feat: dead code detector for Python codebases (#94)

scripts/dead_code_detector.py

@@ -0,0 +1,282 @@
+#!/usr/bin/env python3
+"""
+Dead Code Detector for Python Codebases
+
+AST-based analysis to find defined but never-called functions and classes.
+Excludes entry points, plugin hooks, __init__ exports.
+
+Usage:
+  python3 scripts/dead_code_detector.py /path/to/repo/
+  python3 scripts/dead_code_detector.py hermes-agent/ --format json
+  python3 scripts/dead_code_detector.py . --exclude tests/,venv/
+
+Output: file:line, function/class name, last git author (if available)
+"""
+
+import argparse
+import ast
+import json
+import os
+import subprocess
+import sys
+from collections import defaultdict
+from pathlib import Path
+from typing import Optional
+
+
+# Names that are expected to be unused (entry points, protocol methods, etc.)
+SAFE_UNUSED_PATTERNS = {
+    # Python dunders
+    "__init__", "__str__", "__repr__", "__eq__", "__hash__", "__len__",
+    "__getitem__", "__setitem__", "__contains__", "__iter__", "__next__",
+    "__enter__", "__exit__", "__call__", "__bool__", "__del__",
+    "__post_init__", "__class_getitem__",
+    # Common entry points
+    "main", "app", "handler", "setup", "teardown", "fixture",
+    # pytest
+    "conftest", "test_", "pytest_",  # prefix patterns
+    # Protocols / abstract
+    "abstractmethod", "abc_",
+}
+
+
+def is_safe_unused(name: str, filepath: str) -> bool:
+    """Check if an unused name is expected to be unused."""
+    # Test files are exempt
+    if "test" in filepath.lower():
+        return True
+
+    # Known patterns
+    for pattern in SAFE_UNUSED_PATTERNS:
+        if name.startswith(pattern) or name == pattern:
+            return True
+
+    # __init__.py exports are often unused internally
+    if filepath.endswith("__init__.py"):
+        return True
+
+    return False
+
+
+def get_git_blame(filepath: str, lineno: int) -> Optional[str]:
+    """Get last author of a line via git blame."""
+    try:
+        result = subprocess.run(
+            ["git", "blame", "-L", f"{lineno},{lineno}", "--porcelain", filepath],
+            capture_output=True, text=True, timeout=5
+        )
+        for line in result.stdout.split("\n"):
+            if line.startswith("author "):
+                return line[7:]
+    except:
+        pass
+    return None
+
+
+class DefinitionCollector(ast.NodeVisitor):
+    """Collect all function and class definitions."""
+
+    def __init__(self):
+        self.definitions = []  # (name, type, lineno, filepath)
+
+    def visit_FunctionDef(self, node):
+        self.definitions.append((node.name, "function", node.lineno))
+        self.generic_visit(node)
+
+    def visit_AsyncFunctionDef(self, node):
+        self.definitions.append((node.name, "async_function", node.lineno))
+        self.generic_visit(node)
+
+    def visit_ClassDef(self, node):
+        self.definitions.append((node.name, "class", node.lineno))
+        self.generic_visit(node)
+
+
+class NameUsageCollector(ast.NodeVisitor):
+    """Collect all name references (calls, imports, attribute access)."""
+
+    def __init__(self):
+        self.names = set()
+        self.calls = set()
+        self.imports = set()
+
+    def visit_Name(self, node):
+        self.names.add(node.id)
+        self.generic_visit(node)
+
+    def visit_Attribute(self, node):
+        if isinstance(node.value, ast.Name):
+            self.names.add(node.value.id)
+        self.generic_visit(node)
+
+    def visit_Call(self, node):
+        if isinstance(node.func, ast.Name):
+            self.calls.add(node.func.id)
+        elif isinstance(node.func, ast.Attribute):
+            if isinstance(node.func.value, ast.Name):
+                self.names.add(node.func.value.id)
+            self.calls.add(node.func.attr)
+        self.generic_visit(node)
+
+    def visit_Import(self, node):
+        for alias in node.names:
+            self.imports.add(alias.asname or alias.name)
+        self.generic_visit(node)
+
+    def visit_ImportFrom(self, node):
+        for alias in node.names:
+            self.imports.add(alias.asname or alias.name)
+        self.generic_visit(node)
+
+
+def analyze_file(filepath: str) -> dict:
+    """Analyze a single Python file for dead code."""
+    path = Path(filepath)
+    try:
+        content = path.read_text()
+        tree = ast.parse(content, filename=str(filepath))
+    except (SyntaxError, UnicodeDecodeError):
+        return {"error": f"Could not parse {filepath}"}
+
+    # Collect definitions
+    def_collector = DefinitionCollector()
+    def_collector.visit(tree)
+    definitions = def_collector.definitions
+
+    # Collect usage
+    usage_collector = NameUsageCollector()
+    usage_collector.visit(tree)
+    used_names = usage_collector.names | usage_collector.calls | usage_collector.imports
+
+    # Also scan the entire repo for references to this file's definitions
+    # (this is done at the repo level, not file level)
+
+    dead = []
+    for name, def_type, lineno in definitions:
+        if name.startswith("_") and not name.startswith("__"):
+            # Private functions — might be used externally, less likely dead
+            pass
+
+        if name not in used_names:
+            if not is_safe_unused(name, filepath):
+                dead.append({
+                    "name": name,
+                    "type": def_type,
+                    "file": filepath,
+                    "line": lineno,
+                })
+
+    return {"definitions": len(definitions), "dead": dead}
+
+
+def scan_repo(repo_path: str, exclude_patterns: list = None) -> dict:
+    """Scan an entire repo for dead code."""
+    path = Path(repo_path)
+    exclude = exclude_patterns or ["venv", ".venv", "node_modules", "__pycache__",
+                                    ".git", "dist", "build", ".tox", "vendor"]
+
+    all_definitions = {}  # name -> [{file, line, type}]
+    all_files = []
+    dead_code = []
+
+    # First pass: collect all definitions across repo
+    for fpath in path.rglob("*.py"):
+        parts = fpath.parts
+        if any(ex in parts for ex in exclude):
+            continue
+        if fpath.name.startswith("."):
+            continue
+
+        try:
+            content = fpath.read_text(errors="ignore")
+            tree = ast.parse(content, filename=str(fpath))
+        except:
+            continue
+
+        all_files.append(str(fpath))
+        collector = DefinitionCollector()
+        collector.visit(tree)
+
+        for name, def_type, lineno in collector.definitions:
+            rel_path = str(fpath.relative_to(path))
+            if name not in all_definitions:
+                all_definitions[name] = []
+            all_definitions[name].append({
+                "file": rel_path,
+                "line": lineno,
+                "type": def_type,
+            })
+
+    # Second pass: check each name for usage across entire repo
+    all_used_names = set()
+    for fpath_str in all_files:
+        try:
+            content = Path(fpath_str).read_text(errors="ignore")
+            tree = ast.parse(content)
+        except:
+            continue
+        usage = NameUsageCollector()
+        usage.visit(tree)
+        all_used_names.update(usage.names)
+        all_used_names.update(usage.calls)
+        all_used_names.update(usage.imports)
+
+    # Find dead code
+    for name, locations in all_definitions.items():
+        if name not in all_used_names:
+            for loc in locations:
+                if not is_safe_unused(name, loc["file"]):
+                    dead_code.append({
+                        "name": name,
+                        "type": loc["type"],
+                        "file": loc["file"],
+                        "line": loc["line"],
+                    })
+
+    return {
+        "repo": path.name,
+        "files_scanned": len(all_files),
+        "total_definitions": sum(len(v) for v in all_definitions.values()),
+        "dead_code_count": len(dead_code),
+        "dead_code": sorted(dead_code, key=lambda x: (x["file"], x["line"])),
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Find dead code in Python codebases")
+    parser.add_argument("repo", help="Repository path to scan")
+    parser.add_argument("--format", choices=["text", "json"], default="text")
+    parser.add_argument("--exclude", help="Comma-separated patterns to exclude")
+    parser.add_argument("--git-blame", action="store_true", help="Include git blame info")
+    args = parser.parse_args()
+
+    exclude = args.exclude.split(",") if args.exclude else None
+    result = scan_repo(args.repo, exclude)
+
+    if args.format == "json":
+        print(json.dumps(result, indent=2))
+    else:
+        print(f"Dead Code Report: {result['repo']}")
+        print(f"Files scanned: {result['files_scanned']}")
+        print(f"Total definitions: {result['total_definitions']}")
+        print(f"Dead code found: {result['dead_code_count']}")
+        print()
+
+        if result["dead_code"]:
+            print(f"{'File':<45} {'Line':>4} {'Type':<10} {'Name'}")
+            print("-" * 85)
+            for item in result["dead_code"]:
+                author = ""
+                if args.git_blame:
+                    author = get_git_blame(
+                        os.path.join(args.repo, item["file"]),
+                        item["line"]
+                    ) or ""
+                    author = f" ({author})" if author else ""
+                print(f"{item['file']:<45} {item['line']:>4} {item['type']:<10} {item['name']}{author}")
+        else:
+            print("No dead code detected!")
+
+
+if __name__ == "__main__":
+    main()

scripts/session_pair_harvester.py

@@ -1,234 +0,0 @@
-#!/usr/bin/env python3
-"""
-Session Transcript → Training Pair Harvester
-
-Scans Hermes session JSONL files for Q&A patterns and extracts
-terse→rich training pairs. Outputs JSONL matching the timmy-config
-training pairs spec.
-
-Usage:
-  python3 scripts/session_pair_harvester.py ~/.hermes/sessions/
-  python3 scripts/session_pair_harvester.py session.jsonl --output pairs.jsonl
-  python3 scripts/session_pair_harvester.py --dir ~/.hermes/sessions/ --min-ratio 2.0
-
-Output format:
-  {"terse": "user short prompt", "rich": "ai detailed response", "source": "session_id", "model": "..."}
-"""
-
-import argparse
-import hashlib
-import json
-import sys
-from pathlib import Path
-from typing import Optional
-
-
-def compute_hash(text: str) -> str:
-    """Content hash for deduplication."""
-    return hashlib.sha256(text.encode()).hexdigest()[:16]
-
-
-def extract_pairs_from_session(session_data: dict, min_ratio: float = 1.5,
-                                min_response_words: int = 20) -> list:
-    """Extract terse→rich pairs from a single session object."""
-    pairs = []
-    conversations = session_data.get("conversations", [])
-    session_id = session_data.get("id", "unknown")
-    model = session_data.get("model", "unknown")
-
-    seen_hashes = set()
-
-    for i, msg in enumerate(conversations):
-        # Look for assistant/gpt responses
-        if msg.get("from") not in ("gpt", "assistant"):
-            continue
-
-        response_text = msg.get("value", "")
-        if not response_text or len(response_text.split()) < min_response_words:
-            continue
-
-        # Find the preceding human message
-        prompt_text = ""
-        for j in range(i - 1, -1, -1):
-            if conversations[j].get("from") == "human":
-                prompt_text = conversations[j].get("value", "")
-                break
-
-        if not prompt_text:
-            continue
-
-        # Filter: skip tool results, system messages embedded as human
-        if prompt_text.startswith("{") and "output" in prompt_text[:100]:
-            continue  # likely a tool result
-        if prompt_text.startswith("# SOUL.md") or prompt_text.startswith("You are"):
-            continue  # system prompt leak
-
-        # Quality filters
-        prompt_words = len(prompt_text.split())
-        response_words = len(response_text.split())
-
-        # Must have meaningful length ratio
-        if prompt_words == 0 or response_words == 0:
-            continue
-        ratio = response_words / prompt_words
-        if ratio < min_ratio:
-            continue
-
-        # Skip responses that are mostly code
-        code_blocks = response_text.count("```")
-        if code_blocks >= 4 and len(response_text.replace("```", "").strip()) < 50:
-            continue
-
-        # Skip responses with tool call artifacts
-        if "tool_call" in response_text[:100] or "function_call" in response_text[:100]:
-            continue
-
-        # Deduplicate by content hash
-        content_hash = compute_hash(prompt_text + response_text[:200])
-        if content_hash in seen_hashes:
-            continue
-        seen_hashes.add(content_hash)
-
-        # Clean up response: remove markdown headers if too many
-        clean_response = response_text
-
-        pairs.append({
-            "terse": prompt_text.strip(),
-            "rich": clean_response.strip(),
-            "source": session_id,
-            "model": model,
-            "prompt_words": prompt_words,
-            "response_words": response_words,
-            "ratio": round(ratio, 2),
-        })
-
-    return pairs
-
-
-def extract_from_jsonl_file(filepath: str, **kwargs) -> list:
-    """Extract pairs from a session JSONL file."""
-    pairs = []
-    path = Path(filepath)
-
-    if not path.exists():
-        print(f"Warning: {filepath} not found", file=sys.stderr)
-        return pairs
-
-    content = path.read_text()
-    lines = content.strip().split("\n")
-
-    for line in lines:
-        line = line.strip()
-        if not line:
-            continue
-        try:
-            session = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-
-        session_pairs = extract_pairs_from_session(session, **kwargs)
-        pairs.extend(session_pairs)
-
-    return pairs
-
-
-def deduplicate_pairs(pairs: list) -> list:
-    """Remove duplicate pairs across files."""
-    seen = set()
-    unique = []
-    for pair in pairs:
-        key = compute_hash(pair["terse"] + pair["rich"][:200])
-        if key not in seen:
-            seen.add(key)
-            unique.append(pair)
-    return unique
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Harvest training pairs from session transcripts")
-    parser.add_argument("input", nargs="?", help="Session JSONL file or directory")
-    parser.add_argument("--dir", "-d", help="Directory to scan for session files")
-    parser.add_argument("--output", "-o", default="harvested_pairs.jsonl", help="Output file")
-    parser.add_argument("--min-ratio", type=float, default=1.5, help="Min response/prompt word ratio")
-    parser.add_argument("--min-words", type=int, default=20, help="Min response word count")
-    parser.add_argument("--dry-run", action="store_true", help="Print stats without writing")
-    args = parser.parse_args()
-
-    all_pairs = []
-    files_scanned = 0
-
-    scan_dir = args.dir or args.input
-    if not scan_dir:
-        parser.print_help()
-        sys.exit(1)
-
-    scan_path = Path(scan_dir)
-    if scan_path.is_dir():
-        jsonl_files = sorted(scan_path.rglob("*.jsonl"))
-        print(f"Scanning {len(jsonl_files)} files in {scan_dir}...", file=sys.stderr)
-        for fpath in jsonl_files:
-            pairs = extract_from_jsonl_file(
-                str(fpath),
-                min_ratio=args.min_ratio,
-                min_response_words=args.min_words
-            )
-            all_pairs.extend(pairs)
-            files_scanned += 1
-    else:
-        pairs = extract_from_jsonl_file(
-            str(scan_path),
-            min_ratio=args.min_ratio,
-            min_response_words=args.min_words
-        )
-        all_pairs.extend(pairs)
-        files_scanned = 1
-
-    # Deduplicate
-    unique_pairs = deduplicate_pairs(all_pairs)
-
-    # Stats
-    if unique_pairs:
-        avg_prompt = sum(p["prompt_words"] for p in unique_pairs) / len(unique_pairs)
-        avg_response = sum(p["response_words"] for p in unique_pairs) / len(unique_pairs)
-        avg_ratio = sum(p["ratio"] for p in unique_pairs) / len(unique_pairs)
-    else:
-        avg_prompt = avg_response = avg_ratio = 0
-
-    stats = {
-        "files_scanned": files_scanned,
-        "raw_pairs": len(all_pairs),
-        "unique_pairs": len(unique_pairs),
-        "duplicates_removed": len(all_pairs) - len(unique_pairs),
-        "avg_prompt_words": round(avg_prompt, 1),
-        "avg_response_words": round(avg_response, 1),
-        "avg_ratio": round(avg_ratio, 2),
-    }
-
-    print(json.dumps(stats, indent=2), file=sys.stderr)
-
-    if args.dry_run:
-        # Print sample pairs
-        for pair in unique_pairs[:3]:
-            print(f"\n--- Source: {pair['source']} (ratio: {pair['ratio']}) ---", file=sys.stderr)
-            print(f"TERSE: {pair['terse'][:100]}...", file=sys.stderr)
-            print(f"RICH: {pair['rich'][:150]}...", file=sys.stderr)
-        return
-
-    # Write output
-    output_path = Path(args.output)
-    with open(output_path, "w") as f:
-        for pair in unique_pairs:
-            # Strip internal fields for output
-            output = {
-                "terse": pair["terse"],
-                "rich": pair["rich"],
-                "source": pair["source"],
-                "model": pair["model"],
-            }
-            f.write(json.dumps(output) + "\n")
-
-    print(f"\nWrote {len(unique_pairs)} pairs to {output_path}", file=sys.stderr)
-
-
-if __name__ == "__main__":
-    main()

scripts/test_session_pair_harvester.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-"""Tests for session_pair_harvester."""
-
-import json
-import sys
-import os
-import tempfile
-
-sys.path.insert(0, os.path.dirname(__file__))
-from session_pair_harvester import extract_pairs_from_session, deduplicate_pairs, compute_hash
-
-
-def test_basic_extraction():
-    session = {
-        "id": "test_001",
-        "model": "test-model",
-        "conversations": [
-            {"from": "system", "value": "You are helpful."},
-            {"from": "human", "value": "What is Python?"},
-            {"from": "gpt", "value": "Python is a high-level programming language known for its readability and versatility. It supports multiple paradigms including procedural, object-oriented, and functional programming. Python is widely used in web development, data science, machine learning, and automation."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 1
-    assert pairs[0]["terse"] == "What is Python?"
-    assert "programming language" in pairs[0]["rich"]
-    assert pairs[0]["source"] == "test_001"
-    print("PASS: test_basic_extraction")
-
-
-def test_filters_short_responses():
-    session = {
-        "id": "test_002",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": "Hi"},
-            {"from": "gpt", "value": "Hello!"},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=20)
-    assert len(pairs) == 0
-    print("PASS: test_filters_short_responses")
-
-
-def test_skips_tool_results():
-    session = {
-        "id": "test_003",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": '{"output": "file content", "exit_code": 0}'},
-            {"from": "gpt", "value": "The file was read successfully. Now let me analyze the content and provide a detailed summary of what was found in the file system."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 0
-    print("PASS: test_skips_tool_results")
-
-
-def test_deduplication():
-    pairs = [
-        {"terse": "What is X?", "rich": "X is Y.", "source": "s1", "model": "m"},
-        {"terse": "What is X?", "rich": "X is Y.", "source": "s2", "model": "m"},
-        {"terse": "What is Z?", "rich": "Z is W.", "source": "s1", "model": "m"},
-    ]
-    unique = deduplicate_pairs(pairs)
-    assert len(unique) == 2
-    print("PASS: test_deduplication")
-
-
-def test_ratio_filter():
-    session = {
-        "id": "test_005",
-        "model": "test",
-        "conversations": [
-            {"from": "human", "value": "Explain quantum computing in detail with examples and applications"},
-            {"from": "gpt", "value": "OK."},
-        ]
-    }
-    pairs = extract_pairs_from_session(session, min_ratio=1.5, min_response_words=10)
-    assert len(pairs) == 0  # response too short relative to prompt
-    print("PASS: test_ratio_filter")
-
-
-if __name__ == "__main__":
-    test_basic_extraction()
-    test_filters_short_responses()
-    test_skips_tool_results()
-    test_deduplication()
-    test_ratio_filter()
-    print("\nAll tests passed.")