test(scanner): unit tests for github_trending_scanner

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/github_trending_scanner.py

@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+"""GitHub Trending Scanner — Scan trending repos in AI/ML.
+
+Extracts: repo description, stars, key features (topics, inferred highlights).
+Filters by language and/or topic. Outputs dated JSON for daily scan pipeline.
+
+Usage:
+    python3 github_trending_scanner.py --language python --topic ai --output metrics/trending
+    python3 github_trending_scanner.py --topic machine-learning --limit 50
+    python3 github_trending_scanner.py --language rust --topic artificial-intelligence
+"""
+
+import argparse
+import json
+import os
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional, List, Dict
+import urllib.request
+import urllib.parse
+import urllib.error
+
+GITHUB_API_BASE = os.environ.get("GITHUB_API_BASE", "https://api.github.com")
+DEFAULT_OUTPUT_DIR = os.environ.get("TRENDING_OUTPUT_DIR", "metrics/trending")
+DEFAULT_LIMIT = int(os.environ.get("TRENDING_LIMIT", "30"))
+DEFAULT_MIN_STARS = int(os.environ.get("TRENDING_MIN_STARS", "1000"))
+
+
+def fetch_trending_repos(
+    language: Optional[str] = None,
+    topic: Optional[str] = None,
+    min_stars: int = DEFAULT_MIN_STARS,
+    limit: int = DEFAULT_LIMIT,
+) -> List[Dict]:
+    """Fetch trending-like repositories from GitHub using the search API.
+
+    GitHub's public search API is unauthenticated-rate-limited (60 req/hr).
+    This function retries on rate-limit backoff and falls back gracefully.
+    """
+    # Build search query: stars threshold + optional language/topic filters
+    query = f"stars:>{min_stars}"
+    if language:
+        query += f" language:{language}"
+    if topic:
+        query += f" topic:{topic}"
+
+    # Sort by stars descending as a proxy for trending/popular
+    params = {
+        "q": query,
+        "sort": "stars",
+        "order": "desc",
+        "per_page": min(limit, 100),  # GitHub max per_page is 100
+    }
+    url = f"{GITHUB_API_BASE}/search/repositories?{urllib.parse.urlencode(params)}"
+
+    headers = {
+        "Accept": "application/vnd.github.v3+json",
+        "User-Agent": "Sovereign-Trending-Scanner/1.0",
+    }
+
+    for attempt in range(3):
+        try:
+            req = urllib.request.Request(url, headers=headers)
+            with urllib.request.urlopen(req, timeout=30) as resp:
+                if resp.status != 200:
+                    raise RuntimeError(f"GitHub API returned {resp.status}")
+                data = json.loads(resp.read().decode("utf-8"))
+                return data.get("items", [])[:limit]
+        except urllib.error.HTTPError as e:
+            if e.code == 403:
+                # Check for rate limit message
+                body = e.read().decode("utf-8", errors="replace").lower()
+                if "rate limit" in body or "api rate limit exceeded" in body:
+                    reset_ts = int(e.headers.get("X-RateLimit-Reset", 0))
+                    wait_seconds = max(5, reset_ts - int(time.time()) + 5)
+                    print(f"Rate limit exceeded — waiting {wait_seconds}s (attempt {attempt+1}/3)...", file=sys.stderr)
+                    time.sleep(wait_seconds)
+                    continue
+            print(f"ERROR: GitHub API request failed: {e} — {e.read().decode('utf-8', errors='replace')[:200]}", file=sys.stderr)
+            return []
+        except Exception as e:
+            if attempt < 2:
+                backoff = 2 ** attempt
+                print(f"WARNING: Fetch attempt {attempt+1} failed: {e} — retrying in {backoff}s", file=sys.stderr)
+                time.sleep(backoff)
+                continue
+            print(f"ERROR: All fetch attempts failed: {e}", file=sys.stderr)
+            return []
+
+    return []
+
+
+def extract_repo_features(repo_data: Dict) -> Dict:
+    """Extract structured fields for a trending repo."""
+    description = (repo_data.get("description") or "").strip()
+    topics = repo_data.get("topics", [])
+
+    # Infer key features from description and topics
+    features = infer_features(description, topics)
+
+    return {
+        "name": repo_data.get("full_name", ""),
+        "description": description,
+        "stars": repo_data.get("stargazers_count", 0),
+        "forks": repo_data.get("forks_count", 0),
+        "open_issues": repo_data.get("open_issues_count", 0),
+        "language": repo_data.get("language", ""),
+        "topics": topics,
+        "url": repo_data.get("html_url", ""),
+        "created_at": repo_data.get("created_at", ""),
+        "updated_at": repo_data.get("updated_at", ""),
+        "key_features": features,
+        "scanned_at": datetime.now(timezone.utc).isoformat(),
+    }
+
+
+def infer_features(description: str, topics: List[str]) -> List[str]:
+    """Infer notable capabilities/features from repo metadata.
+
+    Looks for AI/ML-relevant capabilities in topics and description.
+    """
+    features = []
+    text = (description + " " + " ".join(topics)).lower()
+
+    # Domain capabilities (keys normalized to lowercase for consistency)
+    capability_keywords = {
+        "fine-tuning": ["fine-tun", "finetun"],
+        "agent framework": ["agent"],
+        "local/offline": ["local", "on-device", "offline"],
+        "quantized models": ["quantized", "quantization", "gguf", "gptq"],
+        "vision": ["vision", "multimodal", "image", "visual"],
+        "speech/audio": ["speech", "audio", "whisper", "tts"],
+        "retrieval/rag": ["rag", "retrieval", "embedding", "vector"],
+        "training": ["train", "training", "sft", "dpo"],
+        "gui/playground": ["gui", "playground", "webui", "interface"],
+        "sota": ["state-of-the-art", "sota", "latest"],
+    }
+
+    for label, keywords in capability_keywords.items():
+        if any(kw in text for kw in keywords):
+            features.append(label)
+
+    # Also include non-generic topics as features
+    generic_topics = {"ai", "ml", "machine-learning", "deep-learning", "llm", "python", "pytorch", "tensorflow"}
+    for topic in topics:
+        if topic.lower() not in generic_topics:
+            features.append(topic)
+
+    # Deduplicate while preserving order, return up to 10
+    seen = set()
+    unique = []
+    for f in features:
+        key = f.lower()
+        if key not in seen:
+            seen.add(key)
+            unique.append(f)
+    return unique[:10]
+
+
+def save_trending(repos: List[Dict], output_dir: str = "metrics/trending") -> str:
+    """Save trending results to a dated JSON file.
+
+    Returns the path of the written file.
+    """
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+    filename = output_path / f"github-trending-{date_str}.json"
+
+    output_data = {
+        "scanned_at": datetime.now(timezone.utc).isoformat(),
+        "count": len(repos),
+        "repos": repos,
+    }
+
+    with open(filename, "w") as f:
+        json.dump(output_data, f, indent=2, ensure_ascii=False)
+
+    return str(filename)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Scan GitHub trending repositories in AI/ML"
+    )
+    parser.add_argument(
+        "--language",
+        help="Filter by programming language (e.g., python, rust, go)",
+    )
+    parser.add_argument(
+        "--topic",
+        help="Filter by GitHub topic (e.g., ai, machine-learning, llm)",
+    )
+    parser.add_argument(
+        "--since",
+        default="daily",
+        choices=["daily", "weekly", "monthly"],
+        help="Trending period (daily/weekly/monthly) — informational only",
+    )
+    parser.add_argument(
+        "--output",
+        default="metrics/trending",
+        help="Output directory for results (default: metrics/trending)",
+    )
+    parser.add_argument(
+        "--limit",
+        type=int,
+        default=DEFAULT_LIMIT,
+        help=f"Maximum repos to fetch (default: {DEFAULT_LIMIT})",
+    )
+    parser.add_argument(
+        "--min-stars",
+        type=int,
+        default=DEFAULT_MIN_STARS,
+        help=f"Minimum star count for relevance (default: {DEFAULT_MIN_STARS})",
+    )
+    args = parser.parse_args()
+
+    print(
+        f"Fetching trending repos "
+        f"(language={args.language or 'any'}, topic={args.topic or 'any'}, period={args.since})..."
+    )
+
+    repos_raw = fetch_trending_repos(
+        language=args.language,
+        topic=args.topic,
+        min_stars=args.min_stars,
+        limit=args.limit,
+    )
+
+    if not repos_raw:
+        print("WARNING: No repos fetched — check network or rate limits", file=sys.stderr)
+
+    repos = [extract_repo_features(r) for r in repos_raw]
+
+    output_file = save_trending(repos, args.output)
+    print(f"Saved {len(repos)} trending repos to {output_file}")
+
+    # Brief human-readable summary
+    if repos:
+        print("\nTop repos:")
+        for repo in repos[:5]:
+            features_preview = ", ".join(repo["key_features"][:3])
+            print(f"  ★ {repo['stars']:>7}  {repo['name']}")
+            if repo["description"]:
+                desc = repo["description"][:80]
+                print(f"         {desc}{'...' if len(repo['description']) > 80 else ''}")
+            if features_preview:
+                print(f"         Features: {features_preview}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/pr_complexity_scorer.py

@@ -1,351 +0,0 @@
-#!/usr/bin/env python3
-"""
-PR Complexity Scorer - Estimate review effort for PRs.
-"""
-
-import argparse
-import json
-import os
-import re
-import sys
-from dataclasses import dataclass, asdict
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-import urllib.request
-import urllib.error
-
-GITEA_BASE = "https://forge.alexanderwhitestone.com/api/v1"
-
-DEPENDENCY_FILES = {
-    "requirements.txt", "pyproject.toml", "setup.py", "setup.cfg",
-    "Pipfile", "poetry.lock", "package.json", "yarn.lock", "Gemfile",
-    "go.mod", "Cargo.toml", "pom.xml", "build.gradle"
-}
-
-TEST_PATTERNS = [
-    r"tests?/.*\.py$", r".*_test\.py$", r"test_.*\.py$",
-    r"spec/.*\.rb$", r".*_spec\.rb$",
-    r"__tests__/", r".*\.test\.(js|ts|jsx|tsx)$"
-]
-
-WEIGHT_FILES = 0.25
-WEIGHT_LINES = 0.25
-WEIGHT_DEPS = 0.30
-WEIGHT_TEST_COV = 0.20
-
-SMALL_FILES = 5
-MEDIUM_FILES = 20
-LARGE_FILES = 50
-
-SMALL_LINES = 100
-MEDIUM_LINES = 500
-LARGE_LINES = 2000
-
-TIME_PER_POINT = {1: 5, 2: 10, 3: 15, 4: 20, 5: 25, 6: 30, 7: 45, 8: 60, 9: 90, 10: 120}
-
-
-@dataclass
-class PRComplexity:
-    pr_number: int
-    title: str
-    files_changed: int
-    additions: int
-    deletions: int
-    has_dependency_changes: bool
-    test_coverage_delta: Optional[int]
-    score: int
-    estimated_minutes: int
-    reasons: List[str]
-
-    def to_dict(self) -> dict:
-        return asdict(self)
-
-
-class GiteaClient:
-    def __init__(self, token: str):
-        self.token = token
-        self.base_url = GITEA_BASE.rstrip("/")
-
-    def _request(self, path: str, params: Dict = None) -> Any:
-        url = f"{self.base_url}{path}"
-        if params:
-            qs = "&".join(f"{k}={v}" for k, v in params.items() if v is not None)
-            url += f"?{qs}"
-
-        req = urllib.request.Request(url)
-        req.add_header("Authorization", f"token {self.token}")
-        req.add_header("Content-Type", "application/json")
-
-        try:
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                return json.loads(resp.read().decode())
-        except urllib.error.HTTPError as e:
-            print(f"API error {e.code}: {e.read().decode()[:200]}", file=sys.stderr)
-            return None
-        except urllib.error.URLError as e:
-            print(f"Network error: {e}", file=sys.stderr)
-            return None
-
-    def get_open_prs(self, org: str, repo: str) -> List[Dict]:
-        prs = []
-        page = 1
-        while True:
-            batch = self._request(f"/repos/{org}/{repo}/pulls", {"limit": 50, "page": page, "state": "open"})
-            if not batch:
-                break
-            prs.extend(batch)
-            if len(batch) < 50:
-                break
-            page += 1
-        return prs
-
-    def get_pr_files(self, org: str, repo: str, pr_number: int) -> List[Dict]:
-        files = []
-        page = 1
-        while True:
-            batch = self._request(
-                f"/repos/{org}/{repo}/pulls/{pr_number}/files",
-                {"limit": 100, "page": page}
-            )
-            if not batch:
-                break
-            files.extend(batch)
-            if len(batch) < 100:
-                break
-            page += 1
-        return files
-
-    def post_comment(self, org: str, repo: str, pr_number: int, body: str) -> bool:
-        data = json.dumps({"body": body}).encode("utf-8")
-        req = urllib.request.Request(
-            f"{self.base_url}/repos/{org}/{repo}/issues/{pr_number}/comments",
-            data=data,
-            method="POST",
-            headers={"Authorization": f"token {self.token}", "Content-Type": "application/json"}
-        )
-        try:
-            with urllib.request.urlopen(req, timeout=30) as resp:
-                return resp.status in (200, 201)
-        except urllib.error.HTTPError:
-            return False
-
-
-def is_dependency_file(filename: str) -> bool:
-    return any(filename.endswith(dep) for dep in DEPENDENCY_FILES)
-
-
-def is_test_file(filename: str) -> bool:
-    return any(re.search(pattern, filename) for pattern in TEST_PATTERNS)
-
-
-def score_pr(
-    files_changed: int,
-    additions: int,
-    deletions: int,
-    has_dependency_changes: bool,
-    test_coverage_delta: Optional[int] = None
-) -> tuple[int, int, List[str]]:
-    score = 1.0
-    reasons = []
-
-    # Files changed
-    if files_changed <= SMALL_FILES:
-        fscore = 1.0
-        reasons.append("small number of files changed")
-    elif files_changed <= MEDIUM_FILES:
-        fscore = 2.0
-        reasons.append("moderate number of files changed")
-    elif files_changed <= LARGE_FILES:
-        fscore = 2.5
-        reasons.append("large number of files changed")
-    else:
-        fscore = 3.0
-        reasons.append("very large PR spanning many files")
-
-    # Lines changed
-    total_lines = additions + deletions
-    if total_lines <= SMALL_LINES:
-        lscore = 1.0
-        reasons.append("small change size")
-    elif total_lines <= MEDIUM_LINES:
-        lscore = 2.0
-        reasons.append("moderate change size")
-    elif total_lines <= LARGE_LINES:
-        lscore = 3.0
-        reasons.append("large change size")
-    else:
-        lscore = 4.0
-        reasons.append("very large change")
-
-    # Dependency changes
-    if has_dependency_changes:
-        dscore = 2.5
-        reasons.append("dependency changes (architectural impact)")
-    else:
-        dscore = 0.0
-
-    # Test coverage delta
-    tscore = 0.0
-    if test_coverage_delta is not None:
-        if test_coverage_delta > 0:
-            reasons.append(f"test additions (+{test_coverage_delta} test files)")
-            tscore = -min(2.0, test_coverage_delta / 2.0)
-        elif test_coverage_delta < 0:
-            reasons.append(f"test removals ({abs(test_coverage_delta)} test files)")
-            tscore = min(2.0, abs(test_coverage_delta) * 0.5)
-    else:
-        reasons.append("test coverage change not assessed")
-
-    # Weighted sum, scaled by 3 to use full 1-10 range
-    bonus = (fscore * WEIGHT_FILES) + (lscore * WEIGHT_LINES) + (dscore * WEIGHT_DEPS) + (tscore * WEIGHT_TEST_COV)
-    scaled_bonus = bonus * 3.0
-    score = 1.0 + scaled_bonus
-
-    final_score = max(1, min(10, int(round(score))))
-    est_minutes = TIME_PER_POINT.get(final_score, 30)
-
-    return final_score, est_minutes, reasons
-
-
-def analyze_pr(client: GiteaClient, org: str, repo: str, pr_data: Dict) -> PRComplexity:
-    pr_num = pr_data["number"]
-    title = pr_data.get("title", "")
-    files = client.get_pr_files(org, repo, pr_num)
-
-    additions = sum(f.get("additions", 0) for f in files)
-    deletions = sum(f.get("deletions", 0) for f in files)
-    filenames = [f.get("filename", "") for f in files]
-
-    has_deps = any(is_dependency_file(f) for f in filenames)
-
-    test_added = sum(1 for f in files if f.get("status") == "added" and is_test_file(f.get("filename", "")))
-    test_removed = sum(1 for f in files if f.get("status") == "removed" and is_test_file(f.get("filename", "")))
-    test_delta = test_added - test_removed if (test_added or test_removed) else None
-
-    score, est_min, reasons = score_pr(
-        files_changed=len(files),
-        additions=additions,
-        deletions=deletions,
-        has_dependency_changes=has_deps,
-        test_coverage_delta=test_delta
-    )
-
-    return PRComplexity(
-        pr_number=pr_num,
-        title=title,
-        files_changed=len(files),
-        additions=additions,
-        deletions=deletions,
-        has_dependency_changes=has_deps,
-        test_coverage_delta=test_delta,
-        score=score,
-        estimated_minutes=est_min,
-        reasons=reasons
-    )
-
-
-def build_comment(complexity: PRComplexity) -> str:
-    change_desc = f"{complexity.files_changed} files, +{complexity.additions}/-{complexity.deletions} lines"
-    deps_note = "\n- :warning: Dependency changes detected — architectural review recommended" if complexity.has_dependency_changes else ""
-    test_note = ""
-    if complexity.test_coverage_delta is not None:
-        if complexity.test_coverage_delta > 0:
-            test_note = f"\n- :+1: {complexity.test_coverage_delta} test file(s) added"
-        elif complexity.test_coverage_delta < 0:
-            test_note = f"\n- :warning: {abs(complexity.test_coverage_delta)} test file(s) removed"
-
-    comment = f"## 📊 PR Complexity Analysis\n\n"
-    comment += f"**PR #{complexity.pr_number}: {complexity.title}**\n\n"
-    comment += f"| Metric | Value |\n|--------|-------|\n"
-    comment += f"| Changes | {change_desc} |\n"
-    comment += f"| Complexity Score | **{complexity.score}/10** |\n"
-    comment += f"| Estimated Review Time | ~{complexity.estimated_minutes} minutes |\n\n"
-    comment += f"### Scoring rationale:"
-    for r in complexity.reasons:
-        comment += f"\n- {r}"
-    if deps_note:
-        comment += deps_note
-    if test_note:
-        comment += test_note
-    comment += f"\n\n---\n"
-    comment += f"*Generated by PR Complexity Scorer — [issue #135](https://forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence/issues/135)*"
-    return comment
-
-
-def main():
-    parser = argparse.ArgumentParser(description="PR Complexity Scorer")
-    parser.add_argument("--org", default="Timmy_Foundation")
-    parser.add_argument("--repo", default="compounding-intelligence")
-    parser.add_argument("--token", default=os.environ.get("GITEA_TOKEN") or os.path.expanduser("~/.config/gitea/token"))
-    parser.add_argument("--dry-run", action="store_true")
-    parser.add_argument("--apply", action="store_true")
-    parser.add_argument("--output", default="metrics/pr_complexity.json")
-    args = parser.parse_args()
-
-    token_path = args.token
-    if os.path.exists(token_path):
-        with open(token_path) as f:
-            token = f.read().strip()
-    else:
-        token = args.token
-
-    if not token:
-        print("ERROR: No Gitea token provided", file=sys.stderr)
-        sys.exit(1)
-
-    client = GiteaClient(token)
-
-    print(f"Fetching open PRs for {args.org}/{args.repo}...")
-    prs = client.get_open_prs(args.org, args.repo)
-    if not prs:
-        print("No open PRs found.")
-        sys.exit(0)
-
-    print(f"Found {len(prs)} open PR(s). Analyzing...")
-
-    results = []
-    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
-
-    for pr in prs:
-        pr_num = pr["number"]
-        title = pr.get("title", "")
-        print(f"  Analyzing PR #{pr_num}: {title[:60]}")
-
-        try:
-            complexity = analyze_pr(client, args.org, args.repo, pr)
-            results.append(complexity.to_dict())
-
-            comment = build_comment(complexity)
-
-            if args.dry_run:
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [DRY-RUN]")
-            elif args.apply:
-                success = client.post_comment(args.org, args.repo, pr_num, comment)
-                status = "[commented]" if success else "[FAILED]"
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min {status}")
-            else:
-                print(f"    → Score: {complexity.score}/10, Est: {complexity.estimated_minutes}min [no action]")
-
-        except Exception as e:
-            print(f"    ERROR analyzing PR #{pr_num}: {e}", file=sys.stderr)
-
-    with open(args.output, "w") as f:
-        json.dump({
-            "org": args.org,
-            "repo": args.repo,
-            "timestamp": datetime.now(timezone.utc).isoformat(),
-            "pr_count": len(results),
-            "results": results
-        }, f, indent=2)
-
-    if results:
-        scores = [r["score"] for r in results]
-        print(f"\nResults saved to {args.output}")
-        print(f"Summary: {len(results)} PRs, scores range {min(scores):.0f}-{max(scores):.0f}")
-    else:
-        print("\nNo results to save.")
-
-
-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.")

scripts/test_github_trending_scanner.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""Tests for github_trending_scanner.py — pure function validation.
+
+Tests the feature inference, extraction, and output formatting logic
+without relying on external GitHub API calls.
+"""
+
+import json
+import sys
+import tempfile
+from pathlib import Path
+
+# Add scripts dir to path for import
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+from github_trending_scanner import (
+    extract_repo_features,
+    infer_features,
+    save_trending,
+)
+
+
+def test_infer_features_from_description():
+    """Feature inference extracts capabilities from description text."""
+    desc = "A local, quantized LLM framework for fine-tuning and agent-based RAG with vision."
+    topics = ["ai", "llm"]
+    features = infer_features(desc, topics)
+
+    # Should include relevant capabilities (case-insensitive comparison)
+    expected_lower = {"fine-tuning", "local/offline", "quantized models", "agent framework", "vision", "retrieval/rag"}
+    actual_lower = set(f.lower() for f in features)
+    assert expected_lower.issubset(actual_lower), f"Missing features. Expected subset of {expected_lower}, got {actual_lower}"
+    print("PASS: infer_features_from_description")
+
+
+def test_infer_features_from_topics_only():
+    """Topics alone can drive feature detection."""
+    desc = ""
+    topics = ["computer-vision", "speech", "pytorch"]
+    features = infer_features(desc, topics)
+
+    # Non-generic topics should appear as features (topics preserved as-is)
+    assert "computer-vision" in features, f"Expected 'computer-vision' in {features}"
+    assert "speech" in features, f"Expected 'speech' in {features}"
+    # Generic topics (pytorch) may be filtered
+    print(f"PASS: infer_features_from_topics_only → {features}")
+
+
+def test_extract_repo_features_produces_valid_structure():
+    """extract_repo_features returns all required fields."""
+    mock_repo = {
+        "full_name": "example/repo",
+        "description": "An example repository",
+        "stargazers_count": 1234,
+        "forks_count": 56,
+        "open_issues_count": 7,
+        "language": "Python",
+        "topics": ["ai", "llm"],
+        "html_url": "https://github.com/example/repo",
+        "created_at": "2025-01-01T00:00:00Z",
+        "updated_at": "2026-01-01T00:00:00Z",
+    }
+
+    result = extract_repo_features(mock_repo)
+
+    assert result["name"] == "example/repo"
+    assert result["description"] == "An example repository"
+    assert result["stars"] == 1234
+    assert isinstance(result["key_features"], list)
+    assert "scanned_at" in result
+    assert result["url"] == "https://github.com/example/repo"
+    print("PASS: extract_repo_features_structure")
+
+
+def test_save_trending_creates_dated_json():
+    """save_trending writes a valid JSON file with the expected schema."""
+    repos = [
+        {
+            "name": "test/repo",
+            "description": "Test repository",
+            "stars": 999,
+            "language": "Python",
+            "topics": ["test"],
+            "key_features": ["testing"],
+            "scanned_at": "2026-04-26T00:00:00+00:00",
+        }
+    ]
+
+    with tempfile.TemporaryDirectory() as tmp:
+        output_file = save_trending(repos, output_dir=tmp)
+
+        path = Path(output_file)
+        assert path.exists(), f"Output file not created: {output_file}"
+
+        with open(path) as f:
+            data = json.load(f)
+
+        assert "scanned_at" in data
+        assert data["count"] == 1
+        assert isinstance(data["repos"], list)
+        assert data["repos"][0]["name"] == "test/repo"
+        print(f"PASS: save_trending → {output_file}")
+
+
+def test_save_trending_respects_output_dir_creation():
+    """Output directory is created if it doesn't exist."""
+    repos = []
+
+    with tempfile.TemporaryDirectory() as tmp:
+        nested = Path(tmp) / "nested" / "trending"
+        assert not nested.exists()
+
+        output_file = save_trending(repos, output_dir=str(nested))
+        assert nested.exists()
+        assert Path(output_file).exists()
+        print("PASS: output_dir_creation")
+
+
+if __name__ == "__main__":
+    test_infer_features_from_description()
+    test_infer_features_from_topics_only()
+    test_extract_repo_features_produces_valid_structure()
+    test_save_trending_creates_dated_json()
+    test_save_trending_respects_output_dir_creation()
+    print("\nAll github_trending_scanner tests passed.")

scripts/test_pr_complexity_scorer.py

@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-"""
-Tests for PR Complexity Scorer — unit tests for the scoring logic.
-"""
-
-import sys
-from pathlib import Path
-
-sys.path.insert(0, str(Path(__file__).parent))
-
-from pr_complexity_scorer import (
-    score_pr,
-    is_dependency_file,
-    is_test_file,
-    TIME_PER_POINT,
-    SMALL_FILES,
-    MEDIUM_FILES,
-    LARGE_FILES,
-    SMALL_LINES,
-    MEDIUM_LINES,
-    LARGE_LINES,
-)
-
-PASS = 0
-FAIL = 0
-
-def test(name):
-    def decorator(fn):
-        global PASS, FAIL
-        try:
-            fn()
-            PASS += 1
-            print(f"  [PASS] {name}")
-        except AssertionError as e:
-            FAIL += 1
-            print(f"  [FAIL] {name}: {e}")
-        except Exception as e:
-            FAIL += 1
-            print(f"  [FAIL] {name}: Unexpected error: {e}")
-    return decorator
-
-def assert_eq(a, b, msg=""):
-    if a != b:
-        raise AssertionError(f"{msg} expected {b!r}, got {a!r}")
-
-def assert_true(v, msg=""):
-    if not v:
-        raise AssertionError(msg or "Expected True")
-
-def assert_false(v, msg=""):
-    if v:
-        raise AssertionError(msg or "Expected False")
-
-
-print("=== PR Complexity Scorer Tests ===\n")
-
-print("-- File Classification --")
-
-@test("dependency file detection — requirements.txt")
-def _():
-    assert_true(is_dependency_file("requirements.txt"))
-    assert_true(is_dependency_file("src/requirements.txt"))
-    assert_false(is_dependency_file("requirements_test.txt"))
-
-@test("dependency file detection — pyproject.toml")
-def _():
-    assert_true(is_dependency_file("pyproject.toml"))
-    assert_false(is_dependency_file("myproject.py"))
-
-@test("test file detection — pytest style")
-def _():
-    assert_true(is_test_file("tests/test_api.py"))
-    assert_true(is_test_file("test_module.py"))
-    assert_true(is_test_file("src/module_test.py"))
-
-@test("test file detection — other frameworks")
-def _():
-    assert_true(is_test_file("spec/feature_spec.rb"))
-    assert_true(is_test_file("__tests__/component.test.js"))
-    assert_false(is_test_file("testfixtures/helper.py"))
-
-
-print("\n-- Scoring Logic --")
-
-@test("small PR gets low score (1-3)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=3,
-        additions=50,
-        deletions=10,
-        has_dependency_changes=False,
-        test_coverage_delta=None
-    )
-    assert_true(1 <= score <= 3, f"Score should be low, got {score}")
-    assert_true(minutes < 20)
-
-@test("medium PR gets medium score (4-6)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=15,
-        additions=400,
-        deletions=100,
-        has_dependency_changes=False,
-        test_coverage_delta=None
-    )
-    assert_true(4 <= score <= 6, f"Score should be medium, got {score}")
-    assert_true(20 <= minutes <= 45)
-
-@test("large PR gets high score (7-9)")
-def _():
-    score, minutes, _ = score_pr(
-        files_changed=60,
-        additions=3000,
-        deletions=1500,
-        has_dependency_changes=True,
-        test_coverage_delta=None
-    )
-    assert_true(7 <= score <= 9, f"Score should be high, got {score}")
-    assert_true(minutes >= 45)
-
-@test("dependency changes boost score")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=10, additions=200, deletions=50,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    dep_score, _, _ = score_pr(
-        files_changed=10, additions=200, deletions=50,
-        has_dependency_changes=True, test_coverage_delta=None
-    )
-    assert_true(dep_score > base_score, f"Deps: {base_score} -> {dep_score}")
-
-@test("adding tests lowers complexity")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    better_score, _, _ = score_pr(
-        files_changed=8, additions=180, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=3
-    )
-    assert_true(better_score < base_score, f"Tests: {base_score} -> {better_score}")
-
-@test("removing tests increases complexity")
-def _():
-    base_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=None
-    )
-    worse_score, _, _ = score_pr(
-        files_changed=8, additions=150, deletions=20,
-        has_dependency_changes=False, test_coverage_delta=-2
-    )
-    assert_true(worse_score > base_score, f"Remove tests: {base_score} -> {worse_score}")
-
-@test("score bounded 1-10")
-def _():
-    for files, adds, dels in [(1, 10, 5), (100, 10000, 5000)]:
-        score, _, _ = score_pr(files, adds, dels, False, None)
-        assert_true(1 <= score <= 10, f"Score {score} out of range")
-
-@test("estimated minutes exist for all scores")
-def _():
-    for s in range(1, 11):
-        assert_true(s in TIME_PER_POINT, f"Missing time for score {s}")
-
-
-print(f"\n=== Results: {PASS} passed, {FAIL} failed ===")
-sys.exit(0 if FAIL == 0 else 1)