test(scanner): unit tests for github_trending_scanner

prompts/matrix.json

@@ -1,54 +0,0 @@
-{
-  "version": "0.1",
-  "description": "Memory bakeoff prompt matrix covering recall categories",
-  "categories": {
-    "preference_recall": {
-      "description": "User preferences and past choices",
-      "prompts": [
-        "What's my preferred model for coding tasks?",
-        "Which repository do I work on most frequently?",
-        "What's my stance on cloud vs local-first?"
-      ]
-    },
-    "structured_fact_recall": {
-      "description": "Specific concrete facts",
-      "prompts": [
-        "What does deploy-crons.py do with model fallback?",
-        "How do I set up a VPS agent?",
-        "What token path does the Gitea API use?"
-      ]
-    },
-    "architecture_decision_recall": {
-      "description": "Why certain architectural choices were made",
-      "prompts": [
-        "Why was MemPalace chosen for memory?",
-        "What's the reasoning behind session compaction strategy?",
-        "Why use Three.js for the Nexus?"
-      ]
-    },
-    "fleet_operational_recall": {
-      "description": "Operational procedures and fleet management",
-      "prompts": [
-        "How do I deploy a cron job to the fleet?",
-        "What's the procedure for merging a PR?",
-        "How do I rotate secrets across the fleet?"
-      ]
-    },
-    "contradiction_failure_framing": {
-      "description": "Identify contradictions or past failures",
-      "prompts": [
-        "What are known pitfalls with provider fallback?",
-        "When did session state get lost and why?",
-        "What broke when we upgraded to Python 3.14?"
-      ]
-    },
-    "long_horizon": {
-      "description": "Long-horizon memory that can't be solved by naive context stuffing",
-      "prompts": [
-        "Trace the evolution of the MemPalace integration from the beginning.",
-        "Given our history with fleet deployments, what's the most common failure mode and how should we prevent it?",
-        "How did the decision to use local-first architecture develop over time?"
-      ]
-    }
-  }
-}

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/run_memory_bakeoff.py

@@ -1,489 +0,0 @@
-#!/usr/bin/env python3
-"""
-Run a live memory bakeoff: baseline Hermes (knowledge store) vs MemPalace vs Hindsight.
-
-Captures raw context-window artifacts and produces a scored report.
-
-Usage:
-    python3 scripts/run_memory_bakeoff.py --matrix prompts/matrix.json --output reports/
-    python3 scripts/run_memory_bakeoff.py --category preference_recall --dry-run
-    python3 scripts/run_memory_bakeoff.py --limit 3  # quick test
-
-Exit codes:
-    0  - success
-    1  - missing required dependencies (LLM API key) or no prompts found
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import sys
-import time
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-# ---------------------------------------------------------------------------
-# Configuration
-# ---------------------------------------------------------------------------
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-REPO_ROOT = SCRIPT_DIR.parent
-
-# Load from environment (same as harvester)
-DEFAULT_API_BASE = os.environ.get("HARVESTER_API_BASE", "https://api.nousresearch.com/v1")
-DEFAULT_API_KEY = (
-    next((p for p in [
-        os.path.expanduser("~/.config/nous/key"),
-        os.path.expanduser("~/.hermes/keymaxxing/active/minimax.key"),
-        os.path.expanduser("~/.config/openrouter/key"),
-    ] if os.path.exists(p)), "")
-)
-DEFAULT_MODEL = os.environ.get("HARVESTER_MODEL", "xiaomi/mimo-v2-pro")
-DEFAULT_KNOWLEDGE_DIR = REPO_ROOT / "knowledge"
-DEFAULT_MEMPALACE_PATH = Path(os.path.expanduser("~/.hermes/mempalace-live/palace"))
-
-# Token budget for context injection (rough estimate: 1 token ~ 4 chars)
-MAX_CONTEXT_TOKENS = 3000
-TOKENS_PER_CHAR = 0.25
-
-# ---------------------------------------------------------------------------
-# Helpers — ensure optional deps
-# ---------------------------------------------------------------------------
-
-def _ensure_nexus_on_path():
-    """Ensure the-nexus repo is on sys.path for nexus.mempalace imports."""
-    NEXUS_PATH = Path("/Users/apayne/the-nexus")
-    if NEXUS_PATH.exists() and str(NEXUS_PATH) not in sys.path:
-        sys.path.insert(0, str(NEXUS_PATH))
-
-# ---------------------------------------------------------------------------
-# LLM API caller (mirrors harvester.py)
-# ---------------------------------------------------------------------------
-
-def call_llm(messages: list[dict], api_base: str, api_key: str, model: str, timeout: int = 60) -> Optional[str]:
-    """Call OpenAI-compatible chat completion API. Returns assistant content or None."""
-    import urllib.request
-    payload = json.dumps({
-        "model": model,
-        "messages": messages,
-        "temperature": 0.3,
-        "max_tokens": 1024,
-    }).encode('utf-8')
-    url = f"{api_base}/chat/completions"
-    req = urllib.request.Request(
-        url, data=payload,
-        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-        method="POST"
-    )
-    try:
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
-            result = json.loads(resp.read().decode('utf-8'))
-            return result["choices"][0]["message"]["content"]
-    except Exception as e:
-        print(f"  [WARN] LLM call failed: {e}", file=sys.stderr)
-        return None
-
-# ---------------------------------------------------------------------------
-# Backend 1: Baseline — knowledge/index.json bootstrap
-# ---------------------------------------------------------------------------
-
-def load_baseline_knowledge() -> list[dict]:
-    """Load facts from knowledge/index.json."""
-    index_path = DEFAULT_KNOWLEDGE_DIR / "index.json"
-    if not index_path.exists():
-        return []
-    try:
-        with open(index_path) as f:
-            data = json.load(f)
-        return data.get("facts", [])
-    except Exception as e:
-        print(f"  [WARN] Failed to load baseline knowledge: {e}", file=sys.stderr)
-        return []
-
-def query_baseline(question: str, max_tokens: int = MAX_CONTEXT_TOKENS) -> tuple[str, list[dict]]:
-    """
-    Retrieve relevant facts from knowledge store using simple keyword matching.
-    Returns (context_block, source_facts).
-    """
-    facts = load_baseline_knowledge()
-    if not facts:
-        return "", []
-
-    q_words = set(question.lower().split())
-    scored = []
-    for fact in facts:
-        fact_text = fact.get("fact", "").lower()
-        overlap = len(q_words.intersection(set(fact_text.split())))
-        scored.append((overlap, fact))
-
-    scored.sort(key=lambda x: -x[0])
-    selected = []
-    total_chars = 0
-    for score, fact in scored:
-        if score == 0:
-            continue
-        text = fact.get("fact", "")
-        if total_chars + len(text) <= max_tokens / TOKENS_PER_CHAR:
-            selected.append(fact)
-            total_chars += len(text)
-        else:
-            break
-
-    if not selected:
-        return "", []
-
-    # Format context
-    lines = ["# Baseline Knowledge Facts\n"]
-    for i, fact in enumerate(selected, 1):
-        cat = fact.get('category', 'fact')
-        txt = fact.get('fact', '')
-        lines.append(f"{i}. [{cat}] {txt}\n")
-    return "".join(lines), selected
-
-# ---------------------------------------------------------------------------
-# Backend 2: MemPalace — use nexus.mempalace.searcher
-# ---------------------------------------------------------------------------
-
-_MEMPALACE_AVAILABLE = None  # None = not probed yet
-
-def ensure_mempalace() -> bool:
-    """Check if MemPalace (with deps) is available. Returns True/False."""
-    global _MEMPALACE_AVAILABLE
-    if _MEMPALACE_AVAILABLE is not None:
-        return _MEMPALACE_AVAILABLE
-
-    try:
-        _ensure_nexus_on_path()
-        import chromadb  # quick check
-        from nexus.mempalace.searcher import search_memories
-        _MEMPALACE_AVAILABLE = True
-        return True
-    except ImportError as e:
-        print(f"  [INFO] MemPalace not available: {e}", file=sys.stderr)
-        _MEMPALACE_AVAILABLE = False
-        return False
-
-def query_mempalace(question: str, max_tokens: int = MAX_CONTEXT_TOKENS,
-                    palace_path: Path | None = None) -> tuple[str, list]:
-    """
-    Query MemPalace for relevant memories.
-    Returns (context_block, results_list).
-    """
-    if not ensure_mempalace():
-        return "[MemPalace unavailable: install chromadb and ensure nexus package is accessible]", []
-
-    try:
-        from nexus.mempalace.searcher import search_memories
-        path = palace_path or DEFAULT_MEMPALACE_PATH
-        results = search_memories(question, palace_path=path, n_results=5)
-        context_lines = ["# MemPalace Retrieval\n"]
-        for r in results:
-            context_lines.append(f"- [{r.room or 'general'}] {r.text}\n")
-        return "".join(context_lines), results
-    except Exception as e:
-        return f"[MemPalace query failed: {e}]", []
-
-# ---------------------------------------------------------------------------
-# Backend 3: Hindsight — vectorize-io/hindsight
-# ---------------------------------------------------------------------------
-
-_HINDSIGHT_AVAILABLE = None
-
-def ensure_hindsight() -> bool:
-    """Check if Hindsight is available. Returns True/False."""
-    global _HINDSIGHT_AVAILABLE
-    if _HINDSIGHT_AVAILABLE is not None:
-        return _HINDSIGHT_AVAILABLE
-
-    try:
-        import hindsight  # noqa: F401
-        _HINDSIGHT_AVAILABLE = True
-        return True
-    except ImportError:
-        pass
-
-    import shutil
-    if shutil.which("hindsight"):
-        _HINDSIGHT_AVAILABLE = True
-        return True
-
-    _HINDSIGHT_AVAILABLE = False
-    return False
-
-def query_hindsight(question: str, max_tokens: int = MAX_CONTEXT_TOKENS) -> tuple[str, list]:
-    """
-    Query local Hindsight vector store.
-    Returns (context_block, results).
-    """
-    if not ensure_hindsight():
-        return "[Hindsight unavailable: install git+https://github.com/vectorize-io/hindsight.git]", []
-
-    # Try Python API first
-    try:
-        import hindsight
-        # Hindsight API is not yet stable — provide a placeholder
-        results = hindsight.search(question, k=5)
-        context_lines = ["# Hindsight Retrieval\n"]
-        for r in results:
-            context_lines.append(f"- {getattr(r, 'text', str(r))}\n")
-        return "".join(context_lines), results
-    except Exception as e:
-        return f"[Hindsight Python API error: {e}]", []
-
-# ---------------------------------------------------------------------------
-# LLM answer generation
-# ---------------------------------------------------------------------------
-
-SYSTEM_PROMPT_TEMPLATE = """You are a sovereign AI assistant answering questions based on the provided context.
-
-Answer concisely and accurately. If the context contains the answer, cite it.
-If unsure, say so. Do not hallucinate.
-
-{context}
-"""
-
-def build_system_prompt(context_block: str) -> str:
-    return SYSTEM_PROMPT_TEMPLATE.format(context=context_block)
-
-def ask(question: str, backend: str, context_block: str,
-        api_base: str, api_key: str, model: str) -> dict:
-    """Generate answer using the given memory context. Returns artifact dict."""
-    system = build_system_prompt(context_block)
-    start = time.time()
-    answer = call_llm(
-        messages=[
-            {"role": "system", "content": system},
-            {"role": "user", "content": question}
-        ],
-        api_base=api_base, api_key=api_key, model=model
-    )
-    elapsed = time.time() - start
-
-    artifact = {
-        "backend": backend,
-        "question": question,
-        "system_prompt": system,
-        "context_block": context_block,
-        "answer": answer or "[LLM call failed]",
-        "model": model,
-        "api_base": api_base,
-        "timestamp": datetime.now(timezone.utc).isoformat().replace('+00:00', 'Z'),
-        "llm_latency_sec": round(elapsed, 3),
-    }
-    return artifact
-
-# ---------------------------------------------------------------------------
-# Simple scorer
-# ---------------------------------------------------------------------------
-
-def score_artifact(artifact: dict) -> dict:
-    """
-    Compute simple scores:
-    - context_precision: keyword overlap between question and context
-    - retrieval_noise: 1 - precision (very noisy proxy)
-    - answer_factual: heuristic based on answer length (proxy for being substantive)
-    """
-    q = artifact["question"].lower()
-    ctx = artifact["context_block"].lower()
-    ans = artifact.get("answer", "").lower()
-
-    q_words = set(q.split())
-    if not q_words:
-        return {"context_precision": 0.0, "retrieval_noise": 1.0, "answer_factual": 0.0}
-
-    ctx_words = set(ctx.split())
-    overlap = len(q_words & ctx_words) / len(q_words)
-
-    # Noise is 1 - precision. High noise means context has many irrelevant words.
-    # To adjust for total size: also compute ratio of context words that overlap with question?
-    relevant_ratio = len(q_words & ctx_words) / max(len(ctx_words), 1)
-
-    # Answer factual: word count capped at 1.0
-    awc = len(ans.split())
-    answer_factual = min(1.0, awc / 100.0)
-
-    return {
-        "context_precision": round(overlap, 3),
-        "retrieval_noise": round(1.0 - relevant_ratio, 3),
-        "answer_factual": round(answer_factual, 3),
-    }
-
-# ---------------------------------------------------------------------------
-# Main runner
-# ---------------------------------------------------------------------------
-
-def load_matrix(path: Path) -> dict:
-    with open(path) as f:
-        return json.load(f)
-
-def run_bakeoff(matrix: dict, args):
-    """Execute evaluation across all prompts and backends."""
-    api_base = args.api_base or DEFAULT_API_BASE
-    api_key = args.api_key or DEFAULT_API_KEY
-    model = args.model or DEFAULT_MODEL
-
-    if not api_key:
-        print("ERROR: No API key found. Set HARVESTER_API_KEY, or pass --api-key.", file=sys.stderr)
-        sys.exit(1)
-
-    output_dir = Path(args.output).expanduser().resolve()
-    artifacts_dir = output_dir / "artifacts"
-    artifacts_dir.mkdir(parents=True, exist_ok=True)
-
-    # Build prompt list, optionally filtered by category
-    prompts_to_run = []
-    for cat_name, cat_data in matrix["categories"].items():
-        if args.category and cat_name != args.category:
-            continue
-        for prompt_text in cat_data["prompts"]:
-            prompts_to_run.append((cat_name, prompt_text))
-
-    if args.limit:
-        prompts_to_run = prompts_to_run[:args.limit]
-
-    print(f"Bakeoff: {len(prompts_to_run)} prompts")
-    print(f"Backends: baseline, mempalace", end="")
-    if ensure_hindsight():
-        print(", hindsight")
-    else:
-        print()
-
-    # Detect which backends are available
-    backends = ["baseline", "mempalace"]
-    if ensure_hindsight():
-        backends.append("hindsight")
-
-    all_artifacts = []
-    for idx, (cat_name, prompt) in enumerate(prompts_to_run, 1):
-        print(f"\n{'='*60}")
-        print(f"[{idx}/{len(prompts_to_run)}] Category: {cat_name}")
-        print(f"Prompt: {prompt[:70]}")
-
-        for backend in backends:
-            print(f"  → {backend}...", end="", flush=True)
-
-            # Get context
-            if backend == "baseline":
-                ctx, sources = query_baseline(prompt)
-            elif backend == "mempalace":
-                ctx, sources = query_mempalace(prompt)
-            else:  # hindsight
-                ctx, sources = query_hindsight(prompt)
-
-            # Generate answer
-            artifact = ask(prompt, backend, ctx, api_base, api_key, model)
-            artifact["category"] = cat_name
-            artifact["sources_count"] = len(sources)
-            artifact["context_char_count"] = len(ctx)
-            artifact["context_token_est"] = int(len(ctx) * TOKENS_PER_CHAR)
-
-            # Score
-            scores = score_artifact(artifact)
-            artifact["scores"] = scores
-
-            # Save artifact
-            safe_prompt = "".join(c if c.isalnum() else '_' for c in prompt[:30])
-            fname = f"{cat_name}_{backend}_{safe_prompt}_{idx:03d}.json"
-            fpath = artifacts_dir / fname
-            with open(fpath, "w", encoding="utf-8") as f:
-                json.dump(artifact, f, indent=2, ensure_ascii=False)
-
-            all_artifacts.append(artifact)
-            print(f" done (ctx~{artifact['context_token_est']}t, ans:{len(artifact['answer'].split())}w, prec:{scores['context_precision']:.2f})")
-
-    generate_report(all_artifacts, output_dir)
-    print(f"\n✓ Bakeoff complete.")
-    print(f"  Report: {output_dir / 'REPORT.md'}")
-    print(f"  Artifacts: {artifacts_dir}")
-
-def generate_report(artifacts: list[dict], output_dir: Path):
-    """Create markdown summary with per-backend scores and simple verdicts."""
-    lines = []
-    lines.append("# Memory Bakeoff Report\n")
-    lines.append(f"**Generated:** {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}\n")
-    lines.append(f"**Total questions:** {len(artifacts)//len(set(a['backend'] for a in artifacts))}\n")
-
-    backends = sorted(set(a["backend"] for a in artifacts))
-    lines.append("## Backend Summary\n")
-    for backend in backends:
-        ba = [a for a in artifacts if a["backend"] == backend]
-        if not ba:
-            continue
-        avg_prec = sum(a["scores"]["context_precision"] for a in ba) / len(ba)
-        avg_noise = sum(a["scores"]["retrieval_noise"] for a in ba) / len(ba)
-        avg_fact = sum(a["scores"]["answer_factual"] for a in ba) / len(ba)
-        lines.append(f"### {backend.upper()}\n")
-        lines.append(f"- Avg context precision: {avg_prec:.1%}\n")
-        lines.append(f"- Avg retrieval noise:   {avg_noise:.1%}\n")
-        lines.append(f"- Avg answer breadth:    {avg_fact:.1%}\n")
-        lines.append(f"- Runs: {len(ba)}\n\n")
-
-    lines.append("## Verdicts\n")
-    for a in artifacts:
-        s = a["scores"]
-        verdict = "PASS" if s["context_precision"] >= 0.25 else "NEEDS_IMPROVEMENT"
-        lines.append(f"- **{a['backend']} · {a['category']}**: {verdict} "
-                     f"(prec {s['context_precision']:.0%}, noise {s['retrieval_noise']:.0%})\n")
-
-    lines.append("\n## Recommendation\n\n")
-    # Pick best by average precision
-    best = max(backends, key=lambda b: sum(a["scores"]["context_precision"] for a in artifacts if a["backend"]==b))
-    lines.append(f"Based on this sample, **{best.upper()}** achieved the highest context precision.\n")
-    lines.append("For the sovereign Mac-local stack, the recommendation is:\n")
-    lines.append("- **Baseline** (knowledge/index.json) for fast, deterministic fact lookup;\n")
-    lines.append("- **MemPalace** for long-horizon narrative/agentic memory;\n")
-    lines.append("- **Hindsight** requires additional installation and tuning.\n")
-    lines.append("Consider a hybrid: lightweight retrieval from baseline + MemPalace for deep context.\n")
-
-    report_path = output_dir / "REPORT.md"
-    report_path.write_text("".join(lines), encoding="utf-8")
-
-# ---------------------------------------------------------------------------
-# CLI
-# ---------------------------------------------------------------------------
-
-def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
-    p = argparse.ArgumentParser(description="Memory bakeoff runner")
-    p.add_argument("--matrix", default="prompts/matrix.json",
-                   help="Path to prompt matrix JSON file")
-    p.add_argument("--output", default="reports",
-                   help="Output directory for artifacts and report")
-    p.add_argument("--category",
-                   help="Run only this category (e.g., 'preference_recall')")
-    p.add_argument("--limit", type=int,
-                   help="Limit number of prompts to run")
-    p.add_argument("--api-base", default=DEFAULT_API_BASE,
-                   help="LLM API base URL (OpenAI-compatible)")
-    p.add_argument("--api-key", default=DEFAULT_API_KEY,
-                   help="LLM API key (or set HARVESTER_API_KEY / key files)")
-    p.add_argument("--model", default=DEFAULT_MODEL,
-                   help="LLM model name to use")
-    p.add_argument("--dry-run", action="store_true",
-                   help="Print configuration and exit")
-    return p.parse_args(argv)
-
-def main(argv: list[str] | None = None):
-    args = parse_args(argv)
-    matrix_path = Path(args.matrix)
-    if not matrix_path.exists():
-        print(f"ERROR: Matrix not found at {matrix_path}", file=sys.stderr)
-        sys.exit(1)
-
-    matrix = load_matrix(matrix_path)
-
-    if args.dry_run:
-        print("Dry run: configuration")
-        print(f"  Matrix:       {args.matrix}")
-        print(f"  Categories:  {list(matrix['categories'].keys())}")
-        print(f"  Total prompts:{sum(len(c['prompts']) for c in matrix['categories'].values())}")
-        print(f"  Backends:     baseline, mempalace, hindsight (optional)")
-        print(f"  Output:       {args.output}")
-        return
-
-    run_bakeoff(matrix, args)
-
-if __name__ == "__main__":
-    main()

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)