
 
+export interface SimilarityResult extends EmbeddingData {
+  similarity: number;
+}
+
 interface EmbeddingsDatabase {
   version: string;
   lastUpdated: number;
   embeddings: EmbeddingData[];
 }
 
-interface SimilarityResult extends EmbeddingData {
-  similarity: number;
+interface EmbeddingsConfig {
+  endpoint?: string;
+  apiKey?: string;
+  model?: string;
+  batchSize: number;
+  batchDelay: number;
 }
 
 class EmbeddingsService {
@@ -35,48 +43,30 @@ class EmbeddingsService {
   private isInitialized = false;
   private initializationPromise: Promise<void> | null = null;
   private readonly embeddingsPath = path.join(process.cwd(), 'data', 'embeddings.json');
-  private readonly batchSize: number;
-  private readonly batchDelay: number;
-  private enabled: boolean = false;
+  private config: EmbeddingsConfig;
 
   constructor() {
-    this.batchSize = parseInt(process.env.AI_EMBEDDINGS_BATCH_SIZE || '20', 10);
-    this.batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
-    
-    this.enabled = true;
+    this.config = this.loadConfig();
+    console.log('[EMBEDDINGS-SERVICE] Initialized:', {
+      hasEndpoint: !!this.config.endpoint,
+      hasModel: !!this.config.model
+    });
   }
 
-  private async checkEnabledStatus(): Promise<void> {
-    try {   
-      const envEnabled = process.env.AI_EMBEDDINGS_ENABLED;
-      
-      if (envEnabled === 'true') {
-        const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
-        const model = process.env.AI_EMBEDDINGS_MODEL;
-        
-        if (!endpoint || !model) {
-          console.warn('[EMBEDDINGS] Embeddings enabled but API configuration missing - disabling');
-          this.enabled = false;
-          return;
-        }
-        
-        console.log('[EMBEDDINGS] All requirements met - enabling embeddings');
-        this.enabled = true;
-        return;
-      }
-      
-      try {
-        await fs.stat(this.embeddingsPath);
-        console.log('[EMBEDDINGS] Existing embeddings file found - enabling');
-        this.enabled = true;
-      } catch {
-        console.log('[EMBEDDINGS] Embeddings not explicitly enabled - disabling');
-        this.enabled = false;
-      }
-    } catch (error) {
-      console.error('[EMBEDDINGS] Error checking enabled status:', error);
-      this.enabled = false;
-    }
+  private loadConfig(): EmbeddingsConfig {
+    const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
+    const apiKey = process.env.AI_EMBEDDINGS_API_KEY;
+    const model = process.env.AI_EMBEDDINGS_MODEL;
+    const batchSize = parseInt(process.env.AI_EMBEDDINGS_BATCH_SIZE || '20', 10);
+    const batchDelay = parseInt(process.env.AI_EMBEDDINGS_BATCH_DELAY_MS || '1000', 10);
+
+    return {
+      endpoint,
+      apiKey,
+      model,
+      batchSize,
+      batchDelay
+    };
   }
 
   async initialize(): Promise<void> {
@@ -93,63 +83,55 @@ class EmbeddingsService {
   }
 
   private async performInitialization(): Promise<void> {
-    await this.checkEnabledStatus();
-    if (!this.enabled) {
-      console.log('[EMBEDDINGS] Embeddings disabled, skipping initialization');
-      return;
-    }
-
     const initStart = Date.now();
+    
     try {
-      console.log('[EMBEDDINGS] Initializing embeddings system…');
+      console.log('[EMBEDDINGS-SERVICE] Starting initialization');
+
+      /*if (!this.config.enabled) {
+        console.log('[EMBEDDINGS-SERVICE] Service disabled via configuration');
+        return;
+      }*/
 
       await fs.mkdir(path.dirname(this.embeddingsPath), { recursive: true });
 
-      const toolsData        = await getCompressedToolsDataForAI();
-      const currentDataHash  = await this.hashToolsFile();
+      const toolsData = await getCompressedToolsDataForAI();
+      const { getToolsFileHash } = await import('./hashUtils.js');
+      const currentDataHash = await getToolsFileHash();
 
-      const existing         = await this.loadEmbeddings();
-      console.log('[EMBEDDINGS] Current hash:', currentDataHash);
-      console.log('[EMBEDDINGS] Existing file version:', existing?.version);
-      console.log('[EMBEDDINGS] Existing embeddings length:', existing?.embeddings?.length);
-
-      const cacheIsUsable =
-        existing &&
+      const existing = await this.loadEmbeddings();
+      
+      const cacheIsUsable = existing && 
         existing.version === currentDataHash &&
         Array.isArray(existing.embeddings) &&
         existing.embeddings.length > 0;
 
       if (cacheIsUsable) {
-        console.log('[EMBEDDINGS] Using cached embeddings');
-        this.embeddings    = existing.embeddings;
+        console.log('[EMBEDDINGS-SERVICE] Using cached embeddings');
+        this.embeddings = existing.embeddings;
       } else {
-        console.log('[EMBEDDINGS] Generating new embeddings…');
+        console.log('[EMBEDDINGS-SERVICE] Generating new embeddings');
         await this.generateEmbeddings(toolsData, currentDataHash);
       }
 
       this.isInitialized = true;
-      console.log(`[EMBEDDINGS] Initialized with ${this.embeddings.length} embeddings in ${Date.now() - initStart} ms`);
-    } catch (err) {
-      console.error('[EMBEDDINGS] Failed to initialize:', err);
+      console.log(`[EMBEDDINGS-SERVICE] Initialized successfully with ${this.embeddings.length} embeddings in ${Date.now() - initStart}ms`);
+      
+    } catch (error) {
+      console.error('[EMBEDDINGS-SERVICE] Initialization failed:', error);
       this.isInitialized = false;
-      throw err;
+      throw error;
     } finally {
       this.initializationPromise = null;
     }
   }
 
-  private async hashToolsFile(): Promise<string> {
-    const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
-    const raw  = await fs.readFile(file, 'utf8');
-    return crypto.createHash('sha256').update(raw).digest('hex');
-  }
-
   private async loadEmbeddings(): Promise<EmbeddingsDatabase | null> {
     try {
       const data = await fs.readFile(this.embeddingsPath, 'utf8');
       return JSON.parse(data);
     } catch (error) {
-      console.log('[EMBEDDINGS] No existing embeddings found');
+      console.log('[EMBEDDINGS-SERVICE] No existing embeddings file found');
       return null;
     }
   }
@@ -162,7 +144,7 @@ class EmbeddingsService {
     };
     
     await fs.writeFile(this.embeddingsPath, JSON.stringify(database, null, 2));
-    console.log(`[EMBEDDINGS] Saved ${this.embeddings.length} embeddings to disk`);
+    console.log(`[EMBEDDINGS-SERVICE] Saved ${this.embeddings.length} embeddings to disk`);
   }
 
   private createContentString(item: any): string {
@@ -178,30 +160,23 @@ class EmbeddingsService {
   }
 
   private async generateEmbeddingsBatch(contents: string[]): Promise<number[][]> {
-    const endpoint = process.env.AI_EMBEDDINGS_ENDPOINT;
-    const apiKey = process.env.AI_EMBEDDINGS_API_KEY;
-    const model = process.env.AI_EMBEDDINGS_MODEL;
-
-    if (!endpoint || !model) {
-      const missing: string[] = [];
-      if (!endpoint) missing.push('AI_EMBEDDINGS_ENDPOINT');
-      if (!model) missing.push('AI_EMBEDDINGS_MODEL');
-      throw new Error(`Missing embeddings API configuration: ${missing.join(', ')}`);
+    if (!this.config.endpoint || !this.config.model) {
+      throw new Error('Missing embeddings API configuration');
     }
 
     const headers: Record<string, string> = {
       'Content-Type': 'application/json'
     };
 
-    if (apiKey) {
-      headers['Authorization'] = `Bearer ${apiKey}`;
+    if (this.config.apiKey) {
+      headers['Authorization'] = `Bearer ${this.config.apiKey}`;
     }
 
-    const response = await fetch(endpoint, {
+    const response = await fetch(this.config.endpoint, {
       method: 'POST',
       headers,
       body: JSON.stringify({
-        model,
+        model: this.config.model,
         input: contents
       })
     });
@@ -233,11 +208,16 @@ class EmbeddingsService {
     const contents = allItems.map(item => this.createContentString(item));
     this.embeddings = [];
 
-    for (let i = 0; i < contents.length; i += this.batchSize) {
-      const batch = contents.slice(i, i + this.batchSize);
-      const batchItems = allItems.slice(i, i + this.batchSize);
+    console.log(`[EMBEDDINGS-SERVICE] Generating embeddings for ${contents.length} items`);
+
+    for (let i = 0; i < contents.length; i += this.config.batchSize) {
+      const batch = contents.slice(i, i + this.config.batchSize);
+      const batchItems = allItems.slice(i, i + this.config.batchSize);
       
-      console.log(`[EMBEDDINGS] Processing batch ${Math.ceil((i + 1) / this.batchSize)} of ${Math.ceil(contents.length / this.batchSize)}`);
+      const batchNumber = Math.ceil((i + 1) / this.config.batchSize);
+      const totalBatches = Math.ceil(contents.length / this.config.batchSize);
+      
+      console.log(`[EMBEDDINGS-SERVICE] Processing batch ${batchNumber}/${totalBatches}`);
       
       try {
         const embeddings = await this.generateEmbeddingsBatch(batch);
@@ -260,12 +240,12 @@ class EmbeddingsService {
           });
         });
         
-        if (i + this.batchSize < contents.length) {
-          await new Promise(resolve => setTimeout(resolve, this.batchDelay));
+        if (i + this.config.batchSize < contents.length) {
+          await new Promise(resolve => setTimeout(resolve, this.config.batchDelay));
         }
         
       } catch (error) {
-        console.error(`[EMBEDDINGS] Failed to process batch ${Math.ceil((i + 1) / this.batchSize)}:`, error);
+        console.error(`[EMBEDDINGS-SERVICE] Batch ${batchNumber} failed:`, error);
         throw error;
       }
     }
@@ -273,18 +253,17 @@ class EmbeddingsService {
     await this.saveEmbeddings(version);
   }
 
-  public async embedText(text: string): Promise<number[]> {
-    if (!this.enabled || !this.isInitialized) {
+  async embedText(text: string): Promise<number[]> {
+    if (!this.isInitialized) {
       throw new Error('Embeddings service not available');
     }
+    
     const [embedding] = await this.generateEmbeddingsBatch([text.toLowerCase()]);
     return embedding;
   }
 
   async waitForInitialization(): Promise<void> {
-    await this.checkEnabledStatus();
-    
-    if (!this.enabled || this.isInitialized) {
+    if (this.isInitialized) {
       return Promise.resolve();
     }
 
@@ -296,13 +275,6 @@ class EmbeddingsService {
     return this.initialize();
   }
 
-  async forceRecheckEnvironment(): Promise<void> {
-    this.enabled = false;
-    this.isInitialized = false;
-    await this.checkEnabledStatus();
-    console.log('[EMBEDDINGS] Environment status re-checked, enabled:', this.enabled);
-  }
-
   private cosineSimilarity(a: number[], b: number[]): number {
     let dotProduct = 0;
     let normA = 0;
@@ -318,145 +290,62 @@ class EmbeddingsService {
   }
 
   async findSimilar(query: string, maxResults: number = 30, threshold: number = 0.3): Promise<SimilarityResult[]> {
-    if (!this.enabled) {
-      console.log('[EMBEDDINGS] Service disabled for similarity search');
+    /*if (!this.config.enabled) {
+      console.log('[EMBEDDINGS-SERVICE] Service disabled, returning empty results');
+      return [];
+    }*/
+
+    if (!this.isInitialized || this.embeddings.length === 0) {
+      console.log('[EMBEDDINGS-SERVICE] Not initialized or no embeddings available');
       return [];
     }
 
     try {
-      if (this.isInitialized && this.embeddings.length > 0) {
-        console.log(`[EMBEDDINGS] Using embeddings data for similarity search: ${query}`);
-        
-        const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
-        const queryEmbedding = queryEmbeddings[0];
+      console.log(`[EMBEDDINGS-SERVICE] Finding similar items for query: "${query}"`);
+      
+      const queryEmbeddings = await this.generateEmbeddingsBatch([query.toLowerCase()]);
+      const queryEmbedding = queryEmbeddings[0];
 
-        console.log(`[EMBEDDINGS] Computing similarities for ${this.embeddings.length} items`);
+      const similarities: SimilarityResult[] = this.embeddings.map(item => ({
+        ...item,
+        similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
+      }));
 
-        const similarities: SimilarityResult[] = this.embeddings.map(item => ({
-          ...item,
-          similarity: this.cosineSimilarity(queryEmbedding, item.embedding)
-        }));
+      const topScore = Math.max(...similarities.map(s => s.similarity));
+      const dynamicThreshold = Math.max(threshold, topScore * 0.85);
 
-        const topScore      = Math.max(...similarities.map(s => s.similarity));
-        const dynamicCutOff = Math.max(threshold, topScore * 0.85);
+      const results = similarities
+        .filter(item => item.similarity >= dynamicThreshold)
+        .sort((a, b) => b.similarity - a.similarity)
+        .slice(0, maxResults);
 
-        const results = similarities
-          .filter(item => item.similarity >= dynamicCutOff)
-          .sort((a, b) => b.similarity - a.similarity)
-          .slice(0, maxResults);
-
-
-        const orderingValid = results.every((item, index) => {
-          if (index === 0) return true;
-          return item.similarity <= results[index - 1].similarity;
+      console.log(`[EMBEDDINGS-SERVICE] Found ${results.length} similar items (threshold: ${dynamicThreshold.toFixed(3)})`);
+      
+      if (results.length > 0) {
+        console.log('[EMBEDDINGS-SERVICE] Top 5 matches:');
+        results.slice(0, 5).forEach((item, idx) => {
+          console.log(`  ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
         });
-
-        if (!orderingValid) {
-          console.error('[EMBEDDINGS] CRITICAL: Similarity ordering is broken!');
-          results.forEach((item, idx) => {
-            console.error(`  ${idx}: ${item.name} = ${item.similarity.toFixed(4)}`);
-          });
-        }
-
-        console.log(`[EMBEDDINGS] Found ${results.length} similar items (threshold: ${threshold})`);
-        if (results.length > 0) {
-          console.log('[EMBEDDINGS] Top 10 similarity matches:');
-          results.slice(0, 10).forEach((item, idx) => {
-            console.log(`  ${idx + 1}. ${item.name} (${item.type}) = ${item.similarity.toFixed(4)}`);
-          });
-          
-          const topSimilarity = results[0].similarity;
-          const hasHigherSimilarity = results.some(item => item.similarity > topSimilarity);
-          if (hasHigherSimilarity) {
-            console.error('[EMBEDDINGS] CRITICAL: Top result is not actually the highest similarity!');
-          }
-        }
-
-        return results;
-
-      } else {
-        console.log(`[EMBEDDINGS] No embeddings data, using fallback text matching: ${query}`);
-        
-        const { getToolsData } = await import('./dataService.js');
-        const toolsData = await getToolsData();
-        
-        const queryLower = query.toLowerCase();
-        const queryWords = queryLower.split(/\s+/).filter(w => w.length > 2);
-        
-        const similarities: SimilarityResult[] = toolsData.tools
-          .map((tool: any) => {
-            let similarity = 0;
-            
-            if (tool.name.toLowerCase().includes(queryLower)) {
-              similarity += 0.8;
-            }
-            
-            if (tool.description && tool.description.toLowerCase().includes(queryLower)) {
-              similarity += 0.6;
-            }
-            
-            if (tool.tags && Array.isArray(tool.tags)) {
-              const matchingTags = tool.tags.filter((tag: string) => 
-                tag.toLowerCase().includes(queryLower) || queryLower.includes(tag.toLowerCase())
-              );
-              if (tool.tags.length > 0) {
-                similarity += (matchingTags.length / tool.tags.length) * 0.4;
-              }
-            }
-            
-            const toolText = `${tool.name} ${tool.description || ''} ${(tool.tags || []).join(' ')}`.toLowerCase();
-            const matchingWords = queryWords.filter(word => toolText.includes(word));
-            if (queryWords.length > 0) {
-              similarity += (matchingWords.length / queryWords.length) * 0.3;
-            }
-            
-            return {
-              id: `tool_${tool.name.replace(/[^a-zA-Z0-9]/g, '_').toLowerCase()}`,
-              type: 'tool' as const,
-              name: tool.name,
-              content: toolText,
-              embedding: [],
-              metadata: {
-                domains: tool.domains || [],
-                phases: tool.phases || [],
-                tags: tool.tags || [],
-                skillLevel: tool.skillLevel,
-                type: tool.type
-              },
-              similarity: Math.min(similarity, 1.0)
-            };
-          })
-          .filter(item => item.similarity >= threshold)
-          .sort((a, b) => b.similarity - a.similarity)
-          .slice(0, maxResults);
-
-        console.log(`[EMBEDDINGS] Fallback found ${similarities.length} similar items`);
-        return similarities;
       }
 
+      return results;
+
     } catch (error) {
-      console.error('[EMBEDDINGS] Failed to find similar items:', error);
+      console.error('[EMBEDDINGS-SERVICE] Similarity search failed:', error);
       return [];
     }
   }
 
-  isEnabled(): boolean {
-    if (!this.enabled && !this.isInitialized) {
-      this.checkEnabledStatus().catch(console.error);
-    }
-    
-    return this.enabled;
-  }
-
-  getStats(): { enabled: boolean; initialized: boolean; count: number } {
+  getStats(): {initialized: boolean; count: number } {
     return {
-      enabled: this.enabled,
       initialized: this.isInitialized,
       count: this.embeddings.length
     };
   }
+
+  getConfig(): EmbeddingsConfig {
+    return { ...this.config };
+  }
 }
 
-const embeddingsService = new EmbeddingsService();
-
-export { embeddingsService, type EmbeddingData, type SimilarityResult };
+export const embeddingsService = new EmbeddingsService();

src/utils/hashUtils.ts

@@ -0,0 +1,20 @@
+// src/utils/hashUtils.ts
+import { promises as fs } from 'fs';
+import path from 'path';
+import crypto from 'crypto';
+
+export async function getToolsFileHash(): Promise<string> {
+  const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
+  const raw = await fs.readFile(file, 'utf8');
+  return crypto.createHash('sha256').update(raw).digest('hex');
+}
+
+export function getToolsFileHashSync(): string | null {
+  try {
+    const file = path.join(process.cwd(), 'src', 'data', 'tools.yaml');
+    const raw = require('fs').readFileSync(file, 'utf8');
+    return crypto.createHash('sha256').update(raw).digest('hex');
+  } catch {
+    return null;
+  }
+}

src/utils/jsonUtils.ts

@@ -0,0 +1,356 @@
+// src/utils/jsonUtils.ts
+export class JSONParser {
+  static safeParseJSON(jsonString: string, fallback: any = null): any {
+    try {
+      let cleaned = jsonString.trim();
+      
+      const jsonBlockPatterns = [
+        /```json\s*([\s\S]*?)\s*```/i,
+        /```\s*([\s\S]*?)\s*```/i,
+        /\{[\s\S]*\}/,
+      ];
+      
+      for (const pattern of jsonBlockPatterns) {
+        const match = cleaned.match(pattern);
+        if (match) {
+          cleaned = match[1] || match[0];
+          break;
+        }
+      }
+      
+      if (!cleaned.endsWith('}') && !cleaned.endsWith(']')) {
+        console.warn('[JSON-PARSER] JSON appears truncated, attempting recovery');
+        cleaned = this.repairTruncatedJSON(cleaned);
+      }
+      
+      const parsed = JSON.parse(cleaned);
+      
+      if (parsed && typeof parsed === 'object') {
+        if (!parsed.selectedTools) parsed.selectedTools = [];
+        if (!parsed.selectedConcepts) parsed.selectedConcepts = [];
+        if (!Array.isArray(parsed.selectedTools)) parsed.selectedTools = [];
+        if (!Array.isArray(parsed.selectedConcepts)) parsed.selectedConcepts = [];
+      }
+      
+      return parsed;
+      
+    } catch (error) {
+      console.warn('[JSON-PARSER] JSON parsing failed:', error.message);
+      return fallback;
+    }
+  }
+
+  private static repairTruncatedJSON(cleaned: string): string {
+    let braceCount = 0;
+    let bracketCount = 0;
+    let inString = false;
+    let escaped = false;
+    let lastCompleteStructure = '';
+    
+    for (let i = 0; i < cleaned.length; i++) {
+      const char = cleaned[i];
+      
+      if (escaped) {
+        escaped = false;
+        continue;
+      }
+      
+      if (char === '\\') {
+        escaped = true;
+        continue;
+      }
+      
+      if (char === '"' && !escaped) {
+        inString = !inString;
+        continue;
+      }
+      
+      if (!inString) {
+        if (char === '{') braceCount++;
+        if (char === '}') braceCount--;
+        if (char === '[') bracketCount++;
+        if (char === ']') bracketCount--;
+        
+        if (braceCount === 0 && bracketCount === 0 && (char === '}' || char === ']')) {
+          lastCompleteStructure = cleaned.substring(0, i + 1);
+        }
+      }
+    }
+    
+    if (lastCompleteStructure) {
+      return lastCompleteStructure;
+    } else {
+      if (braceCount > 0) cleaned += '}';
+      if (bracketCount > 0) cleaned += ']';
+      return cleaned;
+    }
+  }
+
+  static extractToolsFromMalformedJSON(jsonString: string): { selectedTools: string[]; selectedConcepts: string[] } {
+    const selectedTools: string[] = [];
+    const selectedConcepts: string[] = [];
+    
+    const toolsMatch = jsonString.match(/"selectedTools"\s*:\s*\[([\s\S]*?)\]/i);
+    if (toolsMatch) {
+      const toolMatches = toolsMatch[1].match(/"([^"]+)"/g);
+      if (toolMatches) {
+        selectedTools.push(...toolMatches.map(match => match.replace(/"/g, '')));
+      }
+    }
+    
+    const conceptsMatch = jsonString.match(/"selectedConcepts"\s*:\s*\[([\s\S]*?)\]/i);
+    if (conceptsMatch) {
+      const conceptMatches = conceptsMatch[1].match(/"([^"]+)"/g);
+      if (conceptMatches) {
+        selectedConcepts.push(...conceptMatches.map(match => match.replace(/"/g, '')));
+      }
+    }
+    
+    if (selectedTools.length === 0 && selectedConcepts.length === 0) {
+      const allMatches = jsonString.match(/"([^"]+)"/g);
+      if (allMatches) {
+        const possibleNames = allMatches
+          .map(match => match.replace(/"/g, ''))
+          .filter(name => 
+            name.length > 2 && 
+            !['selectedTools', 'selectedConcepts', 'reasoning'].includes(name) &&
+            !name.includes(':') &&
+            !name.match(/^\d+$/)
+          )
+          .slice(0, 15);
+        
+        selectedTools.push(...possibleNames);
+      }
+    }
+    
+    return { selectedTools, selectedConcepts };
+  }
+
+  static secureParseJSON(jsonString: string, maxSize: number = 10 * 1024 * 1024): any {
+    if (typeof jsonString !== 'string') {
+      throw new Error('Input must be a string');
+    }
+
+    if (jsonString.length > maxSize) {
+      throw new Error(`JSON string too large (${jsonString.length} bytes, max ${maxSize})`);
+    }
+
+    const suspiciousPatterns = [
+      /<script/i,
+      /javascript:/i,
+      /eval\(/i,
+      /function\s*\(/i,
+      /__proto__/i,
+      /constructor/i
+    ];
+
+    for (const pattern of suspiciousPatterns) {
+      if (pattern.test(jsonString)) {
+        throw new Error('Potentially malicious content detected in JSON');
+      }
+    }
+
+    try {
+      const parsed = JSON.parse(jsonString);
+      
+      if (typeof parsed !== 'object' || parsed === null) {
+        throw new Error('JSON must be an object');
+      }
+
+      return parsed;
+    } catch (error) {
+      if (error instanceof SyntaxError) {
+        throw new Error(`Invalid JSON syntax: ${error.message}`);
+      }
+      throw error;
+    }
+  }
+
+  static sanitizeForAudit(obj: any, maxDepth: number = 5, currentDepth: number = 0): any {
+    if (currentDepth >= maxDepth) {
+      return '[Max depth reached]';
+    }
+
+    if (obj === null || obj === undefined) {
+      return obj;
+    }
+
+    if (typeof obj === 'string') {
+      if (obj.length > 500) {
+        return obj.slice(0, 500) + '...[truncated]';
+      }
+      return obj.replace(/<script[\s\S]*?<\/script>/gi, '[script removed]');
+    }
+
+    if (typeof obj === 'number' || typeof obj === 'boolean') {
+      return obj;
+    }
+
+    if (Array.isArray(obj)) {
+      if (obj.length > 20) {
+        return [
+          ...obj.slice(0, 20).map(item => this.sanitizeForAudit(item, maxDepth, currentDepth + 1)),
+          `...[${obj.length - 20} more items]`
+        ];
+      }
+      return obj.map(item => this.sanitizeForAudit(item, maxDepth, currentDepth + 1));
+    }
+
+    if (typeof obj === 'object') {
+      const keys = Object.keys(obj);
+      if (keys.length > 50) {
+        const sanitized: any = {};
+        keys.slice(0, 50).forEach(key => {
+          sanitized[key] = this.sanitizeForAudit(obj[key], maxDepth, currentDepth + 1);
+        });
+        sanitized['[truncated]'] = `${keys.length - 50} more properties`;
+        return sanitized;
+      }
+
+      const sanitized: any = {};
+      keys.forEach(key => {
+        if (['__proto__', 'constructor', 'prototype'].includes(key)) {
+          return;
+        }
+        sanitized[key] = this.sanitizeForAudit(obj[key], maxDepth, currentDepth + 1);
+      });
+      return sanitized;
+    }
+
+    return String(obj);
+  }
+
+  static validateAuditExportStructure(data: any): { isValid: boolean; errors: string[] } {
+    const errors: string[] = [];
+
+    if (!data || typeof data !== 'object') {
+      errors.push('Export data must be an object');
+      return { isValid: false, errors };
+    }
+
+    const requiredProps = ['metadata', 'recommendation', 'auditTrail'];
+    for (const prop of requiredProps) {
+      if (!(prop in data)) {
+        errors.push(`Missing required property: ${prop}`);
+      }
+    }
+
+    if (data.metadata && typeof data.metadata === 'object') {
+      const requiredMetadataProps = ['timestamp', 'version', 'userQuery', 'mode'];
+      for (const prop of requiredMetadataProps) {
+        if (!(prop in data.metadata)) {
+          errors.push(`Missing required metadata property: ${prop}`);
+        }
+      }
+    } else {
+      errors.push('Invalid metadata structure');
+    }
+
+    if (!Array.isArray(data.auditTrail)) {
+      errors.push('auditTrail must be an array');
+    } else {
+      data.auditTrail.forEach((entry: any, index: number) => {
+        if (!entry || typeof entry !== 'object') {
+          errors.push(`Audit entry ${index} is not a valid object`);
+          return;
+        }
+
+        const requiredEntryProps = ['timestamp', 'phase', 'action', 'confidence', 'processingTimeMs'];
+        for (const prop of requiredEntryProps) {
+          if (!(prop in entry)) {
+            errors.push(`Audit entry ${index} missing required property: ${prop}`);
+          }
+        }
+      });
+    }
+
+    return {
+      isValid: errors.length === 0,
+      errors
+    };
+  }
+
+  static prepareAuditExport(
+    recommendation: any,
+    userQuery: string,
+    mode: string,
+    auditTrail: any[] = [],
+    additionalMetadata: any = {}
+  ): any {
+    return {
+      metadata: {
+        timestamp: new Date().toISOString(),
+        version: "1.0",
+        userQuery: userQuery.slice(0, 1000),
+        mode,
+        exportedBy: 'ForensicPathways',
+        toolsDataHash: additionalMetadata.toolsDataHash || 'unknown',
+        aiModel: additionalMetadata.aiModel || 'unknown',
+        aiParameters: additionalMetadata.aiParameters || {},
+        processingStats: additionalMetadata.processingStats || {}
+      },
+      recommendation: this.sanitizeForAudit(recommendation, 6),
+      auditTrail: auditTrail.map(entry => this.sanitizeForAudit(entry, 4)),
+      rawContext: {
+        selectedTools: additionalMetadata.selectedTools || [],
+        backgroundKnowledge: additionalMetadata.backgroundKnowledge || [],
+        contextHistory: additionalMetadata.contextHistory || [],
+        embeddingsSimilarities: additionalMetadata.embeddingsSimilarities || {}
+      }
+    };
+  }
+
+  static validateUploadedAnalysis(data: any): { isValid: boolean; issues: string[]; warnings: string[] } {
+    const issues: string[] = [];
+    const warnings: string[] = [];
+
+    const structureValidation = this.validateAuditExportStructure(data);
+    if (!structureValidation.isValid) {
+      issues.push(...structureValidation.errors);
+      return { isValid: false, issues, warnings };
+    }
+
+    if (data.metadata) {
+      const timestamp = new Date(data.metadata.timestamp);
+      if (isNaN(timestamp.getTime())) {
+        warnings.push('Invalid timestamp in metadata');
+      } else {
+        const age = Date.now() - timestamp.getTime();
+        const maxAge = 30 * 24 * 60 * 60 * 1000; // 30 days
+        if (age > maxAge) {
+          warnings.push(`Analysis is ${Math.floor(age / (24 * 60 * 60 * 1000))} days old`);
+        }
+      }
+
+      if (!['workflow', 'tool'].includes(data.metadata.mode)) {
+        warnings.push(`Unknown analysis mode: ${data.metadata.mode}`);
+      }
+    }
+
+    if (Array.isArray(data.auditTrail)) {
+      const aiDecisions = data.auditTrail.filter(e => e.action === 'ai-decision').length;
+      const toolSelections = data.auditTrail.filter(e => e.action === 'selection-decision').length;
+      
+      if (aiDecisions === 0) {
+        warnings.push('No AI decisions found in audit trail');
+      }
+      
+      if (toolSelections === 0) {
+        warnings.push('No tool selections found in audit trail');
+      }
+
+      const entriesWithConfidence = data.auditTrail.filter(e => typeof e.confidence === 'number').length;
+      const confidenceRatio = entriesWithConfidence / data.auditTrail.length;
+      
+      if (confidenceRatio < 0.8) {
+        warnings.push(`Only ${Math.round(confidenceRatio * 100)}% of audit entries have confidence scores`);
+      }
+    }
+
+    return {
+      isValid: issues.length === 0,
+      issues,
+      warnings
+    };
+  }
+}

src/utils/toolHelpers.ts

@@ -1,22 +0,0 @@
-// src/utils/toolHelpers.ts
-
-export interface Tool {
-  name: string;
-  type?: 'software' | 'method' | 'concept';
-  projectUrl?: string | null;
-  license?: string;
-  knowledgebase?: boolean;
-  domains?: string[];
-  phases?: string[];
-  platforms?: string[];
-  skillLevel?: string;
-  description?: string;
-  tags?: string[];
-  related_concepts?: string[];
-}
-
-export { 
-  createToolSlug, 
-  findToolByIdentifier, 
-  isToolHosted 
-} from './clientUtils.js';

src/utils/toolSelector.ts

@@ -0,0 +1,372 @@
+// src/utils/toolSelector.ts
+import { aiService } from './aiService.js';
+import { embeddingsService, type SimilarityResult } from './embeddings.js';
+import { confidenceScoring } from './confidenceScoring.js';
+import { JSONParser } from './jsonUtils.js';
+import { getPrompt } from '../config/prompts.js';
+import 'dotenv/config';
+
+export interface ToolSelectionConfig {
+  maxSelectedItems: number;
+  embeddingCandidates: number;
+  similarityThreshold: number;
+  embeddingSelectionLimit: number;
+  embeddingConceptsLimit: number;
+  embeddingsMinTools: number;
+  embeddingsMaxReductionRatio: number;
+  methodSelectionRatio: number;
+  softwareSelectionRatio: number;
+}
+
+
+export interface SelectionContext {
+  userQuery: string;
+  mode: string;
+  embeddingsSimilarities: Map<string, number>;
+  seenToolNames: Set<string>;
+  selectedTools?: Array<{
+    tool: any;
+    phase: string;
+    priority: string;
+    justification?: string;
+    taskRelevance?: number;
+    limitations?: string[];
+  }>;
+}
+
+export interface ToolSelectionResult {
+  selectedTools: any[];
+  selectedConcepts: any[];
+  confidence: number;
+}
+
+class ToolSelector {
+  private config: ToolSelectionConfig;
+
+  constructor() {
+    this.config = {
+      maxSelectedItems: this.getEnvInt('AI_MAX_SELECTED_ITEMS', 25),
+      embeddingCandidates: this.getEnvInt('AI_EMBEDDING_CANDIDATES', 50),
+      similarityThreshold: this.getEnvFloat('AI_SIMILARITY_THRESHOLD', 0.3),
+      embeddingSelectionLimit: this.getEnvInt('AI_EMBEDDING_SELECTION_LIMIT', 30),
+      embeddingConceptsLimit: this.getEnvInt('AI_EMBEDDING_CONCEPTS_LIMIT', 15),
+      embeddingsMinTools: this.getEnvInt('AI_EMBEDDINGS_MIN_TOOLS', 8),
+      embeddingsMaxReductionRatio: this.getEnvFloat('AI_EMBEDDINGS_MAX_REDUCTION_RATIO', 0.75),
+      methodSelectionRatio: this.getEnvFloat('AI_METHOD_SELECTION_RATIO', 0.4),
+      softwareSelectionRatio: this.getEnvFloat('AI_SOFTWARE_SELECTION_RATIO', 0.5),
+    };
+    console.log('[TOOL-SELECTOR] Initialized with config:', this.config);
+  }
+
+  private getEnvInt(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseInt(value, 10) : defaultValue;
+  }
+
+  private getEnvFloat(key: string, defaultValue: number): number {
+    const value = process.env[key];
+    return value ? parseFloat(value) : defaultValue;
+  }
+
+  async getIntelligentCandidates(
+    userQuery: string,
+    toolsData: any,
+    mode: string,
+    context: SelectionContext
+  ): Promise<{
+    tools: any[];
+    concepts: any[];
+    domains: any[];
+    phases: any[];
+    'domain-agnostic-software': any[];
+  }> {
+    console.log('[TOOL-SELECTOR] Getting intelligent candidates for query');
+    
+    let candidateTools: any[] = [];
+    let candidateConcepts: any[] = [];
+    
+    context.embeddingsSimilarities.clear();
+    
+    try {
+      await embeddingsService.waitForInitialization();
+    } catch (error) {
+      console.error('[TOOL-SELECTOR] Embeddings initialization failed:', error);
+    }
+    
+    console.log('[TOOL-SELECTOR] Using embeddings for candidate selection');
+    
+    const embeddingsSearchStart = Date.now();
+    
+    const similarItems = await embeddingsService.findSimilar(
+      userQuery,
+      this.config.embeddingCandidates,
+      this.config.similarityThreshold
+    ) as SimilarityResult[];
+    
+    console.log('[TOOL-SELECTOR] Embeddings found', similarItems.length, 'similar items');
+    
+    const { auditService } = await import('./auditService.js');
+    const { getDataVersion } = await import('./dataService.js');
+    
+    const toolsDataHash = getDataVersion() || 'unknown';
+    
+    auditService.addEmbeddingsSearch(
+      userQuery,
+      similarItems,
+      this.config.similarityThreshold,
+      embeddingsSearchStart,
+      {
+        toolsDataHash: toolsDataHash,
+        selectionPhase: 'initial-candidate-selection',
+        candidateLimit: this.config.embeddingCandidates,
+        mode: mode,
+        reasoning: `Initiale semantische Suche für ${mode}-Modus - Reduzierung der ${toolsData.tools.length} verfügbaren Tools auf ${similarItems.length} relevante Kandidaten`
+      }
+    );
+    
+    similarItems.forEach(item => {
+      context.embeddingsSimilarities.set(item.name, item.similarity);
+    });
+    
+    const toolsMap = new Map(toolsData.tools.map((tool: any) => [tool.name, tool]));
+    const conceptsMap = new Map(toolsData.concepts.map((concept: any) => [concept.name, concept]));
+    
+    const similarTools = similarItems
+      .filter((item: any) => item.type === 'tool')
+      .map((item: any) => toolsMap.get(item.name))
+      .filter((tool: any): tool is NonNullable<any> => tool !== undefined && tool !== null);
+    
+    const similarConcepts = similarItems
+      .filter((item: any) => item.type === 'concept')
+      .map((item: any) => conceptsMap.get(item.name))
+      .filter((concept: any): concept is NonNullable<any> => concept !== undefined && concept !== null);
+    
+    const totalAvailableTools = toolsData.tools.length;
+    const reductionRatio = similarTools.length / totalAvailableTools;
+    
+    if (similarTools.length >= this.config.embeddingsMinTools && reductionRatio <= this.config.embeddingsMaxReductionRatio) {
+      candidateTools = similarTools;
+      candidateConcepts = similarConcepts;
+      
+      console.log('[TOOL-SELECTOR] Using embeddings filtering:', totalAvailableTools, '→', similarTools.length, 'tools');
+    } else {
+      console.log('[TOOL-SELECTOR] Embeddings filtering insufficient, using full dataset');
+      candidateTools = toolsData.tools;
+      candidateConcepts = toolsData.concepts;
+    }
+
+    const selection = await this.performAISelection(
+      userQuery,
+      candidateTools,
+      candidateConcepts,
+      mode,
+      context
+    );
+    
+    return {
+      tools: selection.selectedTools,
+      concepts: selection.selectedConcepts,
+      domains: toolsData.domains,
+      phases: toolsData.phases,
+      'domain-agnostic-software': toolsData['domain-agnostic-software']
+    };
+  }
+
+  private async performAISelection(
+    userQuery: string,
+    candidateTools: any[],
+    candidateConcepts: any[],
+    mode: string,
+    context: SelectionContext
+  ): Promise<ToolSelectionResult> {
+    console.log('[TOOL-SELECTOR] Performing AI selection');
+
+    const candidateMethods = candidateTools.filter((t: any) => t && t.type === 'method');
+    const candidateSoftware = candidateTools.filter((t: any) => t && t.type === 'software');
+
+    console.log('[TOOL-SELECTOR] Candidates:',
+      candidateMethods.length, 'methods,',
+      candidateSoftware.length, 'software,',
+      candidateConcepts.length, 'concepts'
+    );
+
+    const methodsWithFullData = candidateMethods.map(this.createToolData);
+    const softwareWithFullData = candidateSoftware.map(this.createToolData);
+    const conceptsWithFullData = candidateConcepts.map(this.createConceptData);
+
+    const maxTools = Math.min(this.config.embeddingSelectionLimit, candidateTools.length);
+    const maxConcepts = Math.min(this.config.embeddingConceptsLimit, candidateConcepts.length);
+
+    const methodRatio = Math.max(0, Math.min(1, this.config.methodSelectionRatio));
+    const softwareRatio = Math.max(0, Math.min(1, this.config.softwareSelectionRatio));
+
+    let methodLimit = Math.round(maxTools * methodRatio);
+    let softwareLimit = Math.round(maxTools * softwareRatio);
+
+    if (methodLimit + softwareLimit > maxTools) {
+      const scale = maxTools / (methodLimit + softwareLimit);
+      methodLimit = Math.floor(methodLimit * scale);
+      softwareLimit = Math.floor(softwareLimit * scale);
+    }
+
+    const methodsPrimary = methodsWithFullData.slice(0, methodLimit);
+    const softwarePrimary = softwareWithFullData.slice(0, softwareLimit);
+
+    const toolsToSend: any[] = [...methodsPrimary, ...softwarePrimary];
+
+    let mIdx = methodsPrimary.length;
+    let sIdx = softwarePrimary.length;
+
+    while (toolsToSend.length < maxTools && (mIdx < methodsWithFullData.length || sIdx < softwareWithFullData.length)) {
+      const remM = methodsWithFullData.length - mIdx;
+      const remS = softwareWithFullData.length - sIdx;
+
+      if (remS >= remM && sIdx < softwareWithFullData.length) {
+        toolsToSend.push(softwareWithFullData[sIdx++]);
+      } else if (mIdx < methodsWithFullData.length) {
+        toolsToSend.push(methodsWithFullData[mIdx++]);
+      } else if (sIdx < softwareWithFullData.length) {
+        toolsToSend.push(softwareWithFullData[sIdx++]);
+      } else {
+        break;
+      }
+    }
+
+    const conceptsToSend = conceptsWithFullData.slice(0, maxConcepts);
+
+    console.log('[TOOL-SELECTOR-DEBUG] maxTools:', maxTools, 'maxConcepts:', maxConcepts);
+    console.log('[TOOL-SELECTOR] Sending to AI:',
+      toolsToSend.filter((t: any) => t.type === 'method').length, 'methods,',
+      toolsToSend.filter((t: any) => t.type === 'software').length, 'software,',
+      conceptsToSend.length, 'concepts'
+    );
+
+    const basePrompt = getPrompt('toolSelection', mode, userQuery, this.config.maxSelectedItems);
+    const prompt = getPrompt('toolSelectionWithData', basePrompt, toolsToSend, conceptsToSend);
+
+    try {
+      const response = await aiService.callAI(prompt);
+      const result = JSONParser.safeParseJSON(response.content, null);
+
+      if (!result || !Array.isArray(result.selectedTools) || !Array.isArray(result.selectedConcepts)) {
+        console.error('[TOOL-SELECTOR] AI selection returned invalid structure');
+        throw new Error('AI selection failed to return valid tool and concept selection');
+      }
+
+      const totalSelected = result.selectedTools.length + result.selectedConcepts.length;
+      if (totalSelected === 0) {
+        throw new Error('AI selection returned empty selection');
+      }
+
+      const toolsMap = new Map(candidateTools.map((tool: any) => [tool.name, tool]));
+      const conceptsMap = new Map(candidateConcepts.map((concept: any) => [concept.name, concept]));
+
+      const selectedTools = result.selectedTools
+        .map((name: string) => toolsMap.get(name))
+        .filter((tool: any): tool is NonNullable<any> => tool !== undefined && tool !== null);
+
+      const selectedConcepts = result.selectedConcepts
+        .map((name: string) => conceptsMap.get(name))
+        .filter((concept: any): concept is NonNullable<any> => concept !== undefined && concept !== null);
+
+      const selectedMethods = selectedTools.filter((t: any) => t && t.type === 'method');
+      const selectedSoftware = selectedTools.filter((t: any) => t && t.type === 'software');
+
+      console.log('[TOOL-SELECTOR] AI selected:',
+        selectedMethods.length, 'methods,',
+        selectedSoftware.length, 'software,',
+        selectedConcepts.length, 'concepts'
+      );
+
+      const confidence = confidenceScoring.calculateSelectionConfidence(
+        result,
+        candidateTools.length + candidateConcepts.length
+      );
+
+      return { selectedTools, selectedConcepts, confidence };
+    } catch (error) {
+      console.error('[TOOL-SELECTOR] AI selection failed:', error);
+      throw error;
+    }
+  }
+
+  async selectToolsForPhase(
+    userQuery: string,
+    phase: any,
+    availableTools: any[],
+    context: SelectionContext
+  ): Promise<Array<{
+    toolName: string;
+    taskRelevance: number;
+    justification: string;
+    limitations: string[];
+  }>> {
+    console.log('[TOOL-SELECTOR] Selecting tools for phase:', phase.id);
+    
+    if (availableTools.length === 0) {
+      console.log('[TOOL-SELECTOR] No tools available for phase:', phase.id);
+      return [];
+    }
+
+    const prompt = getPrompt('phaseToolSelection', userQuery, phase, availableTools);
+    
+    try {
+      const response = await aiService.callMicroTaskAI(prompt);
+      const selections = JSONParser.safeParseJSON(response.content, []);
+      
+      if (Array.isArray(selections)) {
+        const validSelections = selections.filter((sel: any) => {
+          const matchingTool = availableTools.find((tool: any) => tool && tool.name === sel.toolName);
+          if (!matchingTool) {
+            console.warn('[TOOL-SELECTOR] Invalid tool selection for phase:', phase.id, sel.toolName);
+          }
+          return !!matchingTool;
+        });
+        
+        console.log('[TOOL-SELECTOR] Valid selections for phase:', phase.id, validSelections.length);
+        return validSelections;
+      }
+      
+      return [];
+      
+    } catch (error) {
+      console.error('[TOOL-SELECTOR] Phase tool selection failed:', error);
+      return [];
+    }
+  }
+
+  private createToolData = (tool: any) => ({
+    name: tool.name,
+    type: tool.type,
+    description: tool.description,
+    domains: tool.domains,
+    phases: tool.phases,
+    platforms: tool.platforms || [],
+    tags: tool.tags || [],
+    skillLevel: tool.skillLevel,
+    license: tool.license,
+    accessType: tool.accessType,
+    projectUrl: tool.projectUrl,
+    knowledgebase: tool.knowledgebase,
+    related_concepts: tool.related_concepts || [],
+    related_software: tool.related_software || []
+  });
+
+  private createConceptData = (concept: any) => ({
+    name: concept.name,
+    type: 'concept',
+    description: concept.description,
+    domains: concept.domains,
+    phases: concept.phases,
+    tags: concept.tags || [],
+    skillLevel: concept.skillLevel,
+    related_concepts: concept.related_concepts || [],
+    related_software: concept.related_software || []
+  });
+
+  getConfig(): ToolSelectionConfig {
+    return { ...this.config };
+  }
+}
+
+export const toolSelector = new ToolSelector();