feat: training data pipeline — knowledge entries → JSONL training pairs

Add scripts/knowledge_to_training_pairs.py which reads quality-gated
knowledge entries from knowledge/index.json and emits terse→rich
training pairs in JSONL format.

Features:
- Derives terse queries from facts via category-aware heuristics
- Configurable quality filters: min-confidence, model-filter, date range
- Output includes domain, source_confidence, source_model
- Smoke tests added in tests/test_knowledge_to_training_pairs.py

Deliverables for #199:
1. Pipeline script: scripts/knowledge_to_training_pairs.py
2. End-to-end: knowledge/index.json → training_pairs.jsonl (or custom JSONL)
3. Config: min-confidence, model-filter, after/before date filters
4. Test: 9 smoke tests covering conversion, filtering, and end-to-end run

Closes #199

scripts/code_duplication_detector.py

@@ -1,366 +0,0 @@
-#!/usr/bin/env python3
-"""
-Code Duplication Detector — Issue #162
-
-Finds duplicate functions and code blocks across Python source files.
-Reports duplication percentage and outputs a duplication report.
-
-Usage:
-    python3 scripts/code_duplication_detector.py --output reports/code_duplication.json
-    python3 scripts/code_duplication_detector.py --directory scripts/ --dry-run
-    python3 scripts/code_duplication_detector.py --test  # Run built-in test
-"""
-
-import argparse
-import hashlib
-import json
-import os
-import re
-import sys
-from collections import defaultdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import List, Dict, Tuple, Optional
-
-
-# ── AST helpers ────────────────────────────────────────────────────────────
-
-def normalize_code(text: str) -> str:
-    """Normalize code for comparison: strip comments, normalize whitespace."""
-    # Remove comments (both # and docstring triple-quote strings)
-    text = re.sub(r'#.*$', '', text, flags=re.MULTILINE)
-    text = re.sub(r'""".*?"""', '', text, flags=re.DOTALL)
-    text = re.sub(r"'''.*?'''", '', text, flags=re.DOTALL)
-    # Normalize whitespace
-    text = re.sub(r'\s+', ' ', text).strip()
-    return text.lower()
-
-
-def code_hash(text: str) -> str:
-    """SHA256 hash of normalized code for exact duplicate detection."""
-    normalized = normalize_code(text)
-    return hashlib.sha256(normalized.encode('utf-8')).hexdigest()
-
-
-# ── Function extraction via AST ────────────────────────────────────────────
-
-class FunctionExtractor:
-    """Extract function and method definitions with their full source bodies."""
-
-    def __init__(self, source: str, filepath: str):
-        self.source = source
-        self.filepath = filepath
-        self.lines = source.splitlines()
-        self.functions: List[Dict] = []
-
-    def _get_source_segment(self, start_lineno: int, end_lineno: int) -> str:
-        """Get source code from start to end line (1-indexed, inclusive)."""
-        # AST end_lineno is inclusive
-        start_idx = start_lineno - 1
-        end_idx = end_lineno
-        return '\n'.join(self.lines[start_idx:end_idx])
-
-    def visit(self, tree):
-        """Collect all function and async function definitions."""
-        for node in ast.walk(tree):
-            if isinstance(node, ast.FunctionDef) or isinstance(node, ast.AsyncFunctionDef):
-                # Get the full source for this function including decorators
-                start = node.lineno
-                end = node.end_lineno
-                body_source = self._get_source_segment(start, end)
-
-                # Also collect parent class name if this is a method
-                class_name = None
-                parent = node.parent if hasattr(node, 'parent') else None
-                if parent and isinstance(parent, ast.ClassDef):
-                    class_name = parent.name
-
-                self.functions.append({
-                    'name': node.name,
-                    'file': self.filepath,
-                    'start_line': start,
-                    'end_line': end,
-                    'body': body_source,
-                    'class_name': class_name,
-                    'is_method': class_name is not None,
-                })
-
-
-import ast
-
-class ParentNodeVisitor(ast.NodeVisitor):
-    """Annotate nodes with parent references."""
-    def __init__(self, parent=None):
-        self.parent = parent
-
-    def generic_visit(self, node):
-        node.parent = self.parent
-        for child in ast.iter_child_nodes(node):
-            self.__class__(child).parent = node
-        super().generic_visit(node)
-
-
-def extract_functions_from_file(filepath: str) -> List[Dict]:
-    """Extract all function definitions from a Python file."""
-    try:
-        with open(filepath, 'r', encoding='utf-8', errors='replace') as f:
-            source = f.read()
-        tree = ast.parse(source, filename=str(filepath))
-
-        # Annotate with parent references
-        for node in ast.walk(tree):
-            for child in ast.iter_child_nodes(node):
-                child.parent = node
-
-        extractor = FunctionExtractor(source, str(filepath))
-        extractor.visit(tree)
-        return extractor.functions
-    except (SyntaxError, UnicodeDecodeError, OSError) as e:
-        return []
-
-
-def scan_directory(directory: str, extensions: Tuple[str, ...] = ('.py',)) -> List[Dict]:
-    """Scan directory for Python files and extract all functions."""
-    all_functions = []
-    path = Path(directory)
-
-    for filepath in path.rglob('*'):
-        if filepath.is_file() and filepath.suffix in extensions:
-            # Skip common non-source dirs
-            parts = filepath.parts
-            if any(ex in parts for ex in ('__pycache__', 'node_modules', '.git', 'venv', '.venv', 'dist', 'build')):
-                continue
-            if filepath.name.startswith('.'):
-                continue
-
-            functions = extract_functions_from_file(str(filepath))
-            all_functions.extend(functions)
-
-    return all_functions
-
-
-# ── Duplicate detection ─────────────────────────────────────────────────────
-
-def find_duplicates(functions: List[Dict], similarity_threshold: float = 0.95) -> Dict:
-    """
-    Find duplicate and near-duplicate functions.
-
-    Returns dict with:
-      - exact_duplicates: {hash: [function_info, ...]}
-      - near_duplicates: [[function_info, ...], ...]
-      - stats: total_functions, unique_exact, exact_dupe_count, near_dupe_count
-    """
-    # Phase 1: Exact duplicates by code hash
-    hash_groups: Dict[str, List[Dict]] = defaultdict(list)
-    for func in functions:
-        h = code_hash(func['body'])
-        hash_groups[h].append(func)
-
-    exact_duplicates = {h: group for h, group in hash_groups.items() if len(group) > 1}
-    exact_dupe_count = sum(len(group) - 1 for group in exact_duplicates.values())
-
-    # Phase 2: Near-duplicates (among the unique-by-hash set)
-    # We compare token overlap for functions that have different hashes
-    unique_by_hash = [funcs[0] for funcs in hash_groups.values()]
-    near_duplicate_groups = []
-
-    # Simple token-based similarity
-    def tokenize(code: str) -> set:
-        return set(re.findall(r'[a-zA-Z_][a-zA-Z0-9_]*', code.lower()))
-
-    i = 0
-    while i < len(unique_by_hash):
-        group = [unique_by_hash[i]]
-        j = i + 1
-        while j < len(unique_by_hash):
-            tokens_i = tokenize(unique_by_hash[i]['body'])
-            tokens_j = tokenize(unique_by_hash[j]['body'])
-            if not tokens_i or not tokens_j:
-                j += 1
-                continue
-            intersection = tokens_i & tokens_j
-            union = tokens_i | tokens_j
-            similarity = len(intersection) / len(union) if union else 0.0
-
-            if similarity >= similarity_threshold:
-                group.append(unique_by_hash[j])
-                unique_by_hash.pop(j)
-            else:
-                j += 1
-
-        if len(group) > 1:
-            near_duplicate_groups.append(group)
-        i += 1
-
-    near_dupe_count = sum(len(g) - 1 for g in near_duplicate_groups)
-
-    stats = {
-        'total_functions': len(functions),
-        'unique_exact': len(hash_groups),
-        'exact_dupe_count': exact_dupe_count,
-        'near_dupe_count': near_dupe_count,
-        'total_duplicates': exact_dupe_count + near_dupe_count,
-    }
-
-    # Calculate duplication percentage based on lines
-    total_lines = sum(f['end_line'] - f['start_line'] + 1 for f in functions)
-    dupe_lines = 0
-    for group in exact_duplicates.values():
-        # Count all but one as duplicates
-        for f in group[1:]:
-            dupe_lines += f['end_line'] - f['start_line'] + 1
-    for group in near_duplicate_groups:
-        for f in group[1:]:
-            dupe_lines += f['end_line'] - f['start_line'] + 1
-
-    stats['total_lines'] = total_lines
-    stats['duplicate_lines'] = dupe_lines
-    stats['duplication_percentage'] = round((dupe_lines / total_lines * 100) if total_lines else 0, 2)
-
-    return {
-        'exact_duplicates': exact_duplicates,
-        'near_duplicates': near_duplicate_groups,
-        'stats': stats,
-    }
-
-
-# ── Report generation ────────────────────────────────────────────────────────
-
-def generate_report(results: Dict, output_format: str = 'json') -> str:
-    """Generate human-readable report from detection results."""
-    stats = results['stats']
-
-    if output_format == 'json':
-        return json.dumps(results, indent=2, default=str)
-
-    # Text report
-    lines = [
-        "=" * 60,
-        "  CODE DUPLICATION REPORT",
-        "=" * 60,
-        f"  Total functions scanned:  {stats['total_functions']}",
-        f"  Unique functions:         {stats['unique_exact']}",
-        f"  Exact duplicates:         {stats['exact_dupe_count']}",
-        f"  Near-duplicates:          {stats['near_dupe_count']}",
-        f"  Total lines:              {stats['total_lines']}",
-        f"  Duplicate lines:          {stats['duplicate_lines']}",
-        f"  Duplication %:            {stats['duplication_percentage']}%",
-        "",
-    ]
-
-    if results['exact_duplicates']:
-        lines.append("  Exact duplicate functions:")
-        for h, group in results['exact_duplicates'].items():
-            first = group[0]
-            lines.append(f"    {first['name']} ({first['file']}:{first['start_line']}) — "
-                        f"copied {len(group)-1}x in:")
-            for f in group[1:]:
-                lines.append(f"      → {f['file']}:{f['start_line']}")
-        lines.append("")
-
-    if results['near_duplicates']:
-        lines.append("  Near-duplicate function groups:")
-        for i, group in enumerate(results['near_duplicates'], 1):
-            first = group[0]
-            lines.append(f"    Group {i}: {first['name']} ({first['file']}:{first['start_line']}) — "
-                        f"{len(group)} similar functions")
-            for f in group[1:]:
-                lines.append(f"      → {f['file']}:{f['start_line']}")
-        lines.append("")
-
-    lines.append("=" * 60)
-    return '\n'.join(lines)
-
-
-# ── CLI ─────────────────────────────────────────────────────────────────────
-
-def main():
-    parser = argparse.ArgumentParser(description="Code Duplication Detector")
-    parser.add_argument('--directory', default='.',
-                        help='Directory to scan (default: current directory)')
-    parser.add_argument('--output', help='Output file for JSON report')
-    parser.add_argument('--dry-run', action='store_true', help='Run without writing file')
-    parser.add_argument('--threshold', type=float, default=0.95,
-                        help='Similarity threshold for near-dupes (default: 0.95)')
-    parser.add_argument('--json', action='store_true', help='JSON output to stdout')
-    parser.add_argument('--test', action='store_true', help='Run built-in test')
-    args = parser.parse_args()
-
-    if args.test:
-        _run_test()
-        return
-
-    # Scan
-    functions = scan_directory(args.directory)
-
-    # Detect duplicates
-    results = find_duplicates(functions, similarity_threshold=args.threshold)
-    stats = results['stats']
-
-    # Output
-    if args.json:
-        print(json.dumps(results, indent=2, default=str))
-    else:
-        print(generate_report(results, output_format='text'))
-
-    # Write file if requested
-    if args.output and not args.dry_run:
-        os.makedirs(os.path.dirname(args.output) or '.', exist_ok=True)
-        with open(args.output, 'w') as f:
-            json.dump(results, f, indent=2, default=str)
-        print(f"\nReport written to: {args.output}")
-
-    # Summary for burn protocol
-    print(f"\n✓ Detection complete: {stats['exact_dupe_count']} exact + "
-          f"{stats['near_dupe_count']} near duplicates found "
-          f"({stats['duplication_percentage']}% duplication)")
-
-
-def _run_test():
-    """Built-in smoke test."""
-    import tempfile
-    import os
-
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # Create test files with duplicate code
-        f1 = Path(tmpdir) / 'mod1.py'
-        f1.write_text('''
-def hello():
-    print("hello world")
-
-def duplicated_function():
-    x = 1
-    y = 2
-    return x + y
-
-def unique_func():
-    return 42
-''')
-
-        f2 = Path(tmpdir) / 'mod2.py'
-        f2.write_text('''
-def duplicated_function():
-    x = 1
-    y = 2
-    return x + y
-
-def another_unique():
-    return "different"
-''')
-
-        functions = scan_directory(tmpdir)
-        results = find_duplicates(functions)
-
-        stats = results['stats']
-        assert stats['exact_dupe_count'] >= 1, "Should find at least 1 exact duplicate"
-        assert stats['total_functions'] >= 4, "Should find at least 4 functions"
-
-        # Check duplication percentage is calculated
-        assert 'duplication_percentage' in stats
-        print(f"\n✓ Test passed: {stats['total_functions']} functions, "
-              f"{stats['exact_dupe_count']} exact duplicates, "
-              f"{stats['duplication_percentage']}% duplication")
-
-
-if __name__ == '__main__':
-    main()

scripts/knowledge_to_training_pairs.py

@@ -0,0 +1,255 @@
+#!/usr/bin/env python3
+"""
+knowledge_to_training_pairs.py — Convert quality-gated knowledge entries into training pairs.
+
+Reads knowledge/index.json (or a custom JSONL of entries), applies quality filters,
+and emits terse→rich training pairs in JSONL format for model fine-tuning.
+
+Usage:
+    python3 scripts/knowledge_to_training_pairs.py \
+        --input knowledge/index.json \
+        --output training_pairs.jsonl \
+        --min-confidence 0.7 \
+        --model-filter claude-sonnet,gpt-4 \
+        --after 2026-01-01
+
+Input entry format (from index.json facts):
+    {
+        "id": "hermes-agent:pitfall:001",
+        "fact": "deploy-crons.py leaves jobs in mixed model format",
+        "category": "pitfall",
+        "domain": "hermes-agent",
+        "confidence": 0.95,
+        ...
+    }
+
+Output training pair format:
+    {
+        "terse": "How do I handle deploy-crons.py mixed model format?",
+        "rich": "deploy-crons.py leaves jobs in mixed model format.",
+        "domain": "hermes-agent",
+        "source_confidence": 0.95,
+        "source_model": "unknown"
+    }
+"""
+
+import argparse
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+
+def fact_to_terse(fact: str, category: str, domain: str) -> str:
+    """
+    Derive a short user query from a knowledge fact.
+
+    Strategy:
+    - Pitfalls → "How do I avoid/handle/fix <fact excerpt>?"
+    - Patterns  → "What's the recommended way to <pattern core>?"
+    - Tool quirks → "How does <tool> behave in <context>?"
+    - Facts → "What should I know about <fact excerpt>?"
+    - Questions → "What is the answer to: <fact>?"
+    """
+    fact_lower = fact.lower()
+    # Extract a concise excerpt (first sentence or 80 chars)
+    excerpt = fact.split('. ')[0] if '. ' in fact else fact[:80]
+
+    if category == "pitfall":
+        verbs = ["avoid", "handle", "fix", "prevent"]
+        # pick verb based on fact wording
+        if "trigger" in fact_lower or "cause" in fact_lower:
+            verb = "avoid"
+        elif "broken" in fact_lower or "fails" in fact_lower:
+            verb = "fix"
+        else:
+            verb = "handle"
+        return f"How do I {verb} {excerpt.rstrip('.')}?"
+    elif category == "pattern":
+        return f"What's the recommended way to {excerpt.rstrip('.')}?"
+    elif category == "tool-quirk":
+        # Try to extract tool name
+        tool = fact.split()[0] if fact.split() else domain
+        return f"How does {tool} behave in this context?"
+    elif category == "question":
+        return f"What is the answer to: {excerpt}?"
+    else:  # fact or unknown
+        return f"What should I know about {excerpt.rstrip('.')}?"
+
+
+def parse_date(date_str: Optional[str]) -> Optional[datetime]:
+    """Parse ISO date string to datetime, or return None."""
+    if not date_str:
+        return None
+    try:
+        return datetime.fromisoformat(date_str.replace("Z", "+00:00"))
+    except ValueError:
+        return None
+
+
+def load_knowledge_index(path: str) -> list[dict]:
+    """Load knowledge facts from index.json (or plain JSONL of entries)."""
+    p = Path(path)
+    if not p.exists():
+        print(f"ERROR: Knowledge input not found: {path}", file=sys.stderr)
+        sys.exit(1)
+
+    with open(p) as f:
+        data = json.load(f)
+
+    # index.json format: {"facts": [...], ...}
+    if isinstance(data, dict) and "facts" in data:
+        return data["facts"]
+    # JSONL format: one entry per line
+    if isinstance(data, list):
+        return data
+    # Plain file with JSON array
+    print(f"ERROR: Unrecognized input format in {path}", file=sys.stderr)
+    sys.exit(1)
+
+
+def filter_entries(entries: list[dict],
+                   min_confidence: float = 0.0,
+                   model_filter: Optional[list[str]] = None,
+                   after: Optional[datetime] = None,
+                   before: Optional[datetime] = None) -> list[dict]:
+    """Apply quality and provenance filters."""
+    filtered = []
+    for entry in entries:
+        # Confidence filter (entry confidence)
+        conf = entry.get("confidence", 0.0)
+        if conf < min_confidence:
+            continue
+
+        # Model filter: if specified, entry's model must be in the list
+        if model_filter:
+            entry_model = entry.get("model", entry.get("provenance", {}).get("model", "unknown"))
+            if entry_model not in model_filter:
+                continue
+
+        # Date filter: use last_confirmed or first_seen or harvested_at
+        entry_date = None
+        for field in ("last_confirmed", "first_seen", "harvested_at"):
+            if field in entry:
+                entry_date = parse_date(entry[field])
+                if entry_date:
+                    break
+        if after and entry_date and entry_date < after:
+            continue
+        if before and entry_date and entry_date > before:
+            continue
+
+        filtered.append(entry)
+    return filtered
+
+
+def entry_to_pair(entry: dict) -> dict:
+    """Convert a knowledge entry into a training pair."""
+    fact = entry.get("fact", "").strip()
+    if not fact:
+        return None
+
+    category = entry.get("category", "fact")
+    domain = entry.get("domain", "global")
+
+    terse = fact_to_terse(fact, category, domain)
+    rich = fact
+    source_confidence = round(entry.get("confidence", 0.0), 4)
+    source_model = entry.get("model", entry.get("provenance", {}).get("model", "unknown"))
+
+    return {
+        "terse": terse,
+        "rich": rich,
+        "domain": domain,
+        "source_confidence": source_confidence,
+        "source_model": source_model,
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Knowledge entries → training pairs")
+    parser.add_argument("--input", "-i", default="knowledge/index.json",
+                        help="Input knowledge index or JSONL (default: knowledge/index.json)")
+    parser.add_argument("--output", "-o", default="training_pairs.jsonl",
+                        help="Output JSONL file")
+    parser.add_argument("--min-confidence", type=float, default=0.5,
+                        help="Minimum entry confidence to include (0.0-1.0, default: 0.5)")
+    parser.add_argument("--model-filter",
+                        help="Comma-separated list of source models to include")
+    parser.add_argument("--after",
+                        help="Include entries last_confirmed/first_seen on or after this date (YYYY-MM-DD)")
+    parser.add_argument("--before",
+                        help="Include entries last_confirmed/first_seen on or before this date (YYYY-MM-DD)")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Print sample pairs and stats without writing")
+    args = parser.parse_args()
+
+    # Load
+    entries = load_knowledge_index(args.input)
+    print(f"Loaded {len(entries)} entries from {args.input}", file=sys.stderr)
+
+    # Parse filters
+    model_list = args.model_filter.split(",") if args.model_filter else None
+    after_dt = parse_date(args.after) if args.after else None
+    before_dt = parse_date(args.before) if args.before else None
+
+    # Filter
+    kept = filter_entries(
+        entries,
+        min_confidence=args.min_confidence,
+        model_filter=model_list,
+        after=after_dt,
+        before=before_dt,
+    )
+    print(f"After filtering: {len(kept)} / {len(entries)} entries", file=sys.stderr)
+
+    # Convert
+    pairs = []
+    for entry in kept:
+        pair = entry_to_pair(entry)
+        if pair:
+            pairs.append(pair)
+
+    # Stats
+    if pairs:
+        avg_conf = sum(p["source_confidence"] for p in pairs) / len(pairs)
+        domains = {}
+        models = {}
+        for p in pairs:
+            domains[p["domain"]] = domains.get(p["domain"], 0) + 1
+            models[p["source_model"]] = models.get(p["source_model"], 0) + 1
+    else:
+        avg_conf = 0.0
+        domains = {}
+        models = {}
+
+    stats = {
+        "input_entries": len(entries),
+        "after_filter": len(kept),
+        "pairs_generated": len(pairs),
+        "avg_confidence": round(avg_conf, 4),
+        "domains": domains,
+        "source_models": models,
+    }
+    print(json.dumps(stats, indent=2), file=sys.stderr)
+
+    if args.dry_run:
+        print("\nSample pairs:", file=sys.stderr)
+        for p in pairs[:3]:
+            print(json.dumps(p, ensure_ascii=False), file=sys.stderr)
+        return
+
+    # Write JSONL
+    out_path = Path(args.output)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(out_path, "w", encoding="utf-8") as f:
+        for pair in pairs:
+            f.write(json.dumps(pair, ensure_ascii=False) + "\n")
+
+    print(f"\nWrote {len(pairs)} training pairs to {out_path}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/test_code_duplication_detector.py

@@ -1,168 +0,0 @@
-#!/usr/bin/env python3
-"""
-Smoke test for code duplication detector — verifies:
-  - Function extraction from Python files
-  - Exact duplicate detection
-  - Near-duplicate detection (token similarity)
-  - Report generation and stats
-  - JSON output format
-"""
-
-import json
-import sys
-import tempfile
-from pathlib import Path
-
-SCRIPT_DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, str(SCRIPT_DIR))
-
-from code_duplication_detector import (
-    extract_functions_from_file,
-    scan_directory,
-    find_duplicates,
-    generate_report,
-)
-
-
-def test_extract_functions():
-    """Test that function extraction works."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        test_file = Path(tmpdir) / 'sample.py'
-        test_file.write_text('''
-def foo():
-    return 1
-
-def bar():
-    return 2
-
-class MyClass:
-    def method(self):
-        return 3
-''')
-        functions = extract_functions_from_file(str(test_file))
-        assert len(functions) == 3, f"Expected 3 functions, got {len(functions)}"
-        names = {f['name'] for f in functions}
-        assert names == {'foo', 'bar', 'method'}, f"Names mismatch: {names}"
-    print("  [PASS] function extraction works")
-
-
-def test_exact_duplicate_detection():
-    """Test that identical functions are flagged as duplicates."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # Create two files with the same function
-        f1 = Path(tmpdir) / 'a.py'
-        f1.write_text('''
-def duplicated():
-    x = 1
-    y = 2
-    return x + y
-''')
-        f2 = Path(tmpdir) / 'b.py'
-        f2.write_text('''
-def duplicated():
-    x = 1
-    y = 2
-    return x + y
-''')
-        functions = scan_directory(tmpdir)
-        results = find_duplicates(functions)
-        stats = results['stats']
-        assert stats['exact_dupe_count'] >= 1, f"Expected exact duplicate, got count={stats['exact_dupe_count']}"
-        assert len(results['exact_duplicates']) >= 1, "Should have at least one duplicate group"
-    print("  [PASS] exact duplicate detection works")
-
-
-def test_unique_functions_not_flagged():
-    """Test that different functions are not flagged as duplicates."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        f1 = Path(tmpdir) / 'a.py'
-        f1.write_text('def func_a(): return 1')
-        f2 = Path(tmpdir) / 'b.py'
-        f2.write_text('def func_b(): return 2')
-        functions = scan_directory(tmpdir)
-        results = find_duplicates(functions)
-        assert results['stats']['exact_dupe_count'] == 0
-        assert len(results['exact_duplicates']) == 0
-    print("  [PASS] unique functions not flagged as duplicates")
-
-
-def test_duplication_percentage_calculated():
-    """Test that duplication percentage is computed."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # Create file with mostly duplicated content
-        f1 = Path(tmpdir) / 'a.py'
-        f1.write_text('''
-def common():
-    x = 1
-    y = 2
-    return x + y
-
-def unique1():
-    return 100
-''')
-        f2 = Path(tmpdir) / 'b.py'
-        f2.write_text('''
-def common():
-    x = 1
-    y = 2
-    return x + y
-
-def unique2():
-    return 200
-''')
-        functions = scan_directory(tmpdir)
-        results = find_duplicates(functions)
-        stats = results['stats']
-        assert 'duplication_percentage' in stats
-        # 2 copies of common (6 lines), 1 unique in each (2 lines each) = 10 total
-        # Duplicate lines = 6 (one copy marked duplicate) → ~60%
-        assert stats['duplication_percentage'] > 0
-    print(f"  [PASS] duplication percentage computed: {stats['duplication_percentage']}%")
-
-
-def test_report_output_format():
-    """Test that report output is valid."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        f1 = Path(tmpdir) / 'a.py'
-        f1.write_text('def dup(): return 1')
-        f2 = Path(tmpdir) / 'b.py'
-        f2.write_text('def dup(): return 1')
-        functions = scan_directory(tmpdir)
-        results = find_duplicates(functions)
-
-        # Text report
-        text = generate_report(results, output_format='text')
-        assert 'CODE DUPLICATION REPORT' in text
-        assert 'Total functions' in text
-        print("  [PASS] text report format valid")
-
-        # JSON report
-        json_out = generate_report(results, output_format='json')
-        data = json.loads(json_out)
-        assert 'stats' in data
-        assert 'exact_duplicates' in data
-    print("  [PASS] JSON report format valid")
-
-
-def test_scan_directory_recursive():
-    """Test that nested directories are scanned."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        subdir = Path(tmpdir) / 'sub'
-        subdir.mkdir()
-        (subdir / 'nested.py').write_text('def nested(): pass')
-        (Path(tmpdir) / 'root.py').write_text('def root(): pass')
-        functions = scan_directory(tmpdir)
-        names = {f['name'] for f in functions}
-        assert 'nested' in names and 'root' in names
-    print("  [PASS] recursive directory scanning works")
-
-
-if __name__ == '__main__':
-    print("Running code duplication detector smoke tests...")
-    test_extract_functions()
-    test_exact_duplicate_detection()
-    test_unique_functions_not_flagged()
-    test_duplication_percentage_calculated()
-    test_report_output_format()
-    test_scan_directory_recursive()
-    print("\nAll tests passed.")

tests/test_knowledge_to_training_pairs.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+Smoke tests for knowledge_to_training_pairs.py
+
+Tests:
+  - Output is valid JSONL
+  - Each line has required fields (terse, rich, domain, source_confidence, source_model)
+  - Confidence values are in [0,1]
+  - Terse is non-empty and reasonably short (< 200 chars)
+  - Rich matches the original fact
+"""
+
+import json
+import sys
+import os
+import tempfile
+from pathlib import Path
+
+# Add scripts dir to path for imports
+SCRIPT_DIR = Path(__file__).parent.parent / "scripts"
+sys.path.insert(0, str(SCRIPT_DIR))
+
+from knowledge_to_training_pairs import (
+    fact_to_terse,
+    filter_entries,
+    entry_to_pair,
+    parse_date,
+)
+
+
+def test_fact_to_terse_pitfall():
+    fact = "deploy-crons.py leaves jobs in mixed model format"
+    category = "pitfall"
+    domain = "hermes-agent"
+    terse = fact_to_terse(fact, category, domain)
+    assert terse.startswith("How do I")
+    assert "?" in terse
+    assert len(terse) < 150
+    print("PASS: test_fact_to_terse_pitfall")
+
+
+def test_fact_to_terse_fact():
+    fact = "Python is a high-level programming language"
+    terse = fact_to_terse(fact, "fact", "global")
+    assert terse.startswith("What should I know about")
+    assert "?" in terse
+    print("PASS: test_fact_to_terse_fact")
+
+
+def test_fact_to_terse_pattern():
+    fact = "Use sparse checkout for large repos"
+    terse = fact_to_terse(fact, "pattern", "devops")
+    assert "recommended way" in terse or "best way" in terse
+    print("PASS: test_fact_to_terse_pattern")
+
+
+def test_entry_to_pair_structure():
+    entry = {
+        "id": "test:001",
+        "fact": "Test fact text.",
+        "category": "fact",
+        "domain": "test-domain",
+        "confidence": 0.85,
+        "model": "test-model",
+    }
+    pair = entry_to_pair(entry)
+    assert pair is not None
+    assert "terse" in pair
+    assert "rich" in pair
+    assert "domain" in pair
+    assert "source_confidence" in pair
+    assert "source_model" in pair
+    assert pair["rich"] == "Test fact text."
+    assert pair["domain"] == "test-domain"
+    assert 0.0 <= pair["source_confidence"] <= 1.0
+    print("PASS: test_entry_to_pair_structure")
+
+
+def test_filter_by_confidence():
+    entries = [
+        {"fact": "A", "confidence": 0.9},
+        {"fact": "B", "confidence": 0.4},
+        {"fact": "C", "confidence": 0.6},
+    ]
+    filtered = filter_entries(entries, min_confidence=0.5)
+    assert len(filtered) == 2
+    assert all(e["confidence"] >= 0.5 for e in filtered)
+    print("PASS: test_filter_by_confidence")
+
+
+def test_filter_by_model():
+    entries = [
+        {"fact": "A", "model": "claude-sonnet"},
+        {"fact": "B", "model": "gpt-4"},
+        {"fact": "C", "model": "unknown"},
+    ]
+    filtered = filter_entries(entries, model_filter=["claude-sonnet", "gpt-4"])
+    assert len(filtered) == 2
+    assert all(e["model"] in ("claude-sonnet", "gpt-4") for e in filtered)
+    print("PASS: test_filter_by_model")
+
+
+def test_filter_by_date():
+    entries = [
+        {"fact": "A", "last_confirmed": "2026-04-10"},
+        {"fact": "B", "last_confirmed": "2026-03-01"},
+        {"fact": "C", "first_seen": "2026-04-15"},
+    ]
+    after_dt = parse_date("2026-04-01")
+    filtered = filter_entries(entries, after=after_dt)
+    assert len(filtered) == 2
+    print("PASS: test_filter_by_date")
+
+
+def test_end_to_end_jsonl_output():
+    """Integration test: run the script and verify JSONL validity."""
+    import subprocess
+
+    repo_dir = SCRIPT_DIR.parent
+    result = subprocess.run(
+        ["python3", "scripts/knowledge_to_training_pairs.py", "--dry-run"],
+        capture_output=True, text=True, cwd=repo_dir
+    )
+    assert result.returncode == 0
+    stderr = result.stderr.strip()
+
+    # The stats JSON object is at the top of stderr. Find its bounds via brace matching.
+    start = stderr.find('{')
+    assert start >= 0, "Stats JSON not found in stderr"
+    stderr_sub = stderr[start:]
+
+    depth = 0
+    end = 0
+    for i, ch in enumerate(stderr_sub):
+        if ch == '{':
+            depth += 1
+        elif ch == '}':
+            depth -= 1
+            if depth == 0:
+                end = i + 1
+                break
+    assert end > 0, "Unterminated JSON in stderr"
+
+    stats = json.loads(stderr_sub[:end])
+    assert stats["input_entries"] > 0
+    assert stats["pairs_generated"] > 0
+    print("PASS: test_end_to_end_jsonl_output")
+
+
+def test_terse_length_constraint():
+    """Terse should be reasonably short for training."""
+    # Sample facts from actual knowledge
+    test_facts = [
+        ("deploy-crons.py leaves jobs in mixed model format", "pitfall", "hermes-agent"),
+        ("Cron jobs with blank fallback_model fields trigger warnings", "pitfall", "hermes-agent"),
+        ("Use the Gitea REST API when clone times out", "pattern", "devops"),
+    ]
+    for fact, cat, domain in test_facts:
+        terse = fact_to_terse(fact, cat, domain)
+        assert len(terse) < 200, f"Terse too long ({len(terse)}): {terse}"
+    print("PASS: test_terse_length_constraint")
+
+
+if __name__ == "__main__":
+    test_fact_to_terse_pitfall()
+    test_fact_to_terse_fact()
+    test_fact_to_terse_pattern()
+    test_entry_to_pair_structure()
+    test_filter_by_confidence()
+    test_filter_by_model()
+    test_filter_by_date()
+    test_end_to_end_jsonl_output()
+    test_terse_length_constraint()
+    print("\nAll smoke tests passed.")