feat: add zero-shot knowledge synthesizer (#205)

Implement knowledge_synthesizer.py — a pipeline that picks two
unrelated knowledge entries, calls the LLM to generate a novel
hypothesis bridging them, scores plausibility, and stores the
result as a new pattern fact if above threshold.

- scripts/knowledge_synthesizer.py: main pipeline
- templates/synthesis-prompt.md: LLM prompt
- scripts/test_knowledge_synthesizer.py: 11 tests, all passing
- Supports both LLM synthesis and heuristic fallback
- Respects existing knowledge deduplication
- Integration test demonstrates end-to-end storage

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc

quality_gate.py

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+#!/usr/bin/env python3
+"""
+quality_gate.py — Score and filter knowledge entries.
+
+Scores each entry on 4 dimensions:
+- Specificity: concrete examples vs vague generalities
+- Actionability: can this be used to do something?
+- Freshness: is this still accurate?
+- Source quality: was the model/provider reliable?
+
+Usage:
+    from quality_gate import score_entry, filter_entries, quality_report
+
+    score = score_entry(entry)
+    filtered = filter_entries(entries, threshold=0.5)
+    report = quality_report(entries)
+"""
+
+import json
+import math
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Any, Optional
+
+# Source quality scores (higher = more reliable)
+SOURCE_QUALITY = {
+    "claude-sonnet": 0.9,
+    "claude-opus": 0.95,
+    "gpt-4": 0.85,
+    "gpt-4-turbo": 0.85,
+    "gpt-5": 0.9,
+    "mimo-v2-pro": 0.8,
+    "gemini-pro": 0.8,
+    "llama-3-70b": 0.75,
+    "llama-3-8b": 0.7,
+    "ollama": 0.6,
+    "unknown": 0.5,
+}
+
+DEFAULT_SOURCE_QUALITY = 0.5
+
+# Specificity indicators
+SPECIFIC_INDICATORS = [
+    r"\b\d+\.\d+",  # decimal numbers
+    r"\b\d{4}-\d{2}-\d{2}",  # dates
+    r"\b[A-Z][a-z]+\s[A-Z][a-z]+",  # proper nouns
+    r"`[^`]+`",  # code/commands
+    r"https?://",  # URLs
+    r"\b(example|instance|specifically|concretely)\b",
+    r"\b(step \d|first|second|third)\b",
+    r"\b(exactly|precisely|measured|counted)\b",
+]
+
+# Vagueness indicators (penalty)
+VAGUE_INDICATORS = [
+    r"\b(generally|usually|often|sometimes|might|could|perhaps)\b",
+    r"\b(various|several|many|some|few)\b",
+    r"\b(it depends|varies|differs)\b",
+    r"\b(basically|essentially|fundamentally)\b",
+    r"\b(everyone knows|it's obvious|clearly)\b",
+]
+
+# Actionability indicators
+ACTIONABLE_INDICATORS = [
+    r"\b(run|execute|install|deploy|configure|set up)\b",
+    r"\b(use|apply|implement|create|build)\b",
+    r"\b(check|verify|test|validate|confirm)\b",
+    r"\b(fix|resolve|solve|debug|troubleshoot)\b",
+    r"\b(if .+ then|when .+ do|to .+ use)\b",
+    r"```[a-z]*\n",  # code blocks
+    r"\$\s",  # shell commands
+    r"\b\d+\.\s",  # numbered steps
+]
+
+
+def score_specificity(content: str) -> float:
+    """Score specificity: 0=vague, 1=very specific."""
+    content_lower = content.lower()
+    score = 0.5  # baseline
+
+    # Check for specific indicators
+    specific_count = sum(
+        len(re.findall(p, content, re.IGNORECASE))
+        for p in SPECIFIC_INDICATORS
+    )
+
+    # Check for vague indicators
+    vague_count = sum(
+        len(re.findall(p, content_lower))
+        for p in VAGUE_INDICATORS
+    )
+
+    # Adjust score
+    score += min(specific_count * 0.05, 0.4)
+    score -= min(vague_count * 0.08, 0.3)
+
+    # Length bonus (longer = more detail, up to a point)
+    word_count = len(content.split())
+    if word_count > 50:
+        score += min((word_count - 50) * 0.001, 0.1)
+
+    return max(0.0, min(1.0, score))
+
+
+def score_actionability(content: str) -> float:
+    """Score actionability: 0=abstract, 1=highly actionable."""
+    content_lower = content.lower()
+    score = 0.3  # baseline (most knowledge is informational)
+
+    # Check for actionable indicators
+    actionable_count = sum(
+        len(re.findall(p, content_lower))
+        for p in ACTIONABLE_INDICATORS
+    )
+
+    score += min(actionable_count * 0.1, 0.6)
+
+    # Code blocks are highly actionable
+    if "```" in content:
+        score += 0.2
+
+    # Numbered steps are actionable
+    if re.search(r"\d+\.\s+\w", content):
+        score += 0.1
+
+    return max(0.0, min(1.0, score))
+
+
+def score_freshness(timestamp: Optional[str]) -> float:
+    """Score freshness: 1=new, decays over time."""
+    if not timestamp:
+        return 0.5
+
+    try:
+        if isinstance(timestamp, str):
+            ts = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
+        else:
+            ts = timestamp
+
+        now = datetime.now(timezone.utc)
+        age_days = (now - ts).days
+
+        # Exponential decay: 1.0 at day 0, 0.5 at ~180 days, 0.1 at ~365 days
+        score = math.exp(-age_days / 180)
+        return max(0.1, min(1.0, score))
+    except (ValueError, TypeError):
+        return 0.5
+
+
+def score_source_quality(model: Optional[str]) -> float:
+    """Score source quality based on model/provider."""
+    if not model:
+        return DEFAULT_SOURCE_QUALITY
+
+    # Normalize model name
+    model_lower = model.lower()
+    for key, score in SOURCE_QUALITY.items():
+        if key in model_lower:
+            return score
+
+    return DEFAULT_SOURCE_QUALITY
+
+
+def score_entry(entry: dict) -> float:
+    """
+    Score a knowledge entry on quality (0.0-1.0).
+
+    Weights:
+    - specificity: 0.3
+    - actionability: 0.3
+    - freshness: 0.2
+    - source_quality: 0.2
+    """
+    content = entry.get("content", entry.get("text", entry.get("response", "")))
+    model = entry.get("model", entry.get("provenance", {}).get("model"))
+    timestamp = entry.get("timestamp", entry.get("provenance", {}).get("timestamp"))
+
+    specificity = score_specificity(content)
+    actionability = score_actionability(content)
+    freshness = score_freshness(timestamp)
+    source = score_source_quality(model)
+
+    return round(
+        0.3 * specificity +
+        0.3 * actionability +
+        0.2 * freshness +
+        0.2 * source,
+        4
+    )
+
+
+def score_entry_detailed(entry: dict) -> dict:
+    """Score with breakdown."""
+    content = entry.get("content", entry.get("text", entry.get("response", "")))
+    model = entry.get("model", entry.get("provenance", {}).get("model"))
+    timestamp = entry.get("timestamp", entry.get("provenance", {}).get("timestamp"))
+
+    specificity = score_specificity(content)
+    actionability = score_actionability(content)
+    freshness = score_freshness(timestamp)
+    source = score_source_quality(model)
+
+    return {
+        "score": round(0.3 * specificity + 0.3 * actionability + 0.2 * freshness + 0.2 * source, 4),
+        "specificity": round(specificity, 4),
+        "actionability": round(actionability, 4),
+        "freshness": round(freshness, 4),
+        "source_quality": round(source, 4),
+    }
+
+
+def filter_entries(entries: List[dict], threshold: float = 0.5) -> List[dict]:
+    """Filter entries below quality threshold."""
+    filtered = []
+    for entry in entries:
+        if score_entry(entry) >= threshold:
+            filtered.append(entry)
+    return filtered
+
+
+def quality_report(entries: List[dict]) -> str:
+    """Generate quality distribution report."""
+    if not entries:
+        return "No entries to analyze."
+
+    scores = [score_entry(e) for e in entries]
+
+    avg = sum(scores) / len(scores)
+    min_score = min(scores)
+    max_score = max(scores)
+
+    # Distribution buckets
+    buckets = {"high": 0, "medium": 0, "low": 0, "rejected": 0}
+    for s in scores:
+        if s >= 0.7:
+            buckets["high"] += 1
+        elif s >= 0.5:
+            buckets["medium"] += 1
+        elif s >= 0.3:
+            buckets["low"] += 1
+        else:
+            buckets["rejected"] += 1
+
+    lines = [
+        "=" * 50,
+        "  QUALITY GATE REPORT",
+        "=" * 50,
+        f"  Total entries:  {len(entries)}",
+        f"  Average score:  {avg:.3f}",
+        f"  Min:            {min_score:.3f}",
+        f"  Max:            {max_score:.3f}",
+        "",
+        "  Distribution:",
+    ]
+
+    for bucket, count in buckets.items():
+        pct = count / len(entries) * 100
+        bar = "█" * int(pct / 5)
+        lines.append(f"    {bucket:<12} {count:>5} ({pct:>5.1f}%) {bar}")
+
+    passed = buckets["high"] + buckets["medium"]
+    lines.append(f"\n  Pass rate (>= 0.5): {passed}/{len(entries)} ({passed/len(entries)*100:.1f}%)")
+    lines.append("=" * 50)
+
+    return "\n".join(lines)
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Knowledge quality gate")
+    parser.add_argument("files", nargs="+", help="JSONL files to score")
+    parser.add_argument("--threshold", type=float, default=0.5, help="Quality threshold")
+    parser.add_argument("--json", action="store_true", help="JSON output")
+    parser.add_argument("--filter", action="store_true", help="Filter and write back")
+    args = parser.parse_args()
+
+    all_entries = []
+    for filepath in args.files:
+        with open(filepath) as f:
+            for line in f:
+                if line.strip():
+                    all_entries.append(json.loads(line))
+
+    if args.json:
+        results = [{"entry": e, **score_entry_detailed(e)} for e in all_entries]
+        print(json.dumps(results, indent=2))
+    elif args.filter:
+        filtered = filter_entries(all_entries, args.threshold)
+        print(f"Kept {len(filtered)}/{len(all_entries)} entries (threshold: {args.threshold})")
+    else:
+        print(quality_report(all_entries))
+
+
+if __name__ == "__main__":
+    main()

scripts/dedup.py

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+#!/usr/bin/env python3
+"""
+dedup.py — Knowledge deduplication: content hash + semantic similarity.
+
+Deduplicates harvested knowledge entries to avoid training on duplicates.
+Uses content hashing for exact matches and token overlap for near-duplicates.
+
+Usage:
+    python3 dedup.py --input knowledge/index.json --output knowledge/index_deduped.json
+    python3 dedup.py --input knowledge/index.json --dry-run
+    python3 dedup.py --test  # Run built-in dedup test
+"""
+
+import argparse
+import hashlib
+import json
+import re
+import sys
+from pathlib import Path
+from typing import List, Dict, Optional, Tuple
+
+
+def normalize_text(text: str) -> str:
+    """Normalize text for hashing: lowercase, collapse whitespace, strip."""
+    text = text.lower().strip()
+    text = re.sub(r'\s+', ' ', text)
+    return text
+
+
+def content_hash(text: str) -> str:
+    """SHA256 hash of normalized text for exact dedup."""
+    normalized = normalize_text(text)
+    return hashlib.sha256(normalized.encode('utf-8')).hexdigest()
+
+
+def tokenize(text: str) -> set:
+    """Simple tokenizer: lowercase words, 3+ chars."""
+    words = re.findall(r'[a-z0-9_]{3,}', text.lower())
+    return set(words)
+
+
+def token_similarity(a: str, b: str) -> float:
+    """Token-based Jaccard similarity (0.0-1.0).
+
+    Fast local alternative to embedding similarity.
+    Good enough for near-duplicate detection.
+    """
+    tokens_a = tokenize(a)
+    tokens_b = tokenize(b)
+    if not tokens_a or not tokens_b:
+        return 0.0
+    intersection = tokens_a & tokens_b
+    union = tokens_a | tokens_b
+    return len(intersection) / len(union)
+
+
+def quality_score(fact: dict) -> float:
+    """Compute quality score for merge ranking.
+
+    Higher is better. Factors:
+    - confidence (0-1)
+    - source_count (more confirmations = better)
+    - has tags (richer metadata)
+    """
+    confidence = fact.get('confidence', 0.5)
+    source_count = fact.get('source_count', 1)
+    has_tags = 1.0 if fact.get('tags') else 0.0
+    has_related = 1.0 if fact.get('related') else 0.0
+
+    # Weighted composite
+    score = (
+        confidence * 0.5 +
+        min(source_count / 10, 1.0) * 0.3 +
+        has_tags * 0.1 +
+        has_related * 0.1
+    )
+    return round(score, 4)
+
+
+def merge_facts(keep: dict, drop: dict) -> dict:
+    """Merge two near-duplicate facts, keeping higher-quality fields.
+
+    The 'keep' fact is enriched with metadata from 'drop'.
+    """
+    # Merge tags (union)
+    keep_tags = set(keep.get('tags', []))
+    drop_tags = set(drop.get('tags', []))
+    keep['tags'] = sorted(keep_tags | drop_tags)
+
+    # Merge related (union)
+    keep_related = set(keep.get('related', []))
+    drop_related = set(drop.get('related', []))
+    keep['related'] = sorted(keep_related | drop_related)
+
+    # Update source_count (sum)
+    keep['source_count'] = keep.get('source_count', 1) + drop.get('source_count', 1)
+
+    # Update confidence (max — we've now seen it from multiple sources)
+    keep['confidence'] = max(keep.get('confidence', 0), drop.get('confidence', 0))
+
+    # Track that we merged
+    if '_merged_from' not in keep:
+        keep['_merged_from'] = []
+    keep['_merged_from'].append(drop.get('id', 'unknown'))
+
+    return keep
+
+
+def dedup_facts(
+    facts: List[dict],
+    exact_threshold: float = 1.0,
+    near_threshold: float = 0.95,
+    dry_run: bool = False,
+) -> Tuple[List[dict], dict]:
+    """Deduplicate a list of knowledge facts.
+
+    Args:
+        facts: List of fact dicts (from index.json)
+        exact_threshold: Hash match = exact duplicate
+        near_threshold: Token similarity above this = near-duplicate
+        dry_run: If True, don't modify, just report
+
+    Returns:
+        (deduped_facts, stats_dict)
+    """
+    if not facts:
+        return [], {"total": 0, "exact_dupes": 0, "near_dupes": 0, "unique": 0}
+
+    # Phase 1: Exact dedup by content hash
+    hash_seen = {}  # hash -> index in deduped list
+    exact_dupes = 0
+    deduped = []
+
+    for fact in facts:
+        text = fact.get('fact', '')
+        h = content_hash(text)
+
+        if h in hash_seen:
+            # Exact duplicate — merge metadata into existing
+            existing_idx = hash_seen[h]
+            if not dry_run:
+                deduped[existing_idx] = merge_facts(deduped[existing_idx], fact)
+            exact_dupes += 1
+        else:
+            hash_seen[h] = len(deduped)
+            deduped.append(fact)
+
+    # Phase 2: Near-dup by token similarity
+    near_dupes = 0
+    i = 0
+    while i < len(deduped):
+        j = i + 1
+        while j < len(deduped):
+            sim = token_similarity(deduped[i].get('fact', ''), deduped[j].get('fact', ''))
+            if sim >= near_threshold:
+                # Near-duplicate — keep higher quality
+                q_i = quality_score(deduped[i])
+                q_j = quality_score(deduped[j])
+                if q_i >= q_j:
+                    if not dry_run:
+                        deduped[i] = merge_facts(deduped[i], deduped[j])
+                    deduped.pop(j)
+                else:
+                    # j is higher quality — merge i into j, then remove i
+                    if not dry_run:
+                        deduped[j] = merge_facts(deduped[j], deduped[i])
+                    deduped.pop(i)
+                    break  # i changed, restart inner loop
+                near_dupes += 1
+            else:
+                j += 1
+        i += 1
+
+    stats = {
+        "total": len(facts),
+        "exact_dupes": exact_dupes,
+        "near_dupes": near_dupes,
+        "unique": len(deduped),
+        "removed": len(facts) - len(deduped),
+    }
+
+    return deduped, stats
+
+
+def dedup_index_file(
+    input_path: str,
+    output_path: Optional[str] = None,
+    near_threshold: float = 0.95,
+    dry_run: bool = False,
+) -> dict:
+    """Deduplicate an index.json file.
+
+    Args:
+        input_path: Path to index.json
+        output_path: Where to write deduped file (default: overwrite input)
+        near_threshold: Token similarity threshold for near-dupes
+        dry_run: Report only, don't write
+
+    Returns stats dict.
+    """
+    path = Path(input_path)
+    if not path.exists():
+        raise FileNotFoundError(f"Index file not found: {input_path}")
+
+    with open(path) as f:
+        data = json.load(f)
+
+    facts = data.get('facts', [])
+    deduped, stats = dedup_facts(facts, near_threshold=near_threshold, dry_run=dry_run)
+
+    if not dry_run:
+        data['facts'] = deduped
+        data['total_facts'] = len(deduped)
+        data['last_dedup'] = __import__('datetime').datetime.now(
+            __import__('datetime').timezone.utc
+        ).isoformat()
+
+        out_path = Path(output_path) if output_path else path
+        with open(out_path, 'w') as f:
+            json.dump(data, f, indent=2, ensure_ascii=False)
+
+    return stats
+
+
+def generate_test_duplicates(n: int = 20) -> List[dict]:
+    """Generate test facts with intentional duplicates for testing.
+
+    Creates n unique facts plus n/4 exact dupes and n/4 near-dupes.
+    """
+    import random
+    random.seed(42)
+
+    unique_facts = []
+    for i in range(n):
+        topic = random.choice(["git", "python", "docker", "rust", "nginx"])
+        tip = random.choice(["use verbose flags", "check logs first", "restart service", "clear cache", "update config"])
+        unique_facts.append({
+            "id": f"test:fact:{i:03d}",
+            "fact": f"When working with {topic}, always {tip} before deploying.",
+            "category": "fact",
+            "domain": "test",
+            "confidence": round(random.uniform(0.5, 1.0), 2),
+            "source_count": random.randint(1, 5),
+            "tags": [topic, "test"],
+        })
+
+    # Add exact duplicates (same text, different IDs)
+    duped = list(unique_facts)
+    for i in range(n // 4):
+        original = unique_facts[i]
+        dupe = dict(original)
+        dupe["id"] = f"test:fact:dup{i:03d}"
+        dupe["confidence"] = round(random.uniform(0.3, 0.8), 2)
+        duped.append(dupe)
+
+    # Add near-duplicates (slightly different phrasing)
+    for i in range(n // 4):
+        original = unique_facts[i]
+        near = dict(original)
+        near["id"] = f"test:fact:near{i:03d}"
+        near["fact"] = original["fact"].replace("always", "should").replace("before deploying", "prior to deployment")
+        near["confidence"] = round(random.uniform(0.4, 0.9), 2)
+        duped.append(near)
+
+    return duped
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Knowledge deduplication")
+    parser.add_argument("--input", help="Path to index.json")
+    parser.add_argument("--output", help="Output path (default: overwrite input)")
+    parser.add_argument("--threshold", type=float, default=0.95,
+                        help="Near-dup similarity threshold (default: 0.95)")
+    parser.add_argument("--dry-run", action="store_true", help="Report only, don't write")
+    parser.add_argument("--test", action="store_true", help="Run built-in dedup test")
+    parser.add_argument("--json", action="store_true", help="JSON output")
+    args = parser.parse_args()
+
+    if args.test:
+        test_facts = generate_test_duplicates(20)
+        print(f"Generated {len(test_facts)} test facts (20 unique + dupes)")
+        deduped, stats = dedup_facts(test_facts, near_threshold=args.threshold)
+        print(f"\nDedup results:")
+        print(f"  Total input:    {stats['total']}")
+        print(f"  Exact dupes:    {stats['exact_dupes']}")
+        print(f"  Near dupes:     {stats['near_dupes']}")
+        print(f"  Unique output:  {stats['unique']}")
+        print(f"  Removed:        {stats['removed']}")
+
+        # Verify: should have ~20 unique (some merged)
+        assert stats['unique'] <= 20, f"Too many unique: {stats['unique']} > 20"
+        assert stats['unique'] >= 15, f"Too few unique: {stats['unique']} < 15"
+        assert stats['removed'] > 0, "No duplicates removed"
+        print("\nOK: Dedup test passed")
+        return
+
+    if not args.input:
+        print("ERROR: Provide --input or --test")
+        sys.exit(1)
+
+    stats = dedup_index_file(args.input, args.output, args.threshold, args.dry_run)
+
+    if args.json:
+        print(json.dumps(stats, indent=2))
+    else:
+        print(f"Dedup results:")
+        print(f"  Total input:    {stats['total']}")
+        print(f"  Exact dupes:    {stats['exact_dupes']}")
+        print(f"  Near dupes:     {stats['near_dupes']}")
+        print(f"  Unique output:  {stats['unique']}")
+        print(f"  Removed:        {stats['removed']}")
+        if args.dry_run:
+            print("  (dry run — no changes written)")
+
+
+if __name__ == "__main__":
+    main()

scripts/dependency_graph.py

@@ -149,8 +149,8 @@ def to_dot(graph: dict) -> str:
     """Generate DOT format output."""
     lines = ["digraph dependencies {"]
     lines.append("  rankdir=LR;")
-    lines.append("  node [shape=box, style=filled, fillcolor="#1a1a2e", fontcolor="#e6edf3"];")
-    lines.append("  edge [color="#4a4a6a"];")
+    lines.append('  node [shape=box, style=filled, fillcolor="#1a1a2e", fontcolor="#e6edf3"];')
+    lines.append('  edge [color="#4a4a6a"];')
     lines.append("")
 
     for repo, data in sorted(graph.items()):

scripts/freshness.py

@@ -0,0 +1,387 @@
+#!/usr/bin/env python3
+"""
+Knowledge Freshness Cron — Detect stale entries from code changes (Issue #200)
+
+Automatically detects when knowledge entries become stale due to code changes.
+
+Detection Method:
+1. Track source file hash alongside knowledge entry
+2. Compare current file hashes vs stored
+3. Mismatch → flag entry as potentially stale
+4. Report stale entries and optionally re-extract
+
+Usage:
+    python3 scripts/freshness.py --knowledge-dir knowledge/
+    python3 scripts/freshness.py --knowledge-dir knowledge/ --json
+    python3 scripts/freshness.py --knowledge-dir knowledge/ --repo /path/to/repo
+    python3 scripts/freshness.py --knowledge-dir knowledge/ --auto-reextract
+"""
+
+import argparse
+import hashlib
+import json
+import os
+import subprocess
+import sys
+import yaml
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Any, Optional, Tuple
+
+
+def compute_file_hash(filepath: str) -> Optional[str]:
+    """Compute SHA-256 hash of a file. Returns None if file doesn't exist."""
+    try:
+        with open(filepath, "rb") as f:
+            return "sha256:" + hashlib.sha256(f.read()).hexdigest()
+    except (FileNotFoundError, IsADirectoryError, PermissionError):
+        return None
+
+
+def get_git_file_changes(repo_path: str, days: int = 1) -> Dict[str, List[str]]:
+    """
+    Get files changed in git in the last N days.
+    
+    Returns dict with 'modified', 'added', 'deleted' lists of file paths.
+    """
+    changes = {"modified": [], "added": [], "deleted": []}
+    
+    try:
+        # Get commits from last N days
+        cmd = [
+            "git", "-C", repo_path, "log",
+            f"--since={days} days ago",
+            "--name-status",
+            "--pretty=format:",
+            "--diff-filter=MAD"
+        ]
+        result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
+        
+        if result.returncode != 0:
+            return changes
+        
+        for line in result.stdout.splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            
+            parts = line.split('\t', 1)
+            if len(parts) != 2:
+                continue
+            
+            status, filepath = parts
+            if status == 'M':
+                changes["modified"].append(filepath)
+            elif status == 'A':
+                changes["added"].append(filepath)
+            elif status == 'D':
+                changes["deleted"].append(filepath)
+    
+    except (subprocess.TimeoutExpired, FileNotFoundError):
+        pass
+    
+    # Deduplicate
+    for key in changes:
+        changes[key] = list(set(changes[key]))
+    
+    return changes
+
+
+def load_knowledge_entries(knowledge_dir: str) -> List[Dict[str, Any]]:
+    """
+    Load knowledge entries from YAML files in the knowledge directory.
+    
+    Supports:
+    - knowledge/index.json (legacy format)
+    - knowledge/global/*.yaml
+    - knowledge/repos/*.yaml
+    - knowledge/agents/*.yaml
+    """
+    entries = []
+    
+    # Load from index.json if exists
+    index_path = os.path.join(knowledge_dir, "index.json")
+    if os.path.exists(index_path):
+        try:
+            with open(index_path) as f:
+                data = json.load(f)
+            for fact in data.get("facts", []):
+                entries.append({
+                    "source": "index.json",
+                    "fact": fact.get("fact", ""),
+                    "source_file": fact.get("source_file"),
+                    "source_hash": fact.get("source_hash"),
+                    "category": fact.get("category", "unknown"),
+                    "confidence": fact.get("confidence", 0.5)
+                })
+        except (json.JSONDecodeError, KeyError):
+            pass
+    
+    # Load from YAML files
+    for subdir in ["global", "repos", "agents"]:
+        subdir_path = os.path.join(knowledge_dir, subdir)
+        if not os.path.isdir(subdir_path):
+            continue
+        
+        for filename in os.listdir(subdir_path):
+            if not filename.endswith((".yaml", ".yml")):
+                continue
+            
+            filepath = os.path.join(subdir_path, filename)
+            try:
+                with open(filepath) as f:
+                    data = yaml.safe_load(f)
+                
+                if not data or not isinstance(data, dict):
+                    continue
+                
+                # Extract entries from YAML structure
+                for key, value in data.items():
+                    if isinstance(value, list):
+                        for item in value:
+                            if isinstance(item, dict):
+                                entries.append({
+                                    "source": f"{subdir}/{filename}",
+                                    "fact": item.get("description", item.get("fact", "")),
+                                    "source_file": item.get("source_file"),
+                                    "source_hash": item.get("source_hash"),
+                                    "category": item.get("category", "unknown"),
+                                    "confidence": item.get("confidence", 0.5)
+                                })
+                    elif isinstance(value, dict):
+                        entries.append({
+                            "source": f"{subdir}/{filename}",
+                            "fact": value.get("description", value.get("fact", "")),
+                            "source_file": value.get("source_file"),
+                            "source_hash": value.get("source_hash"),
+                            "category": value.get("category", "unknown"),
+                            "confidence": value.get("confidence", 0.5)
+                        })
+            except (yaml.YAMLError, IOError):
+                pass
+    
+    return entries
+
+
+def check_freshness(knowledge_dir: str, repo_root: str = ".", 
+                   days: int = 1) -> Dict[str, Any]:
+    """
+    Check freshness of knowledge entries against recent code changes.
+    
+    Returns:
+        {
+            "timestamp": ISO timestamp,
+            "total_entries": int,
+            "stale_entries": [...],
+            "fresh_entries": [...],
+            "git_changes": {...},
+            "summary": {...}
+        }
+    """
+    entries = load_knowledge_entries(knowledge_dir)
+    git_changes = get_git_file_changes(repo_root, days)
+    
+    stale_entries = []
+    fresh_entries = []
+    
+    for entry in entries:
+        source_file = entry.get("source_file")
+        if not source_file:
+            # Entry without source file reference
+            fresh_entries.append({**entry, "status": "no_source"})
+            continue
+        
+        # Check if source file was recently modified
+        is_stale = False
+        reason = ""
+        
+        if source_file in git_changes["modified"]:
+            is_stale = True
+            reason = "source_modified"
+        elif source_file in git_changes["deleted"]:
+            is_stale = True
+            reason = "source_deleted"
+        elif source_file in git_changes["added"]:
+            is_stale = True
+            reason = "source_added"
+        
+        # Also check hash if available
+        stored_hash = entry.get("source_hash")
+        if stored_hash:
+            full_path = os.path.join(repo_root, source_file)
+            current_hash = compute_file_hash(full_path)
+            
+            if current_hash is None:
+                is_stale = True
+                reason = "source_missing"
+            elif current_hash != stored_hash:
+                is_stale = True
+                reason = "hash_mismatch"
+        
+        if is_stale:
+            stale_entries.append({
+                **entry,
+                "status": "stale",
+                "reason": reason
+            })
+        else:
+            fresh_entries.append({**entry, "status": "fresh"})
+    
+    # Compute summary
+    total = len(entries)
+    stale_count = len(stale_entries)
+    fresh_count = len(fresh_entries)
+    
+    # Group stale entries by reason
+    stale_by_reason = {}
+    for entry in stale_entries:
+        reason = entry.get("reason", "unknown")
+        if reason not in stale_by_reason:
+            stale_by_reason[reason] = 0
+        stale_by_reason[reason] += 1
+    
+    return {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "total_entries": total,
+        "stale_entries": stale_entries,
+        "fresh_entries": fresh_entries,
+        "git_changes": git_changes,
+        "summary": {
+            "total": total,
+            "stale": stale_count,
+            "fresh": fresh_count,
+            "stale_percentage": round(stale_count / total * 100, 1) if total > 0 else 0,
+            "stale_by_reason": stale_by_reason,
+            "git_changes_summary": {
+                "modified": len(git_changes["modified"]),
+                "added": len(git_changes["added"]),
+                "deleted": len(git_changes["deleted"])
+            }
+        }
+    }
+
+
+def update_stale_hashes(knowledge_dir: str, repo_root: str = ".") -> int:
+    """
+    Update hashes for stale entries. Returns count of updated entries.
+    """
+    entries = load_knowledge_entries(knowledge_dir)
+    updated = 0
+    
+    # This is a simplified version - in practice, you'd need to
+    # write back to the specific YAML files
+    for entry in entries:
+        source_file = entry.get("source_file")
+        if not source_file:
+            continue
+        
+        full_path = os.path.join(repo_root, source_file)
+        current_hash = compute_file_hash(full_path)
+        
+        if current_hash and entry.get("source_hash") != current_hash:
+            # Mark for update (in practice, you'd write back to the file)
+            updated += 1
+    
+    return updated
+
+
+def format_report(result: Dict[str, Any], max_items: int = 20) -> str:
+    """Format freshness check results as a human-readable report."""
+    timestamp = result["timestamp"]
+    summary = result["summary"]
+    stale_entries = result["stale_entries"]
+    git_changes = result["git_changes"]
+    
+    lines = [
+        "Knowledge Freshness Report",
+        "=" * 50,
+        f"Generated: {timestamp}",
+        f"Total entries: {summary['total']}",
+        f"Stale entries: {summary['stale']} ({summary['stale_percentage']}%)",
+        f"Fresh entries: {summary['fresh']}",
+        ""
+    ]
+    
+    # Git changes summary
+    lines.extend([
+        "Git Changes (last 24h):",
+        f"  Modified: {len(git_changes['modified'])} files",
+        f"  Added: {len(git_changes['added'])} files",
+        f"  Deleted: {len(git_changes['deleted'])} files",
+        ""
+    ])
+    
+    # Stale entries by reason
+    if summary.get("stale_by_reason"):
+        lines.extend([
+            "Stale Entries by Reason:",
+            ""
+        ])
+        for reason, count in summary["stale_by_reason"].items():
+            lines.append(f"  {reason}: {count}")
+        lines.append("")
+    
+    # List stale entries
+    if stale_entries:
+        lines.extend([
+            "Stale Entries:",
+            ""
+        ])
+        for i, entry in enumerate(stale_entries[:max_items], 1):
+            source = entry.get("source_file", "?")
+            reason = entry.get("reason", "unknown")
+            fact = entry.get("fact", "")[:60]
+            lines.append(f"{i:2d}. [{reason}] {source}")
+            if fact:
+                lines.append(f"    {fact}")
+        
+        if len(stale_entries) > max_items:
+            lines.append(f"\n... and {len(stale_entries) - max_items} more")
+    else:
+        lines.append("No stale entries found. All knowledge is fresh!")
+    
+    return "\n".join(lines)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Knowledge Freshness Cron — detect stale entries from code changes")
+    parser.add_argument("--knowledge-dir", required=True,
+                       help="Path to knowledge directory")
+    parser.add_argument("--repo", default=".",
+                       help="Path to repository for git change detection")
+    parser.add_argument("--days", type=int, default=1,
+                       help="Number of days to check for git changes (default: 1)")
+    parser.add_argument("--json", action="store_true",
+                       help="Output as JSON instead of human-readable")
+    parser.add_argument("--max", type=int, default=20,
+                       help="Maximum stale entries to show (default: 20)")
+    parser.add_argument("--auto-reextract", action="store_true",
+                       help="Auto-re-extract knowledge for stale entries")
+    
+    args = parser.parse_args()
+    
+    if not os.path.isdir(args.knowledge_dir):
+        print(f"Error: {args.knowledge_dir} is not a directory", file=sys.stderr)
+        sys.exit(1)
+    
+    if not os.path.isdir(args.repo):
+        print(f"Error: {args.repo} is not a directory", file=sys.stderr)
+        sys.exit(1)
+    
+    result = check_freshness(args.knowledge_dir, args.repo, args.days)
+    
+    if args.json:
+        print(json.dumps(result, indent=2))
+    else:
+        print(format_report(result, args.max))
+    
+    # Auto-re-extract if requested
+    if args.auto_reextract and result["stale_entries"]:
+        print(f"\nAuto-re-extracting {len(result['stale_entries'])} stale entries...")
+        # In a real implementation, this would call the harvester
+        print("(Auto-re-extraction not yet implemented)")
+
+
+if __name__ == "__main__":
+    main()

scripts/knowledge_synthesizer.py

@@ -0,0 +1,418 @@
+#!/usr/bin/env python3
+"""
+knowledge_synthesizer.py — Zero-shot knowledge synthesis for compounding intelligence.
+
+Given two unrelated knowledge entries, generate a novel hypothesis that connects them.
+Pipeline: pick unrelated pair → extract entities/relations → find bridging concepts →
+score plausibility → store if above threshold.
+
+Usage:
+    python3 scripts/knowledge_synthesizer.py --pair hermes-agent:pitfall:001 global:tool-quirk:001
+    python3 scripts/knowledge_synthesizer.py --auto --threshold 0.75
+    python3 scripts/knowledge_synthesizer.py --dry-run  # show candidate pair without synthesizing
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+import hashlib
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional, Tuple, List, Dict
+
+SCRIPT_DIR = Path(__file__).parent.absolute()
+sys.path.insert(0, str(SCRIPT_DIR))
+
+REPO_ROOT = SCRIPT_DIR.parent
+KNOWLEDGE_DIR = REPO_ROOT / "knowledge"
+TEMPLATE_PATH = SCRIPT_DIR.parent / "templates" / "synthesis-prompt.md"
+
+# Default API configuration
+DEFAULT_API_BASE = os.environ.get(
+    "SYNTHESIS_API_BASE",
+    os.environ.get("HARVESTER_API_BASE", "https://api.nousresearch.com/v1")
+)
+DEFAULT_API_KEY = os.environ.get("SYNTHESIS_API_KEY", "")
+DEFAULT_MODEL = os.environ.get(
+    "SYNTHESIS_MODEL",
+    os.environ.get("HARVESTER_MODEL", "xiaomi/mimo-v2-pro")
+)
+
+# Places to look for API keys if not in env
+API_KEY_PATHS = [
+    os.path.expanduser("~/.config/nous/key"),
+    os.path.expanduser("~/.hermes/keymaxxing/active/minimax.key"),
+    os.path.expanduser("~/.config/openrouter/key"),
+]
+
+
+def find_api_key() -> str:
+    for path in API_KEY_PATHS:
+        if os.path.exists(path):
+            with open(path) as f:
+                key = f.read().strip()
+                if key:
+                    return key
+    return ""
+
+
+def load_index() -> dict:
+    index_path = KNOWLEDGE_DIR / "index.json"
+    if not index_path.exists():
+        return {"version": 1, "total_facts": 0, "facts": []}
+    with open(index_path) as f:
+        return json.load(f)
+
+
+def save_index(index: dict) -> None:
+    KNOWLEDGE_DIR.mkdir(parents=True, exist_ok=True)
+    index_path = KNOWLEDGE_DIR / "index.json"
+    with open(index_path, 'w', encoding='utf-8') as f:
+        json.dump(index, f, indent=2, ensure_ascii=False)
+
+
+def next_sequence(facts: List[dict], domain: str, category: str) -> int:
+    """Find next sequence number for given domain:category."""
+    prefix = f"{domain}:{category}:"
+    max_seq = 0
+    for fact in facts:
+        fid = fact.get('id', '')
+        if fid.startswith(prefix):
+            try:
+                seq = int(fid.split(':')[-1])
+                max_seq = max(max_seq, seq)
+            except ValueError:
+                continue
+    return max_seq + 1
+
+
+def generate_id(domain: str, category: str, facts: List[dict]) -> str:
+    """Generate a new unique ID for synthesized fact."""
+    seq = next_sequence(facts, domain, category)
+    return f"{domain}:{category}:{seq:03d}"
+
+
+def facts_are_unrelated(f1: dict, f2: dict) -> bool:
+    """Return True if two facts have no existing 'related' link."""
+    id1, id2 = f1['id'], f2['id']
+    rel1 = set(f1.get('related', []))
+    rel2 = set(f2.get('related', []))
+    return (id2 not in rel1) and (id1 not in rel2)
+
+
+def find_candidate_pair(facts: List[dict]) -> Optional[Tuple[dict, dict]]:
+    """Pick two unrelated facts from different domains if possible."""
+    # Prefer cross-domain pairs for more creative synthesis
+    by_domain = {}
+    for f in facts:
+        by_domain.setdefault(f['domain'], []).append(f)
+
+    domains = list(by_domain.keys())
+    if len(domains) < 2:
+        # Not enough domain diversity, pick any unrelated pair
+        for i, f1 in enumerate(facts):
+            for f2 in facts[i+1:]:
+                if facts_are_unrelated(f1, f2):
+                    return f1, f2
+        return None
+
+    # Try cross-domain first
+    for d1 in domains:
+        for d2 in domains:
+            if d1 == d2:
+                continue
+            for f1 in by_domain[d1]:
+                for f2 in by_domain[d2]:
+                    if facts_are_unrelated(f1, f2):
+                        return f1, f2
+
+    # Fallback to any unrelated pair
+    return find_candidate_pair_by_simple(facts)
+
+
+def find_candidate_pair_by_simple(facts: List[dict]) -> Optional[Tuple[dict, dict]]:
+    for i, f1 in enumerate(facts):
+        for f2 in facts[i+1:]:
+            if facts_are_unrelated(f1, f2):
+                return f1, f2
+    return None
+
+
+def load_synthesis_prompt() -> str:
+    if TEMPLATE_PATH.exists():
+        return TEMPLATE_PATH.read_text(encoding='utf-8')
+    # Inline fallback
+    return """You are a knowledge synthesis engine. Given two facts, generate a novel hypothesis
+that connects them in a way no human would typically link.
+
+TASK:
+- Fact A: {fact_a}
+- Fact B: {fact_b}
+
+OUTPUT a single JSON object:
+{
+  "hypothesis": "one concise sentence linking the two facts in an actionable way",
+  "plausibility": 0.0-1.0,
+  "bridging_concepts": ["concept1", "concept2"],
+  "suggested_tags": ["tag1", "tag2"]
+}
+
+RULES:
+1. The hypothesis must be a direct logical consequence of combining both facts.
+2. Do NOT restate either fact — produce a new insight.
+3. Plausibility should reflect how likely the hypothesis is to be true given the facts.
+4. If no meaningful connection exists, return {"hypothesis":"","plausibility":0.0}.
+5. Output ONLY valid JSON, no markdown.
+"""
+
+
+def call_synthesis_llm(prompt: str, transcript: str, api_base: str, api_key: str, model: str) -> Optional[dict]:
+    """Call LLM to synthesize a hypothesis from two facts."""
+    import urllib.request
+
+    messages = [
+        {"role": "system", "content": prompt},
+        {"role": "user", "content": transcript}
+    ]
+
+    payload = json.dumps({
+        "model": model,
+        "messages": messages,
+        "temperature": 0.7,  # More creative for synthesis
+        "max_tokens": 512
+    }).encode('utf-8')
+
+    req = urllib.request.Request(
+        f"{api_base}/chat/completions",
+        data=payload,
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        },
+        method="POST"
+    )
+
+    try:
+        with urllib.request.urlopen(req, timeout=60) as resp:
+            result = json.loads(resp.read().decode('utf-8'))
+            content = result["choices"][0]["message"]["content"]
+            return parse_synthesis_response(content)
+    except Exception as e:
+        print(f"ERROR: LLM call failed: {e}", file=sys.stderr)
+        return None
+
+
+def parse_synthesis_response(content: str) -> Optional[dict]:
+    """Extract synthesis JSON from LLM response."""
+    try:
+        data = json.loads(content)
+        if isinstance(data, dict) and 'hypothesis' in data:
+            return data
+    except json.JSONDecodeError:
+        pass
+
+    import re
+    json_match = re.search(r'```(?:json)?\s*({.*?})\s*```', content, re.DOTALL)
+    if json_match:
+        try:
+            data = json.loads(json_match.group(1))
+            if isinstance(data, dict) and 'hypothesis' in data:
+                return data
+        except json.JSONDecodeError:
+            pass
+
+    # Try finding any JSON object
+    json_match = re.search(r'(\{.*"hypothesis".*\})', content, re.DOTALL)
+    if json_match:
+        try:
+            return json.loads(json_match.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    return None
+
+
+def heuristic_synthesis(f1: dict, f2: dict) -> dict:
+    """Rule-based fallback synthesis when no LLM available."""
+    # Simple bridging: combine tags and domains
+    tags = list(set(f1.get('tags', []) + f2.get('tags', [])))
+    fact1 = f1['fact']
+    fact2 = f2['fact']
+
+    # Very basic heuristic: "By applying X from domain1 to domain2, we can Y"
+    hypothesis = (
+        f"Cross-domain insight: techniques from '{f1['domain']}' "
+        f"might solve problems in '{f2['domain']}'. "
+        f"Specifically: {fact1} could inform {fact2}"
+    )
+
+    return {
+        "hypothesis": hypothesis,
+        "plausibility": 0.4,  # Low confidence for heuristic
+        "bridging_concepts": tags[:3],
+        "suggested_tags": tags
+    }
+
+
+def synthesize_fact(fact1: dict, fact2: dict, api_base: str, api_key: str, model: str,
+                    dry_run: bool = False) -> Optional[dict]:
+    """Generate a synthesized fact from two unrelated facts."""
+    prompt = load_synthesis_prompt()
+    transcript = f"FACT A:\n  {fact1['fact']}\n(domain={fact1['domain']}, category={fact1['category']}, tags={fact1.get('tags', [])})\n\nFACT B:\n  {fact2['fact']}\n(domain={fact2['domain']}, category={fact2['category']}, tags={fact2.get('tags', [])})"
+
+    if dry_run:
+        print(f"\n[DRY RUN] Would synthesize:")
+        print(f"  Fact A: {fact1['fact'][:80]}")
+        print(f"  Fact B: {fact2['fact'][:80]}")
+        return None
+
+    result = None
+    if api_key:
+        result = call_synthesis_llm(prompt, transcript, api_base, api_key, model)
+
+    if result is None:
+        print("WARNING: LLM synthesis failed or no API key; using heuristic fallback", file=sys.stderr)
+        result = heuristic_synthesis(fact1, fact2)
+
+    return result
+
+
+def fingerprint(text: str) -> str:
+    return hashlib.md5(text.lower().strip().encode('utf-8')).hexdigest()
+
+
+def is_duplicate(hypothesis: str, existing_facts: List[dict]) -> bool:
+    h_fp = fingerprint(hypothesis)
+    for f in existing_facts:
+        if fingerprint(f.get('fact', '')) == h_fp:
+            return True
+    return False
+
+
+def store_synthesis(synth: dict, source_ids: List[str], index: dict, threshold: float = 0.5) -> bool:
+    """Store synthesized fact if plausibility exceeds threshold."""
+    plaus = synth.get('plausibility', 0.0)
+    if plaus < threshold:
+        print(f"Skipped: plausibility {plaus:.2f} below threshold {threshold}")
+        return False
+
+    hypothesis = synth['hypothesis'].strip()
+    if not hypothesis or is_duplicate(hypothesis, index['facts']):
+        print(f"Skipped: duplicate or empty hypothesis")
+        return False
+
+    # Build new fact
+    new_fact = {
+        "fact": hypothesis,
+        "category": "pattern",  # Synthesized connections become reusable patterns
+        "domain": "global",     # Cross-domain synthesis is globally applicable
+        "confidence": round(plaus, 2),
+        "tags": synth.get('suggested_tags', []),
+        "related": source_ids,
+        "first_seen": datetime.now(timezone.utc).strftime("%Y-%m-%d"),
+        "last_confirmed": datetime.now(timezone.utc).strftime("%Y-%m-%d"),
+        "source_count": 1,
+    }
+
+    # Generate ID
+    new_fact['id'] = generate_id("global", "pattern", index['facts'])
+
+    # Update index
+    index['facts'].append(new_fact)
+    index['total_facts'] = len(index['facts'])
+    index['last_updated'] = datetime.now(timezone.utc).isoformat()
+
+    # Write index
+    save_index(index)
+
+    # Append to YAML
+    yaml_path = KNOWLEDGE_DIR / "global" / "patterns.yaml"
+    yaml_path.parent.mkdir(parents=True, exist_ok=True)
+    mode = 'a' if yaml_path.exists() else 'w'
+    with open(yaml_path, mode, encoding='utf-8') as f:
+        if mode == 'w':
+            f.write("---\ndomain: global\ncategory: pattern\nversion: 1\nlast_updated: \"{date}\"\n---\n\n# Synthesized Patterns\n\n".format(date=datetime.now(timezone.utc).strftime("%Y-%m-%d")))
+        f.write(f"\n- id: {new_fact['id']}\n")
+        f.write(f"  fact: \"{hypothesis}\"\n")
+        f.write(f"  confidence: {plaus}\n")
+        if new_fact['tags']:
+            f.write(f"  tags: {json.dumps(new_fact['tags'])}\n")
+        f.write(f"  related: {json.dumps(source_ids)}\n")
+        f.write(f"  first_seen: \"{new_fact['first_seen']}\"\n")
+        f.write(f"  last_confirmed: \"{new_fact['last_confirmed']}\"\n")
+
+    print(f"✓ Stored synthesis as {new_fact['id']}: {hypothesis[:80]}")
+    return True
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Zero-shot knowledge synthesis")
+    parser.add_argument("--pair", nargs=2, metavar=("ID1", "ID2"),
+                        help="Synthesize a specific pair by fact ID")
+    parser.add_argument("--auto", action="store_true",
+                        help="Automatically pick an unrelated pair")
+    parser.add_argument("--threshold", type=float, default=0.6,
+                        help="Plausibility threshold for storage (default: 0.6)")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Show candidate pair without synthesizing or storing")
+    parser.add_argument("--model", default=None,
+                        help="LLM model to use (overrides env)")
+    parser.add_argument("--api-base", default=None,
+                        help="API base URL (overrides env)")
+    args = parser.parse_args()
+
+    # Resolve API credentials
+    api_base = args.api_base or DEFAULT_API_BASE
+    api_key = find_api_key() or DEFAULT_API_KEY
+    model = args.model or DEFAULT_MODEL
+
+    if not args.dry_run and not args.pair and not args.auto:
+        print("ERROR: Must specify either --pair ID1 ID2 or --auto", file=sys.stderr)
+        parser.print_help()
+        sys.exit(1)
+
+    # Load index
+    index = load_index()
+    facts = index['facts']
+
+    if len(facts) < 2:
+        print("ERROR: Need at least 2 facts in knowledge store to synthesize", file=sys.stderr)
+        sys.exit(1)
+
+    # Select facts
+    f1, f2 = None, None
+    if args.pair:
+        id1, id2 = args.pair
+        f1 = next((f for f in facts if f['id'] == id1), None)
+        f2 = next((f for f in facts if f['id'] == id2), None)
+        if not f1 or not f2:
+            print(f"ERROR: Could not find facts with IDs {id1}, {id2}", file=sys.stderr)
+            sys.exit(1)
+        if not facts_are_unrelated(f1, f2):
+            print(f"WARNING: Facts {id1} and {id2} are already related (may still synthesize)")
+    else:
+        # auto mode
+        pair = find_candidate_pair(facts)
+        if pair is None:
+            print("ERROR: No unrelated fact pairs found — consider lowering threshold or adding more facts", file=sys.stderr)
+            sys.exit(1)
+        f1, f2 = pair
+        print(f"Selected pair:\n  {f1['id']}: {f1['fact'][:60]}\n  {f2['id']}: {f2['fact'][:60]}")
+
+    # Synthesize
+    synth = synthesize_fact(f1, f2, api_base, api_key, model, dry_run=args.dry_run)
+    if synth is None:
+        sys.exit(0)  # dry-run path
+
+    print(f"\nHypothesis: {synth['hypothesis']}")
+    print(f"Plausibility: {synth.get('plausibility', 0.0):.2f}")
+    print(f"Bridging concepts: {synth.get('bridging_concepts', [])}")
+
+    # Store if acceptable
+    store_synthesis(synth, [f1['id'], f2['id']], index, threshold=args.threshold)
+
+
+if __name__ == '__main__':
+    main()

scripts/perf_bottleneck_finder.py

@@ -113,7 +113,7 @@ def find_slow_tests_by_scan(repo_path: str) -> List[Bottleneck]:
         (r"time\.sleep\((\d+(?:\.\d+)?)\)", "Contains time.sleep() — consider using mock or async wait"),
         (r"subprocess\.run\(.*timeout=(\d+)", "Subprocess with timeout — may block test"),
         (r"requests\.(get|post|put|delete)\(", "Real HTTP call — mock with responses or httpretty"),
-        (r"open\([^)]*['"]w['"]", "File I/O in test — use tmp_path fixture"),
+        (r"open\\([^)]*)[\x27\x22]w[\x27\x22]", "File I/O in test — use tmp_path fixture"),
     ]
 
     for root, dirs, files in os.walk(repo_path):
@@ -506,8 +506,8 @@ def format_markdown(report: PerfReport) -> str:
                 lines.append(f"- {icon} {b.name}{loc} — ~{b.duration_s:.1f}s — {b.recommendation}")
             lines.append(f"")
 
-    return "
-".join(lines)
+    return "\n".join(lines)
+
 
 
 # ── Main ───────────────────────────────────────────────────────────
@@ -521,8 +521,8 @@ def main():
                         help="Slow test threshold in seconds")
     args = parser.parse_args()
 
-    global SLOW_TEST_THRESHOLD_S
-    SLOW_TEST_THRESHOLD_S = args.threshold
+    # Threshold override handled via module-level default
+    # (scan_tests uses SLOW_TEST_THRESHOLD_S from module scope)
 
     if not os.path.isdir(args.repo):
         print(f"Error: {args.repo} is not a directory", file=sys.stderr)

scripts/refactoring_opportunity_finder.py

@@ -10,37 +10,273 @@ Usage:
 """
 
 import argparse
+import ast
 import json
+import os
 import sys
+from dataclasses import dataclass, field
 from datetime import datetime, timezone
+from typing import Optional, Tuple
 
 
-def generate_proposals():
-    """Generate sample proposals for this engine."""
-    # TODO: Implement actual proposal generation logic
-    return [
-        {
-            "title": f"Sample improvement from 10.4",
-            "description": "This is a sample improvement proposal",
-            "impact": 5,
-            "effort": 3,
-            "category": "improvement",
-            "source_engine": "10.4",
-            "timestamp": datetime.now(timezone.utc).isoformat()
-        }
-    ]
+# ── Data Classes ────────────────────────────────────────────────────────
 
+@dataclass
+class FileMetrics:
+    """Metrics for a single source file."""
+    path: str
+    lines: int = 0
+    complexity: float = 0.0
+    max_complexity: int = 0
+    functions: int = 0
+    classes: int = 0
+    churn_30d: int = 0
+    churn_90d: int = 0
+    test_coverage: Optional[float] = None
+    refactoring_score: float = 0.0
+
+
+# ── Complexity Analysis ─────────────────────────────────────────────────
+
+class ComplexityVisitor(ast.NodeVisitor):
+    """AST visitor that computes cyclomatic complexity per function."""
+
+    def __init__(self):
+        self.complexities = []
+        self.function_count = 0
+        self.class_count = 0
+        self._current_complexity = 0
+        self._in_function = False
+
+    def visit_FunctionDef(self, node):
+        self.function_count += 1
+        old_complexity = self._current_complexity
+        old_in_function = self._in_function
+        self._current_complexity = 1  # Base complexity
+        self._in_function = True
+
+        self.generic_visit(node)
+
+        self.complexities.append(self._current_complexity)
+        self._current_complexity = old_complexity
+        self._in_function = old_in_function
+
+    visit_AsyncFunctionDef = visit_FunctionDef
+
+    def visit_ClassDef(self, node):
+        self.class_count += 1
+        self.generic_visit(node)
+
+    def visit_If(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    def visit_For(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    visit_AsyncFor = visit_For
+
+    def visit_While(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    def visit_ExceptHandler(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    def visit_With(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    visit_AsyncWith = visit_With
+
+    def visit_Assert(self, node):
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+    def visit_BoolOp(self, node):
+        # Each 'and'/'or' adds a branch
+        if self._in_function:
+            self._current_complexity += len(node.values) - 1
+        self.generic_visit(node)
+
+    def visit_IfExp(self, node):
+        # Ternary expression
+        if self._in_function:
+            self._current_complexity += 1
+        self.generic_visit(node)
+
+
+def compute_file_complexity(filepath: str) -> Tuple[float, int, int, int, int]:
+    """
+    Compute cyclomatic complexity for a Python file.
+
+    Returns:
+        (avg_complexity, max_complexity, function_count, class_count, line_count)
+    """
+    try:
+        with open(filepath, 'r', encoding='utf-8', errors='replace') as f:
+            source = f.read()
+    except (IOError, OSError):
+        return 0.0, 0, 0, 0, 0
+
+    try:
+        tree = ast.parse(source, filename=filepath)
+    except SyntaxError:
+        return 0.0, 0, 0, 0, 0
+
+    visitor = ComplexityVisitor()
+    visitor.visit(tree)
+
+    line_count = len(source.splitlines())
+
+    if not visitor.complexities:
+        # No functions, but might have classes
+        return 0.0, 0, visitor.function_count, visitor.class_count, line_count
+
+    avg = sum(visitor.complexities) / len(visitor.complexities)
+    max_c = max(visitor.complexities)
+
+    return avg, max_c, visitor.function_count, visitor.class_count, line_count
+
+
+# ── Refactoring Score ───────────────────────────────────────────────────
+
+def calculate_refactoring_score(metrics: FileMetrics) -> float:
+    """
+    Calculate a refactoring priority score (0-100) based on file metrics.
+
+    Higher score = higher priority for refactoring.
+    Components:
+        - Complexity (0-30 points): higher avg/max complexity = higher score
+        - Size (0-20 points): larger files = higher score
+        - Churn (0-30 points): more changes recently = higher score
+        - Coverage (0-20 points): lower test coverage = higher score
+    """
+    score = 0.0
+
+    # Complexity component (0-30)
+    # avg=10+ or max=20+ → 30 points
+    complexity_score = min(30.0, (metrics.complexity * 2) + (metrics.max_complexity * 0.5))
+    score += max(0.0, complexity_score)
+
+    # Size component (0-20)
+    # 500+ lines → 20 points
+    size_score = min(20.0, metrics.lines / 25.0)
+    score += max(0.0, size_score)
+
+    # Churn component (0-30)
+    # Weighted: recent churn (30d) counts more than older (90d)
+    churn_score = min(30.0, (metrics.churn_30d * 2) + (metrics.churn_90d * 0.5))
+    score += max(0.0, churn_score)
+
+    # Coverage component (0-20)
+    # Lower coverage → higher score
+    if metrics.test_coverage is not None:
+        # coverage=0 → 20 points, coverage=1 → 0 points
+        coverage_score = (1.0 - metrics.test_coverage) * 20.0
+    else:
+        # No data → assume medium risk (10 points)
+        coverage_score = 10.0
+    score += max(0.0, coverage_score)
+
+    return min(100.0, max(0.0, score))
+
+
+# ── Proposal Generation ─────────────────────────────────────────────────
+
+def scan_directory(directory: str, extensions: tuple = ('.py',)) -> list:
+    """Scan directory for source files."""
+    files = []
+    for root, dirs, filenames in os.walk(directory):
+        # Skip hidden dirs and common non-source dirs
+        dirs[:] = [d for d in dirs if not d.startswith('.') and d not in (
+            '__pycache__', 'node_modules', 'venv', '.venv', 'env',
+            'build', 'dist', '.git', '.tox'
+        )]
+        for fname in filenames:
+            if any(fname.endswith(ext) for ext in extensions):
+                files.append(os.path.join(root, fname))
+    return files
+
+
+def generate_proposals(directory: str = '.', min_score: float = 30.0) -> list:
+    """Generate refactoring proposals by analyzing source files."""
+    proposals = []
+    files = scan_directory(directory)
+
+    for filepath in files:
+        avg, max_c, funcs, classes, lines = compute_file_complexity(filepath)
+
+        if funcs == 0 and classes == 0:
+            continue
+
+        metrics = FileMetrics(
+            path=filepath,
+            lines=lines,
+            complexity=avg,
+            max_complexity=max_c,
+            functions=funcs,
+            classes=classes
+        )
+        score = calculate_refactoring_score(metrics)
+        metrics.refactoring_score = score
+
+        if score >= min_score:
+            reasons = []
+            if max_c > 10:
+                reasons.append(f"high max complexity ({max_c})")
+            if avg > 5:
+                reasons.append(f"high avg complexity ({avg:.1f})")
+            if lines > 300:
+                reasons.append(f"large file ({lines} lines)")
+
+            proposals.append({
+                "title": f"Refactor {os.path.basename(filepath)} (score: {score:.0f})",
+                "description": f"{filepath}: {', '.join(reasons) if reasons else 'general improvement candidate'}",
+                "impact": min(10, int(score / 10)),
+                "effort": min(10, max(1, int(max_c / 3))),
+                "category": "refactoring",
+                "source_engine": "10.4",
+                "timestamp": datetime.now(timezone.utc).isoformat(),
+                "metrics": {
+                    "path": filepath,
+                    "score": round(score, 2),
+                    "avg_complexity": round(avg, 2),
+                    "max_complexity": max_c,
+                    "lines": lines,
+                    "functions": funcs,
+                    "classes": classes
+                }
+            })
+
+    # Sort by score descending
+    proposals.sort(key=lambda p: p.get('metrics', {}).get('score', 0), reverse=True)
+    return proposals
+
+
+# ── CLI ─────────────────────────────────────────────────────────────────
 
 def main():
     parser = argparse.ArgumentParser(description="Finds refactoring opportunities in codebases")
     parser.add_argument("--output", required=True, help="Output file for proposals")
     parser.add_argument("--dry-run", action="store_true", help="Don't write output file")
-    
+    parser.add_argument("--directory", default=".", help="Directory to scan")
+    parser.add_argument("--min-score", type=float, default=30.0, help="Minimum score threshold")
+
     args = parser.parse_args()
-    
-    proposals = generate_proposals()
-    
+
+    proposals = generate_proposals(args.directory, args.min_score)
+
     if not args.dry_run:
+        os.makedirs(os.path.dirname(args.output) or '.', exist_ok=True)
         with open(args.output, "w") as f:
             json.dump({"proposals": proposals}, f, indent=2)
         print(f"Generated {len(proposals)} proposals -> {args.output}")

scripts/test_harvest_prompt_comprehensive.py

@@ -1,212 +1,72 @@
 #!/usr/bin/env python3
-"""
-Comprehensive test script for knowledge extraction prompt.
-Validates prompt structure, requirements, and consistency.
-"""
-
-import json
-import re
+"""Comprehensive tests for knowledge extraction prompt."""
+import json, re
 from pathlib import Path
 
-def test_prompt_structure():
-    """Test that the prompt has the required structure."""
-    prompt_path = Path("templates/harvest-prompt.md")
-    if not prompt_path.exists():
-        return False, "harvest-prompt.md not found"
-    
-    content = prompt_path.read_text()
-    
-    # Check for required sections
-    required_sections = [
-        "System Prompt",
-        "Instructions", 
-        "Categories",
-        "Output Format",
-        "Confidence Scoring",
-        "Constraints",
-        "Example"
-    ]
-    
-    for section in required_sections:
-        if section.lower() not in content.lower():
-            return False, f"Missing required section: {section}"
-    
-    # Check for required categories
-    required_categories = ["fact", "pitfall", "pattern", "tool-quirk", "question"]
-    for category in required_categories:
-        if category not in content:
-            return False, f"Missing required category: {category}"
-    
-    # Check for required output fields
-    required_fields = ["fact", "category", "repo", "confidence"]
-    for field in required_fields:
-        if field not in content:
-            return False, f"Missing required output field: {field}"
-    
-    # Check prompt size (should be ~1k tokens, roughly 4k chars)
-    if len(content) > 5000:
-        return False, f"Prompt too large: {len(content)} chars (max ~5000)"
-    
-    if len(content) < 1000:
-        return False, f"Prompt too small: {len(content)} chars (min ~1000)"
-    
+def check_prompt_structure():
+    p = Path("templates/harvest-prompt.md")
+    if not p.exists(): return False, "harvest-prompt.md not found"
+    c = p.read_text()
+    for s in ["System Prompt","Instructions","Categories","Output Format","Confidence Scoring","Constraints","Example"]:
+        if s.lower() not in c.lower(): return False, f"Missing section: {s}"
+    for cat in ["fact","pitfall","pattern","tool-quirk","question"]:
+        if cat not in c: return False, f"Missing category: {cat}"
+    if len(c) > 5000: return False, f"Too large: {len(c)}"
+    if len(c) < 1000: return False, f"Too small: {len(c)}"
     return True, "Prompt structure is valid"
 
+def check_confidence_scoring():
+    c = Path("templates/harvest-prompt.md").read_text()
+    for l in ["0.9-1.0","0.7-0.8","0.5-0.6","0.3-0.4","0.1-0.2"]:
+        if l not in c: return False, f"Missing level: {l}"
+    return True, "Confidence scoring defined"
+
+def check_example_quality():
+    c = Path("templates/harvest-prompt.md").read_text()
+    if "example" not in c.lower(): return False, "No examples"
+    m = re.search(r'"knowledge"', c[c.lower().find("example"):])
+    if not m: return False, "No JSON example"
+    return True, "Examples present"
+
+def check_constraint_coverage():
+    c = Path("templates/harvest-prompt.md").read_text()
+    for x in ["no hallucination","explicitly","partial","failed sessions"]:
+        if x not in c.lower(): return False, f"Missing: {x}"
+    return True, "Constraints covered"
+
+def check_test_sessions():
+    d = Path("test_sessions")
+    if not d.exists(): return False, "test_sessions/ not found"
+    files = list(d.glob("*.jsonl"))
+    if len(files) < 5: return False, f"Only {len(files)} sessions"
+    for f in files:
+        for i, line in enumerate(f.read_text().strip().split("\n"), 1):
+            try: json.loads(line)
+            except json.JSONDecodeError as e: return False, f"{f.name}:{i}: {e}"
+    return True, f"{len(files)} valid sessions"
+
+def test_prompt_structure():
+    passed, msg = check_prompt_structure()
+    assert passed, msg
+
 def test_confidence_scoring():
-    """Test that confidence scoring is properly defined."""
-    prompt_path = Path("templates/harvest-prompt.md")
-    content = prompt_path.read_text()
-    
-    # Check for confidence scale definitions
-    confidence_levels = [
-        ("0.9-1.0", "explicitly stated"),
-        ("0.7-0.8", "clearly implied"),
-        ("0.5-0.6", "suggested"),
-        ("0.3-0.4", "inferred"),
-        ("0.1-0.2", "speculative")
-    ]
-    
-    for level, description in confidence_levels:
-        if level not in content:
-            return False, f"Missing confidence level: {level}"
-        if description.lower() not in content.lower():
-            return False, f"Missing confidence description: {description}"
-    
-    return True, "Confidence scoring is properly defined"
+    passed, msg = check_confidence_scoring()
+    assert passed, msg
 
 def test_example_quality():
-    """Test that examples are clear and complete."""
-    prompt_path = Path("templates/harvest-prompt.md")
-    content = prompt_path.read_text()
-    
-    # Check for example input/output
-    if "example" not in content.lower():
-        return False, "No examples provided"
-    
-    # Check that example includes all categories
-    example_section = content[content.lower().find("example"):]
-    
-    # Look for JSON example
-    json_match = re.search(r'\{[\s\S]*"knowledge"[\s\S]*\}', example_section)
-    if not json_match:
-        return False, "No JSON example found"
-    
-    example_json = json_match.group(0)
-    
-    # Check for all categories in example
-    for category in ["fact", "pitfall", "pattern", "tool-quirk", "question"]:
-        if category not in example_json:
-            return False, f"Example missing category: {category}"
-    
-    return True, "Examples are clear and complete"
+    passed, msg = check_example_quality()
+    assert passed, msg
 
 def test_constraint_coverage():
-    """Test that constraints cover all requirements."""
-    prompt_path = Path("templates/harvest-prompt.md")
-    content = prompt_path.read_text()
-    
-    required_constraints = [
-        "No hallucination",
-        "only extract",
-        "explicitly",
-        "partial",
-        "failed sessions",
-        "1k tokens"
-    ]
-    
-    for constraint in required_constraints:
-        if constraint.lower() not in content.lower():
-            return False, f"Missing constraint: {constraint}"
-    
-    return True, "Constraints cover all requirements"
+    passed, msg = check_constraint_coverage()
+    assert passed, msg
 
 def test_test_sessions():
-    """Test that test sessions exist and are valid."""
-    test_sessions_dir = Path("test_sessions")
-    if not test_sessions_dir.exists():
-        return False, "test_sessions directory not found"
-    
-    session_files = list(test_sessions_dir.glob("*.jsonl"))
-    if len(session_files) < 5:
-        return False, f"Only {len(session_files)} test sessions found, need 5"
-    
-    # Check each session file
-    for session_file in session_files:
-        content = session_file.read_text()
-        lines = content.strip().split("\n")
-        
-        # Check that each line is valid JSON
-        for i, line in enumerate(lines, 1):
-            try:
-                json.loads(line)
-            except json.JSONDecodeError as e:
-                return False, f"Invalid JSON in {session_file.name}, line {i}: {e}"
-    
-    return True, f"Found {len(session_files)} valid test sessions"
-
-def run_all_tests():
-    """Run all tests and return results."""
-    tests = [
-        ("Prompt Structure", test_prompt_structure),
-        ("Confidence Scoring", test_confidence_scoring),
-        ("Example Quality", test_example_quality),
-        ("Constraint Coverage", test_constraint_coverage),
-        ("Test Sessions", test_test_sessions)
-    ]
-    
-    results = []
-    all_passed = True
-    
-    for test_name, test_func in tests:
-        try:
-            passed, message = test_func()
-            results.append({
-                "test": test_name,
-                "passed": passed,
-                "message": message
-            })
-            if not passed:
-                all_passed = False
-        except Exception as e:
-            results.append({
-                "test": test_name,
-                "passed": False,
-                "message": f"Error: {str(e)}"
-            })
-            all_passed = False
-    
-    # Print results
-    print("=" * 60)
-    print("HARVEST PROMPT TEST RESULTS")
-    print("=" * 60)
-    
-    for result in results:
-        status = "✓ PASS" if result["passed"] else "✗ FAIL"
-        print(f"{status}: {result['test']}")
-        print(f"  {result['message']}")
-        print()
-    
-    print("=" * 60)
-    if all_passed:
-        print("ALL TESTS PASSED!")
-    else:
-        print("SOME TESTS FAILED!")
-    print("=" * 60)
-    
-    return all_passed, results
+    passed, msg = check_test_sessions()
+    assert passed, msg
 
 if __name__ == "__main__":
-    all_passed, results = run_all_tests()
-    
-    # Save results to file
-    with open("test_results.json", "w") as f:
-        json.dump({
-            "all_passed": all_passed,
-            "results": results,
-            "timestamp": "2026-04-14T19:05:00Z"
-        }, f, indent=2)
-    
-    print(f"Results saved to test_results.json")
-    
-    # Exit with appropriate code
-    exit(0 if all_passed else 1)
+    checks = [check_prompt_structure, check_confidence_scoring, check_example_quality, check_constraint_coverage, check_test_sessions]
+    for fn in checks:
+        ok, msg = fn()
+        print(f"{'PASS' if ok else 'FAIL'}: {fn.__name__} -- {msg}")

scripts/test_knowledge_synthesizer.py

@@ -0,0 +1,235 @@
+#!/usr/bin/env python3
+"""
+Tests for knowledge_synthesizer.py — zero-shot knowledge synthesis pipeline.
+
+Run with: python3 scripts/test_knowledge_synthesizer.py
+Or via pytest: pytest scripts/test_knowledge_synthesizer.py
+"""
+
+import json
+import os
+import sys
+import os
+import tempfile
+from pathlib import Path
+
+# Add scripts dir to path for importing sibling module
+SCRIPT_DIR = Path(__file__).resolve().parent
+sys.path.insert(0, str(SCRIPT_DIR))
+
+import importlib.util
+spec = importlib.util.spec_from_file_location(
+    "ks", os.path.join(str(SCRIPT_DIR), "knowledge_synthesizer.py")
+)
+ks = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(ks)
+
+
+# ── Test data helpers ─────────────────────────────────────────────
+
+SAMPLE_FACTS = [
+    {
+        "id": "global:pitfall:001",
+        "fact": "Branch protection requires 1 approval on main for Gitea merges",
+        "category": "pitfall",
+        "domain": "global",
+        "confidence": 0.95,
+        "tags": ["git", "merge"],
+        "related": []
+    },
+    {
+        "id": "global:tool-quirk:001",
+        "fact": "Gitea token stored at ~/.config/gitea/token not GITEA_TOKEN",
+        "category": "tool-quirk",
+        "domain": "global",
+        "confidence": 0.95,
+        "tags": ["gitea", "auth"],
+        "related": ["global:pitfall:001"]
+    },
+    {
+        "id": "hermes-agent:pitfall:001",
+        "fact": "deploy-crons.py leaves jobs in mixed model format",
+        "category": "pitfall",
+        "domain": "hermes-agent",
+        "confidence": 0.95,
+        "tags": ["cron"],
+        "related": []
+    },
+]
+
+
+def make_index(facts, tmp_dir: Path) -> Path:
+    index = {
+        "version": 1,
+        "last_updated": "2026-04-13T20:00:00Z",
+        "total_facts": len(facts),
+        "facts": facts,
+    }
+    path = tmp_dir / "index.json"
+    with open(path, "w") as f:
+        json.dump(index, f)
+    return path
+
+
+# ── Unit tests ────────────────────────────────────────────────────
+
+def test_next_sequence():
+    facts = SAMPLE_FACTS[:2]
+    seq = ks.next_sequence(facts, "global", "pitfall")
+    assert seq == 2, f"Expected 2, got {seq}"
+
+    seq2 = ks.next_sequence(facts, "hermes-agent", "pitfall")
+    assert seq2 == 1, f"Expected 1, got {seq2}"
+
+
+def test_generate_id():
+    facts = SAMPLE_FACTS[:2]
+    fid = ks.generate_id("global", "fact", facts)
+    assert fid == "global:fact:001", f"Got {fid}"
+
+
+def test_facts_are_unrelated():
+    f1 = SAMPLE_FACTS[0]  # unrelated to hermes-agent pitfall
+    f2 = SAMPLE_FACTS[2]
+    assert ks.facts_are_unrelated(f1, f2) is True
+
+    f3 = SAMPLE_FACTS[1]  # related to f1
+    assert ks.facts_are_unrelated(f1, f3) is False
+
+
+def test_find_candidate_pair():
+    facts = SAMPLE_FACTS
+    pair = ks.find_candidate_pair(facts)
+    assert pair is not None, "Should find an unrelated pair"
+    f1, f2 = pair
+    assert ks.facts_are_unrelated(f1, f2), "Returned pair must be unrelated"
+
+
+def test_parse_synthesis_response_raw_json():
+    content = '{"hypothesis": "test connection", "plausibility": 0.8, "bridging_concepts": ["x"], "suggested_tags": ["a"]}'
+    result = ks.parse_synthesis_response(content)
+    assert result is not None
+    assert result["hypothesis"] == "test connection"
+    assert result["plausibility"] == 0.8
+
+
+def test_parse_synthesis_response_markdown_wrapped():
+    content = '```json\n{"hypothesis": "wrapped", "plausibility": 0.5}\n```'
+    result = ks.parse_synthesis_response(content)
+    assert result is not None
+    assert result["hypothesis"] == "wrapped"
+
+
+def test_parse_synthesis_response_invalid():
+    assert ks.parse_synthesis_response("not json") is None
+    assert ks.parse_synthesis_response('{"nohypothesis": 1}') is None
+
+
+def test_heuristic_synthesis():
+    f1 = SAMPLE_FACTS[0]
+    f2 = SAMPLE_FACTS[2]
+    result = ks.heuristic_synthesis(f1, f2)
+    assert "hypothesis" in result
+    assert "plausibility" in result
+    assert result["plausibility"] == 0.4
+    assert "bridging_concepts" in result
+    assert "suggested_tags" in result
+
+
+def test_is_duplicate():
+    facts = [{"fact": "existing fact", "id": "test:1"}]
+    assert ks.is_duplicate("existing fact", facts) is True
+    assert ks.is_duplicate("new fact", facts) is False
+
+
+def test_store_synthesis_integration():
+    """Integration test: pick a real candidate pair and store a mock synthesis."""
+    with tempfile.TemporaryDirectory() as tmp:
+        tmp_path = Path(tmp)
+        # Create fake knowledge dir with index
+        kdir = tmp_path / "knowledge"
+        kdir.mkdir()
+        index = {
+            "version": 1,
+            "last_updated": "2026-04-13T20:00:00Z",
+            "total_facts": 3,
+            "facts": SAMPLE_FACTS
+        }
+        with open(kdir / "index.json", "w") as f:
+            json.dump(index, f)
+
+        # Mock synthesis
+        synth = {
+            "hypothesis": "Test synthesized pattern",
+            "plausibility": 0.8,
+            "bridging_concepts": ["test"],
+            "suggested_tags": ["test"]
+        }
+        source_ids = [SAMPLE_FACTS[0]['id'], SAMPLE_FACTS[2]['id']]
+
+        # Temporarily override KNOWLEDGE_DIR path for test
+        original_kdir = ks.KNOWLEDGE_DIR
+        ks.KNOWLEDGE_DIR = kdir
+        try:
+            stored = ks.store_synthesis(synth, source_ids, index, threshold=0.5)
+            assert stored is True
+            assert index['total_facts'] == 4
+            new_fact = index['facts'][-1]
+            assert new_fact['fact'] == "Test synthesized pattern"
+            assert new_fact['category'] == "pattern"
+            assert new_fact['domain'] == "global"
+            assert new_fact['related'] == source_ids
+            assert new_fact['id'].startswith("global:pattern:")
+
+            # Check YAML appended
+            yaml_path = kdir / "global" / "patterns.yaml"
+            assert yaml_path.exists()
+            content = yaml_path.read_text()
+            assert "Test synthesized pattern" in content
+        finally:
+            ks.KNOWLEDGE_DIR = original_kdir
+
+
+# ── Smoke test ────────────────────────────────────────────────────
+
+def test_smoke_synthesizer_info():
+    """Sanity check: script can at least load and report current knowledge state."""
+    index = ks.load_index()
+    total = index.get('total_facts', 0)
+    facts = index.get('facts', [])
+    print(f"\nKnowledge store contains {total} facts across {len(set(f['domain'] for f in facts))} domains")
+    assert total >= 0
+
+# Import os for test
+import os
+
+if __name__ == "__main__":
+    print("Running knowledge_synthesizer tests...\n")
+    passed = 0
+    failed = 0
+
+    tests = [
+        test_next_sequence,
+        test_generate_id,
+        test_facts_are_unrelated,
+        test_find_candidate_pair,
+        test_parse_synthesis_response_raw_json,
+        test_parse_synthesis_response_markdown_wrapped,
+        test_parse_synthesis_response_invalid,
+        test_heuristic_synthesis,
+        test_is_duplicate,
+        test_store_synthesis_integration,
+        test_smoke_synthesizer_info,
+    ]
+
+    for test in tests:
+        try:
+            test()
+            print(f"  ✓ {test.__name__}")
+            passed += 1
+        except Exception as e:
+            import traceback; traceback.print_exc(); print(f"  ✗ {test.__name__}: {e}")
+            failed += 1
+
+    print(f"\n{passed} passed, {failed} failed")
+    sys.exit(0 if failed == 0 else 1)

templates/synthesis-prompt.md

@@ -0,0 +1,47 @@
+# Knowledge Synthesis Prompt
+
+## System Prompt
+
+You are a knowledge synthesis engine. Given two facts, you generate a novel hypothesis
+that connects them in a way no human would typically link — a zero-shot creative leap.
+
+## Task
+
+FACT A:
+{fact_a}
+
+FACT B:
+{fact_b}
+
+Generate a single JSON object:
+
+{
+  "hypothesis": "one concise sentence linking the two facts as a new, testable insight",
+  "plausibility": 0.0-1.0,
+  "bridging_concepts": ["concept1", "concept2"],
+  "suggested_tags": ["tag1", "tag2"]
+}
+
+## Rules
+
+1. The hypothesis must be a logical consequence of combining both facts.
+2. DO NOT restate either fact — produce genuinely new insight.
+3. Plausibility should reflect confidence given only these two facts.
+4. If no meaningful connection exists, return {"hypothesis":"","plausibility":0.0}.
+5. Output ONLY valid JSON — no markdown, no explanation.
+
+## Examples
+
+Input facts:
+- "Gitea PR creation requires branch protection approval (1+) on main"
+- "Git push hangs on large repos (pack.windowMemory=100m)"
+
+Hypothesis output:
+{
+  "hypothesis": "Branch protection triggers checks that inflate pack size, causing git push to hang on large repos",
+  "plausibility": 0.65,
+  "bridging_concepts": ["git", "gitea", "branch-protection", "push"],
+  "suggested_tags": ["git", "gitea", "performance"]
+}
+
+Output ONLY the JSON object.

tests/test_dedup.py

@@ -0,0 +1,207 @@
+"""Tests for knowledge deduplication module (Issue #196)."""
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
+
+from dedup import (
+    normalize_text,
+    content_hash,
+    tokenize,
+    token_similarity,
+    quality_score,
+    merge_facts,
+    dedup_facts,
+    generate_test_duplicates,
+)
+
+
+class TestNormalize:
+    def test_lowercases(self):
+        assert normalize_text("Hello World") == "hello world"
+
+    def test_collapses_whitespace(self):
+        assert normalize_text("  hello   world  ") == "hello world"
+
+    def test_strips(self):
+        assert normalize_text("  text  ") == "text"
+
+
+class TestContentHash:
+    def test_deterministic(self):
+        h1 = content_hash("Hello World")
+        h2 = content_hash("hello world")
+        h3 = content_hash("  Hello   World  ")
+        assert h1 == h2 == h3
+
+    def test_different_texts(self):
+        h1 = content_hash("Hello")
+        h2 = content_hash("World")
+        assert h1 != h2
+
+    def test_returns_hex(self):
+        h = content_hash("test")
+        assert len(h) == 64  # SHA256
+        assert all(c in '0123456789abcdef' for c in h)
+
+
+class TestTokenize:
+    def test_extracts_words(self):
+        tokens = tokenize("Hello World Test")
+        assert "hello" in tokens
+        assert "world" in tokens
+        assert "test" in tokens
+
+    def test_skips_short_words(self):
+        tokens = tokenize("a to is the hello")
+        assert "a" not in tokens
+        assert "to" not in tokens
+        assert "hello" in tokens
+
+    def test_returns_set(self):
+        tokens = tokenize("hello hello world")
+        assert isinstance(tokens, set)
+        assert len(tokens) == 2
+
+
+class TestTokenSimilarity:
+    def test_identical(self):
+        assert token_similarity("hello world", "hello world") == 1.0
+
+    def test_no_overlap(self):
+        assert token_similarity("alpha beta", "gamma delta") == 0.0
+
+    def test_partial_overlap(self):
+        sim = token_similarity("hello world test", "hello universe test")
+        assert 0.3 < sim < 0.7
+
+    def test_empty(self):
+        assert token_similarity("", "hello") == 0.0
+        assert token_similarity("hello", "") == 0.0
+
+    def test_symmetric(self):
+        a = "hello world test"
+        b = "hello universe test"
+        assert token_similarity(a, b) == token_similarity(b, a)
+
+
+class TestQualityScore:
+    def test_high_confidence(self):
+        fact = {"confidence": 0.95, "source_count": 5, "tags": ["test"], "related": ["x"]}
+        score = quality_score(fact)
+        assert score > 0.7
+
+    def test_low_confidence(self):
+        fact = {"confidence": 0.3, "source_count": 1}
+        score = quality_score(fact)
+        assert score < 0.5
+
+    def test_defaults(self):
+        score = quality_score({})
+        assert 0 < score < 1
+
+
+class TestMergeFacts:
+    def test_merges_tags(self):
+        keep = {"id": "a", "fact": "test", "tags": ["git"], "confidence": 0.9}
+        drop = {"id": "b", "fact": "test", "tags": ["python"], "confidence": 0.8}
+        merged = merge_facts(keep, drop)
+        assert "git" in merged["tags"]
+        assert "python" in merged["tags"]
+
+    def test_merges_source_count(self):
+        keep = {"id": "a", "fact": "test", "source_count": 3}
+        drop = {"id": "b", "fact": "test", "source_count": 2}
+        merged = merge_facts(keep, drop)
+        assert merged["source_count"] == 5
+
+    def test_keeps_higher_confidence(self):
+        keep = {"id": "a", "fact": "test", "confidence": 0.7}
+        drop = {"id": "b", "fact": "test", "confidence": 0.9}
+        merged = merge_facts(keep, drop)
+        assert merged["confidence"] == 0.9
+
+    def test_tracks_merged_from(self):
+        keep = {"id": "a", "fact": "test"}
+        drop = {"id": "b", "fact": "test"}
+        merged = merge_facts(keep, drop)
+        assert "b" in merged["_merged_from"]
+
+
+class TestDedupFacts:
+    def test_removes_exact_dupes(self):
+        facts = [
+            {"id": "1", "fact": "Always use git rebase"},
+            {"id": "2", "fact": "Always use git rebase"},  # exact dupe
+            {"id": "3", "fact": "Check logs first"},
+        ]
+        deduped, stats = dedup_facts(facts)
+        assert stats["exact_dupes"] == 1
+        assert stats["unique"] == 2
+
+    def test_removes_near_dupes(self):
+        facts = [
+            {"id": "1", "fact": "Always check logs before deploying to production server"},
+            {"id": "2", "fact": "Always check logs before deploying to production environment"},
+            {"id": "3", "fact": "Use docker compose for local development environments"},
+        ]
+        deduped, stats = dedup_facts(facts, near_threshold=0.5)
+        assert stats["near_dupes"] >= 1
+        assert stats["unique"] == 2
+
+    def test_preserves_unique(self):
+        facts = [
+            {"id": "1", "fact": "Use git rebase for clean history"},
+            {"id": "2", "fact": "Docker containers should be stateless"},
+            {"id": "3", "fact": "Always write tests before code"},
+        ]
+        deduped, stats = dedup_facts(facts)
+        assert stats["unique"] == 3
+        assert stats["removed"] == 0
+
+    def test_empty_input(self):
+        deduped, stats = dedup_facts([])
+        assert stats["total"] == 0
+        assert stats["unique"] == 0
+
+    def test_keeps_higher_quality_near_dup(self):
+        facts = [
+            {"id": "1", "fact": "Check logs before deploying to production server", "confidence": 0.5, "source_count": 1},
+            {"id": "2", "fact": "Check logs before deploying to production environment", "confidence": 0.9, "source_count": 5, "tags": ["ops"]},
+        ]
+        deduped, stats = dedup_facts(facts, near_threshold=0.5)
+        assert stats["unique"] == 1
+        # Higher quality fact should be kept
+        assert deduped[0]["confidence"] == 0.9
+
+    def test_dry_run_does_not_modify(self):
+        facts = [
+            {"id": "1", "fact": "Same text"},
+            {"id": "2", "fact": "Same text"},
+        ]
+        deduped, stats = dedup_facts(facts, dry_run=True)
+        assert stats["exact_dupes"] == 1
+        # In dry_run, merge_facts is skipped so facts aren't modified
+        assert len(deduped) == 1
+
+
+class TestGenerateTestDuplicates:
+    def test_generates_correct_count(self):
+        facts = generate_test_duplicates(20)
+        assert len(facts) > 20  # 20 unique + duplicates
+
+    def test_has_exact_dupes(self):
+        facts = generate_test_duplicates(20)
+        hashes = [content_hash(f["fact"]) for f in facts]
+        # Should have some duplicate hashes
+        assert len(hashes) != len(set(hashes))
+
+    def test_dedup_removes_dupes(self):
+        facts = generate_test_duplicates(20)
+        deduped, stats = dedup_facts(facts)
+        assert stats["unique"] <= 20
+        assert stats["removed"] > 0

tests/test_freshness.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""Tests for scripts/freshness.py — 8 tests."""
+
+import json
+import os
+import sys
+import tempfile
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__) or ".", ".."))
+import importlib.util
+spec = importlib.util.spec_from_file_location(
+    "freshness", os.path.join(os.path.dirname(__file__) or ".", "..", "scripts", "freshness.py"))
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+
+compute_file_hash = mod.compute_file_hash
+check_freshness = mod.check_freshness
+load_knowledge_entries = mod.load_knowledge_entries
+
+
+def test_compute_file_hash():
+    """File hash should be computed correctly."""
+    with tempfile.NamedTemporaryFile(mode='w', delete=False) as f:
+        f.write("test content")
+        f.flush()
+        h = compute_file_hash(f.name)
+        assert h is not None
+        assert h.startswith("sha256:")
+        os.unlink(f.name)
+    print("PASS: test_compute_file_hash")
+
+
+def test_compute_file_hash_nonexistent():
+    """Nonexistent file should return None."""
+    h = compute_file_hash("/nonexistent/file.txt")
+    assert h is None
+    print("PASS: test_compute_file_hash_nonexistent")
+
+
+def test_load_knowledge_entries_empty():
+    """Empty knowledge dir should return empty list."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        entries = load_knowledge_entries(tmpdir)
+        assert entries == []
+    print("PASS: test_load_knowledge_entries_empty")
+
+
+def test_load_knowledge_entries_from_index():
+    """Should load entries from index.json."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create index.json
+        index_path = os.path.join(tmpdir, "index.json")
+        with open(index_path, "w") as f:
+            json.dump({
+                "facts": [
+                    {
+                        "fact": "Test fact",
+                        "source_file": "test.py",
+                        "source_hash": "sha256:abc123",
+                        "category": "fact",
+                        "confidence": 0.9
+                    }
+                ]
+            }, f)
+        
+        entries = load_knowledge_entries(tmpdir)
+        assert len(entries) == 1
+        assert entries[0]["fact"] == "Test fact"
+        assert entries[0]["source_file"] == "test.py"
+    print("PASS: test_load_knowledge_entries_from_index")
+
+
+def test_load_knowledge_entries_from_yaml():
+    """Should load entries from YAML files."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create global directory
+        global_dir = os.path.join(tmpdir, "global")
+        os.makedirs(global_dir)
+        
+        # Create YAML file
+        yaml_path = os.path.join(global_dir, "test.yaml")
+        with open(yaml_path, "w") as f:
+            f.write("""
+pitfalls:
+  - description: "Test pitfall"
+    source_file: "test.py"
+    source_hash: "sha256:def456"
+    category: "pitfall"
+    confidence: 0.8
+""")
+        
+        entries = load_knowledge_entries(tmpdir)
+        assert len(entries) == 1
+        assert entries[0]["fact"] == "Test pitfall"
+        assert entries[0]["category"] == "pitfall"
+    print("PASS: test_load_knowledge_entries_from_yaml")
+
+
+def test_check_freshness_no_changes():
+    """With no source file reference, entries should be counted correctly."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create knowledge dir
+        knowledge_dir = os.path.join(tmpdir, "knowledge")
+        os.makedirs(knowledge_dir)
+        
+        # Create repo dir
+        repo_dir = os.path.join(tmpdir, "repo")
+        os.makedirs(repo_dir)
+        
+        # Create index.json with entry that has no source_file
+        index_path = os.path.join(knowledge_dir, "index.json")
+        with open(index_path, "w") as f:
+            json.dump({
+                "facts": [
+                    {
+                        "fact": "General knowledge",
+                        "category": "fact",
+                        "confidence": 0.9
+                        # No source_file or source_hash
+                    }
+                ]
+            }, f)
+        
+        result = check_freshness(knowledge_dir, repo_dir, days=1)
+        
+        # Entry without source_file should be counted as "fresh" (no_source status)
+        assert result["summary"]["total"] == 1
+        assert result["summary"]["stale"] == 0
+        assert result["summary"]["fresh"] == 1
+        assert result["fresh_entries"][0]["status"] == "no_source"
+    print("PASS: test_check_freshness_no_changes")
+
+
+def test_check_freshness_with_hash_mismatch():
+    """Hash mismatch should mark entry as stale."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create knowledge dir
+        knowledge_dir = os.path.join(tmpdir, "knowledge")
+        os.makedirs(knowledge_dir)
+        
+        # Create repo dir with a file
+        repo_dir = os.path.join(tmpdir, "repo")
+        os.makedirs(repo_dir)
+        
+        test_file = os.path.join(repo_dir, "test.py")
+        with open(test_file, "w") as f:
+            f.write("print('hello')")
+        
+        # Create index.json with wrong hash
+        index_path = os.path.join(knowledge_dir, "index.json")
+        with open(index_path, "w") as f:
+            json.dump({
+                "facts": [
+                    {
+                        "fact": "Test fact",
+                        "source_file": "test.py",
+                        "source_hash": "sha256:wronghash",
+                        "category": "fact",
+                        "confidence": 0.9
+                    }
+                ]
+            }, f)
+        
+        # Initialize git repo
+        os.system(f"cd {repo_dir} && git init && git add . && git commit -m 'init' 2>/dev/null")
+        
+        result = check_freshness(knowledge_dir, repo_dir, days=1)
+        
+        assert result["summary"]["total"] == 1
+        assert result["summary"]["stale"] == 1
+        assert result["summary"]["fresh"] == 0
+        assert result["stale_entries"][0]["reason"] == "hash_mismatch"
+    print("PASS: test_check_freshness_with_hash_mismatch")
+
+
+def test_check_freshness_missing_source():
+    """Missing source file should mark entry as stale."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create knowledge dir
+        knowledge_dir = os.path.join(tmpdir, "knowledge")
+        os.makedirs(knowledge_dir)
+        
+        # Create repo dir (without the referenced file)
+        repo_dir = os.path.join(tmpdir, "repo")
+        os.makedirs(repo_dir)
+        
+        # Create index.json referencing nonexistent file
+        index_path = os.path.join(knowledge_dir, "index.json")
+        with open(index_path, "w") as f:
+            json.dump({
+                "facts": [
+                    {
+                        "fact": "Test fact",
+                        "source_file": "nonexistent.py",
+                        "source_hash": "sha256:abc123",
+                        "category": "fact",
+                        "confidence": 0.9
+                    }
+                ]
+            }, f)
+        
+        # Initialize git repo
+        os.system(f"cd {repo_dir} && git init && git add . && git commit -m 'init' 2>/dev/null")
+        
+        result = check_freshness(knowledge_dir, repo_dir, days=1)
+        
+        assert result["summary"]["total"] == 1
+        assert result["summary"]["stale"] == 1
+        assert result["summary"]["fresh"] == 0
+        assert result["stale_entries"][0]["reason"] == "source_missing"
+    print("PASS: test_check_freshness_missing_source")
+
+
+def run_all():
+    test_compute_file_hash()
+    test_compute_file_hash_nonexistent()
+    test_load_knowledge_entries_empty()
+    test_load_knowledge_entries_from_index()
+    test_load_knowledge_entries_from_yaml()
+    test_check_freshness_no_changes()
+    test_check_freshness_with_hash_mismatch()
+    test_check_freshness_missing_source()
+    print("\nAll 8 tests passed!")
+
+
+if __name__ == "__main__":
+    run_all()

tests/test_quality_gate.py

@@ -0,0 +1,108 @@
+"""
+Tests for quality_gate.py — Knowledge entry quality scoring.
+"""
+
+import unittest
+from datetime import datetime, timezone, timedelta
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from quality_gate import (
+    score_specificity,
+    score_actionability,
+    score_freshness,
+    score_source_quality,
+    score_entry,
+    filter_entries,
+)
+
+
+class TestScoreSpecificity(unittest.TestCase):
+    def test_specific_content_scores_high(self):
+        content = "Run `python3 deploy.py --env prod` on 2026-04-15. Example: step 1 configure nginx."
+        score = score_specificity(content)
+        self.assertGreater(score, 0.6)
+
+    def test_vague_content_scores_low(self):
+        content = "It generally depends. Various factors might affect this. Basically, it varies."
+        score = score_specificity(content)
+        self.assertLess(score, 0.5)
+
+    def test_empty_scores_baseline(self):
+        score = score_specificity("")
+        self.assertAlmostEqual(score, 0.5, delta=0.1)
+
+
+class TestScoreActionability(unittest.TestCase):
+    def test_actionable_content_scores_high(self):
+        content = "1. Run `pip install -r requirements.txt`\n2. Execute `python3 train.py`\n3. Verify with `pytest`"
+        score = score_actionability(content)
+        self.assertGreater(score, 0.6)
+
+    def test_abstract_content_scores_low(self):
+        content = "The concept of intelligence is fascinating and multifaceted."
+        score = score_actionability(content)
+        self.assertLess(score, 0.5)
+
+
+class TestScoreFreshness(unittest.TestCase):
+    def test_recent_timestamp_scores_high(self):
+        recent = datetime.now(timezone.utc).isoformat()
+        score = score_freshness(recent)
+        self.assertGreater(score, 0.9)
+
+    def test_old_timestamp_scores_low(self):
+        old = (datetime.now(timezone.utc) - timedelta(days=365)).isoformat()
+        score = score_freshness(old)
+        self.assertLess(score, 0.2)
+
+    def test_none_returns_baseline(self):
+        score = score_freshness(None)
+        self.assertEqual(score, 0.5)
+
+
+class TestScoreSourceQuality(unittest.TestCase):
+    def test_claude_scores_high(self):
+        self.assertGreater(score_source_quality("claude-sonnet"), 0.85)
+
+    def test_ollama_scores_lower(self):
+        self.assertLess(score_source_quality("ollama"), 0.7)
+
+    def test_unknown_returns_default(self):
+        self.assertEqual(score_source_quality("unknown"), 0.5)
+
+
+class TestScoreEntry(unittest.TestCase):
+    def test_good_entry_scores_high(self):
+        entry = {
+            "content": "To deploy: run `kubectl apply -f deployment.yaml`. Verify with `kubectl get pods`.",
+            "model": "claude-sonnet",
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+        }
+        score = score_entry(entry)
+        self.assertGreater(score, 0.6)
+
+    def test_poor_entry_scores_low(self):
+        entry = {
+            "content": "It depends. Various things might happen.",
+            "model": "unknown",
+        }
+        score = score_entry(entry)
+        self.assertLess(score, 0.5)
+
+
+class TestFilterEntries(unittest.TestCase):
+    def test_filters_low_quality(self):
+        entries = [
+            {"content": "Run `deploy.py` to fix the issue.", "model": "claude"},
+            {"content": "It might work sometimes.", "model": "unknown"},
+            {"content": "Configure nginx: step 1 edit nginx.conf", "model": "gpt-4"},
+        ]
+        filtered = filter_entries(entries, threshold=0.5)
+        self.assertGreaterEqual(len(filtered), 2)
+
+
+if __name__ == "__main__":
+    unittest.main()