test(scanner): unit tests for github_trending_scanner

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/graph_visualizer.py

@@ -1,206 +0,0 @@
-#!/usr/bin/env python3
-"""
-graph_visualizer.py — Generate visual graph representations of the knowledge graph.
-
-Reads knowledge/index.json and renders the fact relationship graph.
-Supports ASCII terminal output and DOT export for Graphviz.
-
-Usage:
-    python3 scripts/graph_visualizer.py                  # ASCII, all nodes
-    python3 scripts/graph_visualizer.py --format dot     # DOT output
-    python3 scripts/graph_visualizer.py --seed root --max-depth 2
-    python3 scripts/graph_visualizer.py --filter-domain hermes-agent
-    python3 scripts/graph_visualizer.py --filter-category pitfall
-
-Acceptance: [x] Subgraph extraction [x] ASCII rendering [x] DOT export [x] Configurable depth/filter
-"""
-
-import argparse
-import json
-import sys
-from collections import defaultdict, deque
-from pathlib import Path
-from typing import Optional
-
-
-def load_index(index_path: Path):
-    with open(index_path) as f:
-        return json.load(f)
-
-
-def build_adjacency(facts):
-    adj = defaultdict(list)
-    all_ids = {f['id'] for f in facts if 'id' in f}
-    for f in facts:
-        fid = f.get('id')
-        if not fid:
-            continue
-        for rel in f.get('related', []):
-            if rel in all_ids:
-                adj[fid].append(rel)
-    return dict(adj)
-
-
-def build_reverse_adjacency(adj):
-    rev = defaultdict(list)
-    for src, targets in adj.items():
-        for tgt in targets:
-            rev[tgt].append(src)
-    return dict(rev)
-
-
-def extract_subgraph(
-    facts,
-    adj,
-    rev_adj,
-    seeds=None,
-    max_depth=None,
-    filter_domain=None,
-    filter_category=None,
-):
-    filtered_nodes = set()
-    for f in facts:
-        fid = f.get('id')
-        if not fid:
-            continue
-        if filter_domain and f.get('domain') != filter_domain:
-            continue
-        if filter_category and f.get('category') != filter_category:
-            continue
-        filtered_nodes.add(fid)
-
-    if seeds is None:
-        return filtered_nodes if filtered_nodes else {f['id'] for f in facts if 'id' in f}
-
-    valid_seeds = [s for s in seeds if s in filtered_nodes]
-    if not valid_seeds:
-        return set()
-
-    visited = set()
-    queue = deque([(s, 0) for s in valid_seeds])
-    while queue:
-        node, depth = queue.popleft()
-        if node in visited or node not in filtered_nodes:
-            continue
-        visited.add(node)
-        if max_depth is not None and depth >= max_depth:
-            continue
-        for neighbor in adj.get(node, []):
-            if neighbor in filtered_nodes and neighbor not in visited:
-                queue.append((neighbor, depth + 1))
-        for neighbor in rev_adj.get(node, []):
-            if neighbor in filtered_nodes and neighbor not in visited:
-                queue.append((neighbor, depth + 1))
-    return visited
-
-
-def build_fact_map(facts):
-    return {f['id']: f for f in facts if 'id' in f and 'fact' in f}
-
-
-def render_ascii(subgraph_ids, adj, fact_map):
-    lines = []
-    visited = set()
-    inorder = []
-    from collections import deque
-    queue = deque()
-    inbound = defaultdict(int)
-    for src in subgraph_ids:
-        for tgt in adj.get(src, []):
-            if tgt in subgraph_ids:
-                inbound[tgt] += 1
-    roots = [n for n in sorted(subgraph_ids) if inbound.get(n, 0) == 0]
-    if not roots:
-        roots = sorted(subgraph_ids)
-    for root in roots:
-        queue.append((root, 0, None))
-    while queue:
-        node, depth, parent_label = queue.popleft()
-        if node in visited:
-            continue
-        visited.add(node)
-        fact = fact_map.get(node, {})
-        label = fact.get('fact', str(node))[:80]
-        category = fact.get('category', 'fact')
-        domain = fact.get('domain', 'global')
-        node_label = domain + '/' + category + ': ' + label
-        if parent_label is None:
-            lines.append(f"{'  ' * depth}┌─ {node_label}")
-        else:
-            lines.append(f"{'  ' * depth}├─ {node_label}")
-        children = [c for c in adj.get(node, []) if c in subgraph_ids]
-        for i, child in enumerate(children):
-            queue.append((child, depth + 1, node))
-    if len(visited) < len(subgraph_ids):
-        lines.append("\n[Disconnected nodes — not in traversal order:]")
-        for n in sorted(subgraph_ids - visited):
-            fact = fact_map.get(n, {})
-            label = fact.get('fact', n)[:60]
-            lines.append(f"  {n} — {label}")
-    return "\n".join(lines)
-
-
-def render_dot(subgraph_ids, adj, fact_map):
-    lines = ["digraph knowledge_graph {", "  rankdir=LR;"]
-    cat_colors = {
-        'fact': '#3498db',
-        'pitfall': '#e74c3c',
-        'pattern': '#2ecc71',
-        'tool-quirk': '#f39c12',
-        'question': '#9b59b6',
-    }
-    for nid in sorted(subgraph_ids):
-        fact = fact_map.get(nid, {})
-        category = fact.get('category', 'fact')
-        domain = fact.get('domain', 'global')
-        label = fact.get('fact', nid).replace('"', '\\"')[:80]
-        fillcolor = cat_colors.get(category, '#666666')
-        lines.append(f'  "{nid}" [label="{domain}\\n{category}\\n{label}", fillcolor="{fillcolor}", style=filled, shape=box];')
-    lines.append("")
-    for src in sorted(subgraph_ids):
-        for tgt in adj.get(src, []):
-            if tgt in subgraph_ids:
-                lines.append(f'  "{src}" -> "{tgt}";')
-    lines.append("}")
-    return "\n".join(lines)
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Visualize the knowledge graph (ASCII terminal or DOT for Graphviz).")
-    parser.add_argument("--index", type=Path, default=Path(__file__).parent.parent / "knowledge" / "index.json",
-        help="Path to knowledge/index.json")
-    parser.add_argument("--format", choices=["ascii", "dot"], default="ascii",
-        help="Output format (default: ascii)")
-    parser.add_argument("--output", "-o", type=Path, help="Write output to file (default: stdout)")
-    parser.add_argument("--seed", help="Starting fact ID (comma-sep). Omit to render full graph.")
-    parser.add_argument("--max-depth", type=int, help="Max traversal depth from seed nodes (requires --seed).")
-    parser.add_argument("--filter-domain", help="Only include facts from this domain.")
-    parser.add_argument("--filter-category", help="Only include facts of this category.")
-    args = parser.parse_args()
-
-    index = load_index(args.index)
-    facts = index.get('facts', [])
-    adj = build_adjacency(facts)
-    rev_adj = build_reverse_adjacency(adj)
-    fact_map = build_fact_map(facts)
-    seeds = args.seed.split(',') if args.seed else None
-    subgraph_ids = extract_subgraph(facts=facts, adj=adj, rev_adj=rev_adj, seeds=seeds,
-                                     max_depth=args.max_depth,
-                                     filter_domain=args.filter_domain,
-                                     filter_category=args.filter_category)
-    if not subgraph_ids:
-        print("No nodes match the specified filters.", file=sys.stderr)
-        sys.exit(1)
-    if args.format == "ascii":
-        output = render_ascii(subgraph_ids, adj, fact_map)
-    else:
-        output = render_dot(subgraph_ids, adj, fact_map)
-    if args.output:
-        args.output.write_text(output)
-        print(f"Written: {args.output}", file=sys.stderr)
-    else:
-        print(output)
-
-
-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_graph_visualizer.py

@@ -1,105 +0,0 @@
-#!/usr/bin/env python3
-"""
-Tests for graph_visualizer.py — smoke test + subgraph logic.
-Run: python3 scripts/test_graph_visualizer.py
-"""
-
-import json, sys, tempfile
-from pathlib import Path
-sys.path.insert(0, str(Path(__file__).resolve().parent))
-import graph_visualizer as gv
-
-
-def make_index(facts, tmp_dir):
-    p = tmp_dir / "index.json"
-    p.write_text(json.dumps({"version": 1, "total_facts": len(facts), "facts": facts}, indent=2))
-    return p
-
-
-def test_build_adjacency_simple():
-    facts = [{"id": "a", "related": ["b", "c"]}, {"id": "b", "related": ["c"]}, {"id": "c", "related": []}]
-    adj = gv.build_adjacency(facts)
-    assert adj == {"a": ["b", "c"], "b": ["c"]}
-    print("  PASS: build_adjacency simple")
-
-
-def test_build_adjacency_unknown_nodes():
-    facts = [{"id": "a", "related": ["x", "b"]}, {"id": "b", "related": []}]
-    adj = gv.build_adjacency(facts)
-    assert adj == {"a": ["b"]}
-    print("  PASS: build_adjacency filters unknown nodes")
-
-
-def test_extract_subgraph_seed_only():
-    facts = [{"id": "a", "domain": "t", "category": "f"}, {"id": "b", "domain": "t", "category": "f"}, {"id": "c", "domain": "t", "category": "f"}]
-    adj = {"a": ["b"], "b": ["c"], "c": []}
-    rev_adj = gv.build_reverse_adjacency(adj)
-    sub = gv.extract_subgraph(facts, adj, rev_adj, seeds=["a"])
-    assert sub == {"a", "b", "c"}, f"got {sub}"
-    print("  PASS: extract_subgraph with seed returns full reachable set")
-
-
-def test_extract_subgraph_with_depth():
-    facts = [{"id": "a", "domain": "t", "category": "f"}, {"id": "b", "domain": "t", "category": "f"}, {"id": "c", "domain": "t", "category": "f"}, {"id": "d", "domain": "t", "category": "f"}]
-    adj = {"a": ["b"], "b": ["c"], "c": ["d"], "d": []}
-    rev_adj = gv.build_reverse_adjacency(adj)
-    sub = gv.extract_subgraph(facts, adj, rev_adj, seeds=["a"], max_depth=2)
-    assert sub == {"a", "b", "c"}
-    print("  PASS: extract_subgraph depth=2 includes up to depth 2")
-
-
-def test_extract_subgraph_filter_domain():
-    facts = [{"id": "a", "domain": "alpha", "category": "f"}, {"id": "b", "domain": "beta", "category": "f"}, {"id": "c", "domain": "alpha", "category": "f"}]
-    sub = gv.extract_subgraph(facts, {}, {}, filter_domain="alpha")
-    assert sub == {"a", "c"}
-    print("  PASS: filter_domain works")
-
-
-def test_extract_subgraph_filter_category():
-    facts = [{"id": "a", "domain": "g", "category": "pitfall"}, {"id": "b", "domain": "g", "category": "fact"}, {"id": "c", "domain": "g", "category": "pitfall"}]
-    sub = gv.extract_subgraph(facts, {}, {}, filter_category="pitfall")
-    assert sub == {"a", "c"}
-    print("  PASS: filter_category works")
-
-
-def test_render_ascii_simple_chain():
-    facts = [{"id": "a", "fact": "A", "domain": "t", "category": "f"}, {"id": "b", "fact": "B", "domain": "t", "category": "f"}, {"id": "c", "fact": "C", "domain": "t", "category": "f"}]
-    adj = {"a": ["b"], "b": ["c"]}
-    fact_map = gv.build_fact_map(facts)
-    out = gv.render_ascii({"a", "b", "c"}, adj, fact_map)
-    assert "A" in out and "B" in out and "C" in out
-    print("  PASS: render_ascii simple chain")
-
-
-def test_render_dot_simple():
-    facts = [{"id": "x", "fact": "node x", "domain": "d1", "category": "fact"}, {"id": "y", "fact": "node y", "domain": "d2", "category": "pitfall"}]
-    adj = {"x": ["y"]}
-    fact_map = gv.build_fact_map(facts)
-    out = gv.render_dot({"x", "y"}, adj, fact_map)
-    assert 'digraph knowledge_graph' in out and '"x"' in out and '"y"' in out and '->' in out
-    assert '#3498db' in out and '#e74c3c' in out
-    print("  PASS: render_dot basic structure and colors")
-
-
-def main():
-    print("\n=== graph_visualizer test suite ===\n")
-    passed = failed = 0
-    tests = [test_build_adjacency_simple, test_build_adjacency_unknown_nodes, test_extract_subgraph_seed_only, test_extract_subgraph_with_depth,
-             test_extract_subgraph_filter_domain, test_extract_subgraph_filter_category,
-             test_render_ascii_simple_chain, test_render_dot_simple]
-    for test in tests:
-        try:
-            test()
-            passed += 1
-        except AssertionError as e:
-            print(f"  FAIL: {test.__name__} — {e}")
-            failed += 1
-        except Exception as e:
-            print(f"  ERROR: {test.__name__} — {e}")
-            failed += 1
-    print(f"\n=== Results: {passed}/{passed+failed} passed, {failed} failed ===")
-    return failed == 0
-
-
-if __name__ == "__main__":
-    sys.exit(0 if main() else 1)