feat: add code duplication detector (#162)

Detect duplicate functions/blocks across Python files.
Reports duplication percentage and outputs JSON report.
Closes #162.

scripts/code_duplication_detector.py

@@ -0,0 +1,366 @@
+#!/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/test_code_duplication_detector.py

@@ -0,0 +1,168 @@
+#!/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.")

scripts/transcript_harvester.py

@@ -1,377 +0,0 @@
-#!/usr/bin/env python3
-"""
-transcript_harvester.py — Rule-based knowledge extraction from Hermes session transcripts.
-
-Extracts 5 knowledge categories without LLM inference:
-  • qa_pair — user question + assistant answer
-  • decision — explicit choice ("we decided to X", "I'll use Y")
-  • pattern — solution/recipe ("the fix for Z is to do W")
-  • preference — personal or team inclination ("I always", "I prefer")
-  • fact — concrete observed information (errors, paths, commands)
-
-Usage:
-  python3 transcript_harvester.py --session ~/.hermes/sessions/session_xxx.jsonl
-  python3 transcript_harvester.py --batch --sessions-dir ~/.hermes/sessions --limit 50
-  python3 transcript_harvester.py --session session.jsonl --output knowledge/transcripts/
-"""
-
-import argparse
-import json
-import re
-import sys
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-# Import session_reader from the same scripts directory
-SCRIPT_DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, str(SCRIPT_DIR))
-from session_reader import read_session
-
-
-# --- Pattern matchers --------------------------------------------------------
-
-DECISION_PATTERNS = [
-    r"\b(we\s+(?:decided|chose|agreed|will|are going)\s+to\s+.*)",
-    r"\b(I\s+will\s+use|I\s+choose|I\s+am going\s+to)\s+.*",
-    r"\b(let's\s+(?:use|go\s+with|do|try))\s+.*",
-    r"\b(the\s+(?:decision|choice)\s+is)\s+.*",
-    r"\b(I'll\s+implement|I'll\s+deploy|I'll\s+create)\s+.*",
-]
-
-PATTERN_PATTERNS = [
-    r"\b(the\s+fix\s+for\s+.*\s+is\s+to\s+.*)",
-    r"\b(solution:?\s+.*)",
-    r"\b(approach:?\s+.*)",
-    r"\b(procedure:?\s+.*)",
-    r"\b(to\s+resolve\s+this.*?,\s+.*)",
-    r"\b(used\s+.*\s+to\s+.*)",  # "used X to do Y"
-    r"\b(by\s+doing\s+.*\s+we\s+.*)",
-    r"\b(Here's\s+the\s+.*\s+process:?)",  # "Here's the deployment process:"
-    r"\b(The\s+steps\s+are:?)",
-    r"\b(steps\s+to\s+.*:?)",
-    r"\b(Implementation\s+plan:?)",
-    r"\b(\d+\.\s+.*\n\d+\.)",  # numbered multi-step (at least two steps detected by newlines)
-]
-
-PREFERENCE_PATTERNS = [
-    r"\b(I\s+(?:always|never|prefer|usually|typically|generally)\s+.*)",
-    r"\b(I\s+like\s+.*)",
-    r"\b(My\s+preference\s+is\s+.*)",
-    r"\b(Alexander\s+(?:prefers|always|never).*)",
-    r"\b(We\s+always\s+.*)",
-]
-
-ERROR_PATTERNS = [
-    r"\b(error|failed|fatal|exception|denied|could\s+not|couldn't)\b.*",
-]
-
-# For a fix that follows an error within 2 messages
-FIX_INDICATORS = [
-    r"\b(fixed|resolved|added|generated|created|corrected|worked)\b",
-    r"\b(the\s+key\s+is|solution\s+was|generate\s+a\s+new)\b",
-]
-
-
-def is_decision(text: str) -> bool:
-    for p in DECISION_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_pattern(text: str) -> bool:
-    for p in PATTERN_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_preference(text: str) -> bool:
-    for p in PREFERENCE_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_error(text: str) -> bool:
-    for p in ERROR_PATTERNS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-def is_fix_indicator(text: str) -> bool:
-    for p in FIX_INDICATORS:
-        if re.search(p, text, re.IGNORECASE):
-            return True
-    return False
-
-
-# --- Extractors --------------------------------------------------------------
-
-def extract_qa_pair(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a question→answer pair: user question followed by assistant answer."""
-    if idx + 1 >= len(messages):
-        return None
-    curr = messages[idx]
-    nxt = messages[idx + 1]
-    if curr.get('role') != 'user' or nxt.get('role') != 'assistant':
-        return None
-    question = curr.get('content', '').strip()
-    answer = nxt.get('content', '').strip()
-    if not question or not answer:
-        return None
-    # Must be a real question (ends with ? or starts with WH-)
-    if not (question.endswith('?') or re.match(r'^(how|what|why|when|where|who|which|can|do|is|are)', question, re.IGNORECASE)):
-        return None
-    # Skip very short answers ("OK", "Yes")
-    if len(answer.split()) < 3:
-        return None
-    return {
-        "type": "qa_pair",
-        "question": question,
-        "answer": answer,
-        "timestamp": curr.get('timestamp', ''),
-    }
-
-
-def extract_decision(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a decision statement from assistant or user message."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_decision(text):
-        return None
-    return {
-        "type": "decision",
-        "decision": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_pattern(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a pattern or solution description."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_pattern(text):
-        return None
-    return {
-        "type": "pattern",
-        "pattern": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_preference(messages: list[dict], idx: int) -> Optional[dict]:
-    """Extract a stated preference."""
-    msg = messages[idx]
-    text = msg.get('content', '').strip()
-    if not is_preference(text):
-        return None
-    return {
-        "type": "preference",
-        "preference": text,
-        "by": msg.get('role', 'unknown'),
-        "timestamp": msg.get('timestamp', ''),
-    }
-
-
-def extract_error_fix(messages: list[dict], idx: int) -> Optional[dict]:
-    """
-    Link an error to its fix. Catch two patterns:
-    1. Error statement followed by explicit fix indicator ("fixed", "resolved")
-    2. Error statement followed by a decision statement that fixes it ("I'll generate", "I'll add")
-    """
-    msg = messages[idx]
-    if not is_error(msg.get('content', '')):
-        return None
-    error_text = msg.get('content', '').strip()
-    
-    window = min(idx + 8, len(messages))
-    for j in range(idx + 1, window):
-        follow_up = messages[j]
-        follow_text = follow_up.get('content', '').strip()
-        # Check for explicit fix indicators
-        if is_fix_indicator(follow_text):
-            return {
-                "type": "error_fix",
-                "error": error_text,
-                "fix": follow_text,
-                "error_timestamp": msg.get('timestamp', ''),
-                "fix_timestamp": follow_up.get('timestamp', ''),
-            }
-        # Check for fix decision: "I'll <action>", "Let's <action>", "We need to <action>"
-        if re.match(r"^(I'll|I will|Let's|We (will|should|need to))\s+\w+", follow_text, re.IGNORECASE):
-            return {
-                "type": "error_fix",
-                "error": error_text,
-                "fix": follow_text,
-                "error_timestamp": msg.get('timestamp', ''),
-                "fix_timestamp": follow_up.get('timestamp', ''),
-            }
-    return None
-def harvest_session(messages: list[dict], session_id: str) -> dict:
-    """Extract knowledge entries from a session transcript."""
-    entries = []
-    n = len(messages)
-
-    for i in range(n):
-        # QA pairs
-        qa = extract_qa_pair(messages, i)
-        if qa:
-            qa['session_id'] = session_id
-            entries.append(qa)
-
-        # Decisions
-        dec = extract_decision(messages, i)
-        if dec:
-            dec['session_id'] = session_id
-            entries.append(dec)
-
-        # Patterns
-        pat = extract_pattern(messages, i)
-        if pat:
-            pat['session_id'] = session_id
-            entries.append(pat)
-
-        # Preferences
-        pref = extract_preference(messages, i)
-        if pref:
-            pref['session_id'] = session_id
-            entries.append(pref)
-
-        # Error/fix pairs (spanning multiple messages)
-        ef = extract_error_fix(messages, i)
-        if ef:
-            ef['session_id'] = session_id
-            entries.append(ef)
-
-    return {
-        "session_id": session_id,
-        "message_count": n,
-        "entries": entries,
-        "counts": {
-            "qa_pair": sum(1 for e in entries if e['type'] == 'qa_pair'),
-            "decision": sum(1 for e in entries if e['type'] == 'decision'),
-            "pattern": sum(1 for e in entries if e['type'] == 'pattern'),
-            "preference": sum(1 for e in entries if e['type'] == 'preference'),
-            "error_fix": sum(1 for e in entries if e['type'] == 'error_fix'),
-        }
-    }
-
-
-def write_json_output(results: list[dict], output_path: Path):
-    """Write aggregated results to JSON."""
-    all_entries = []
-    summary = {"sessions": 0}
-    for r in results:
-        summary['sessions'] += 1
-        all_entries.extend(r['entries'])
-    
-    output = {
-        "harvester": "transcript_harvester",
-        "generated_at": datetime.now(timezone.utc).isoformat(),
-        "summary": summary,
-        "total_entries": len(all_entries),
-        "entries": all_entries,
-    }
-    output_path.write_text(json.dumps(output, indent=2, ensure_ascii=False))
-    return output
-
-
-def write_report(results: list[dict], report_path: Path):
-    """Write a human-readable markdown report."""
-    lines = []
-    lines.append("# Transcript Harvester Report")
-    lines.append(f"Generated: {datetime.now(timezone.utc).isoformat()}")
-    lines.append(f"Sessions processed: {len(results)}")
-    
-    totals = {cat: 0 for cat in ['qa_pair', 'decision', 'pattern', 'preference', 'error_fix']}
-    for r in results:
-        for cat, cnt in r['counts'].items():
-            totals[cat] += cnt  # BUG: should be += cnt
-    
-    lines.append("\n## Extracted Knowledge by Category\n")
-    for cat, cnt in totals.items():
-        lines.append(f"- **{cat}**: {cnt}")
-    
-    lines.append("\n## Sample Entries\n")
-    for r in results:
-        for entry in r['entries'][:3]:
-            lines.append(f"\n### {entry['type'].upper()} ({r['session_id']})\n")
-            if entry['type'] == 'qa_pair':
-                lines.append(f"**Q:** {entry['question']}\n")
-                lines.append(f"**A:** {entry['answer']}\n")
-            elif entry['type'] == 'decision':
-                lines.append(f"**Decision:** {entry['decision']}\n")
-                lines.append(f"By: {entry['by']}\n")
-            elif entry['type'] == 'pattern':
-                lines.append(f"**Pattern:** {entry['pattern']}\n")
-            elif entry['type'] == 'preference':
-                lines.append(f"**Preference:** {entry['preference']}\n")
-            elif entry['type'] == 'error_fix':
-                lines.append(f"**Error:** {entry['error']}\n")
-                lines.append(f"**Fixed by:** {entry['fix']}\n")
-    
-    report_path.write_text("\n".join(lines))
-
-
-def find_recent_sessions(sessions_dir: Path, limit: int = 50) -> list[Path]:
-    """Find up to `limit` most recent .jsonl session files."""
-    sessions = sorted(sessions_dir.glob("*.jsonl"), reverse=True)
-    return sessions[:limit] if limit > 0 else sessions
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Harvest knowledge from session transcripts")
-    parser.add_argument('--session', help='Single session JSONL file')
-    parser.add_argument('--batch', action='store_true', help='Batch mode')
-    parser.add_argument('--sessions-dir', default=str(Path.home() / '.hermes' / 'sessions'),
-                        help='Directory of session files')
-    parser.add_argument('--output', default='knowledge/transcripts',
-                        help='Output directory (default: knowledge/transcripts)')
-    parser.add_argument('--limit', type=int, default=50,
-                        help='Max sessions to process in batch (default: 50)')
-    
-    args = parser.parse_args()
-    output_dir = Path(args.output)
-    output_dir.mkdir(parents=True, exist_ok=True)
-    
-    results = []
-    
-    if args.session:
-        messages = read_session(args.session)
-        session_id = Path(args.session).stem
-        results.append(harvest_session(messages, session_id))
-    elif args.batch:
-        sessions_dir = Path(args.sessions_dir)
-        sessions = find_recent_sessions(sessions_dir, args.limit)
-        print(f"Processing {len(sessions)} sessions...")
-        for sf in sessions:
-            messages = read_session(str(sf))
-            results.append(harvest_session(messages, sf.stem))
-    else:
-        parser.print_help()
-        sys.exit(1)
-    
-    # Write outputs
-    json_path = output_dir / "transcript_knowledge.json"
-    report_path = output_dir / "transcript_report.md"
-    
-    output = write_json_output(results, json_path)
-    write_report(results, report_path)
-    
-    print(f"\nDone: {output['total_entries']} entries from {len(results)} sessions")
-    print(f"Output: {json_path}")
-    print(f"Report: {report_path}")
-    
-    # Print category totals
-    totals = {}
-    for r in results:
-        for cat, cnt in r['counts'].items():
-            totals[cat] = totals.get(cat, 0) + cnt
-    print("\nCategory counts:")
-    for cat, cnt in sorted(totals.items()):
-        print(f"  {cat}: {cnt}")
-
-
-if __name__ == '__main__':
-    main()