feat: Priority Rebalancer — re-score issues from pipeline data (#174)

Monthly pipeline tool that:
- Reads knowledge store, metrics, and staleness data
- Scores all open issues across the org
- Suggests priority upgrades/downgrades based on accumulated signals
- Generates JSON + markdown reports
- Optional --apply mode to push changes via Gitea API

Signals detected:
- Stale/missing knowledge entries
- Empty knowledge store
- Missing metrics output
- Low repo coverage
- Issue age, activity, assignment status
- Keyword/label analysis

Usage:
  python3 scripts/priority_rebalancer.py --org Timmy_Foundation
  python3 scripts/priority_rebalancer.py --org Timmy_Foundation --apply
  python3 scripts/priority_rebalancer.py --org Timmy_Foundation --json

23 tests, all passing.

scripts/knowledge_staleness_check.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3
+"""
+Knowledge Store Staleness Detector — Detect stale knowledge entries by comparing source file hashes.
+
+Usage:
+    python3 scripts/knowledge_staleness_check.py --index knowledge/index.json
+    python3 scripts/knowledge_staleness_check.py --index knowledge/index.json --json
+    python3 scripts/knowledge_staleness_check.py --index knowledge/index.json --fix
+"""
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Dict, List, Any, Optional
+
+
+def compute_file_hash(filepath: str) -> Optional[str]:
+    """Compute SHA-256 hash of a file. Returns None if file doesn't exist."""
+    try:
+        with open(filepath, "rb") as f:
+            return "sha256:" + hashlib.sha256(f.read()).hexdigest()
+    except (FileNotFoundError, IsADirectoryError, PermissionError):
+        return None
+
+
+def check_staleness(index_path: str, repo_root: str = ".") -> List[Dict[str, Any]]:
+    """Check all entries in knowledge index for staleness.
+    
+    Returns list of entries with staleness info:
+    - status: "fresh" | "stale" | "missing_source" | "no_hash"
+    - current_hash: computed hash (if source exists)
+    - stored_hash: hash from index
+    """
+    with open(index_path) as f:
+        data = json.load(f)
+
+    facts = data.get("facts", [])
+    results = []
+
+    for entry in facts:
+        source_file = entry.get("source_file")
+        stored_hash = entry.get("source_hash")
+
+        if not source_file:
+            results.append({**entry, "status": "no_source", "current_hash": None})
+            continue
+
+        full_path = os.path.join(repo_root, source_file)
+        current_hash = compute_file_hash(full_path)
+
+        if current_hash is None:
+            results.append({**entry, "status": "missing_source", "current_hash": None})
+        elif not stored_hash:
+            results.append({**entry, "status": "no_hash", "current_hash": current_hash})
+        elif current_hash != stored_hash:
+            results.append({**entry, "status": "stale", "current_hash": current_hash})
+        else:
+            results.append({**entry, "status": "fresh", "current_hash": current_hash})
+
+    return results
+
+
+def fix_hashes(index_path: str, repo_root: str = ".") -> int:
+    """Add hashes to entries missing them. Returns count of fixed entries."""
+    with open(index_path) as f:
+        data = json.load(f)
+
+    fixed = 0
+    for entry in data.get("facts", []):
+        if entry.get("source_hash"):
+            continue
+        source_file = entry.get("source_file")
+        if not source_file:
+            continue
+        full_path = os.path.join(repo_root, source_file)
+        h = compute_file_hash(full_path)
+        if h:
+            entry["source_hash"] = h
+            fixed += 1
+
+    with open(index_path, "w") as f:
+        json.dump(data, f, indent=2)
+
+    return fixed
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Check knowledge store staleness")
+    parser.add_argument("--index", required=True, help="Path to knowledge/index.json")
+    parser.add_argument("--repo", default=".", help="Repo root for source file resolution")
+    parser.add_argument("--json", action="store_true", help="Output as JSON")
+    parser.add_argument("--fix", action="store_true", help="Add hashes to entries missing them")
+
+    args = parser.parse_args()
+
+    if args.fix:
+        fixed = fix_hashes(args.index, args.repo)
+        print(f"Fixed {fixed} entries with missing hashes.")
+        return
+
+    results = check_staleness(args.index, args.repo)
+
+    if args.json:
+        print(json.dumps(results, indent=2))
+    else:
+        stale = [r for r in results if r["status"] != "fresh"]
+        fresh = [r for r in results if r["status"] == "fresh"]
+
+        print(f"Knowledge Store Staleness Check")
+        print(f"  Total entries: {len(results)}")
+        print(f"  Fresh: {len(fresh)}")
+        print(f"  Stale/Issues: {len(stale)}")
+        print()
+
+        if stale:
+            print("Issues found:")
+            for r in stale:
+                status = r["status"]
+                fact = r.get("fact", "?")[:60]
+                source = r.get("source_file", "?")
+                print(f"  [{status}] {source}: {fact}")
+        else:
+            print("All entries are fresh!")
+
+
+if __name__ == "__main__":
+    main()

scripts/priority_rebalancer.py

@@ -0,0 +1,682 @@
+#!/usr/bin/env python3
+"""
+Priority Rebalancer — Re-evaluate issue priorities based on accumulated data.
+
+Reads pipeline outputs, knowledge store, and Gitea issues to suggest
+priority changes based on what the fleet has learned.
+
+Usage:
+    python3 scripts/priority_rebalancer.py --org Timmy_Foundation
+    python3 scripts/priority_rebalancer.py --org Timmy_Foundation --repo compounding-intelligence
+    python3 scripts/priority_rebalancer.py --org Timmy_Foundation --dry-run
+    python3 scripts/priority_rebalancer.py --org Timmy_Foundation --apply
+
+Output:
+    metrics/priority_report.json   — full analysis
+    metrics/priority_suggestions.md — human-readable suggestions
+"""
+
+import argparse
+import json
+import os
+import sys
+from datetime import datetime, timezone, timedelta
+from pathlib import Path
+from typing import Dict, List, Any, Optional, Tuple
+from dataclasses import dataclass, field, asdict
+from collections import Counter, defaultdict
+import urllib.request
+import urllib.error
+
+
+# ============================================================
+# Data Models
+# ============================================================
+
+@dataclass
+class IssueScore:
+    issue_id: int
+    repo: str
+    title: str
+    current_labels: List[str]
+    current_priority: Optional[str]
+    suggested_priority: Optional[str]
+    score: float
+    reasons: List[str]
+    age_days: int
+    comment_count: int
+    assignee: Optional[str]
+    dependencies: List[str] = field(default_factory=list)
+    blocking: List[str] = field(default_factory=list)
+
+
+@dataclass
+class PipelineSignal:
+    source: str  # "knowledge", "metrics", "sessions", "staleness"
+    signal_type: str  # "stale_knowledge", "high_error_rate", "missing_coverage", etc.
+    weight: float  # 0.0 - 1.0
+    detail: str
+    affected_repos: List[str] = field(default_factory=list)
+    affected_issues: List[int] = field(default_factory=list)
+
+
+# ============================================================
+# Gitea API Client
+# ============================================================
+
+class GiteaClient:
+    def __init__(self, base_url: str, token: str):
+        self.base_url = base_url.rstrip("/")
+        self.token = token
+
+    def _request(self, path: str, params: Dict = None) -> Any:
+        url = f"{self.base_url}/api/v1{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} for {path}: {e.read().decode()[:200]}", file=sys.stderr)
+            return None
+
+    def get_org_repos(self, org: str) -> List[Dict]:
+        repos = []
+        page = 1
+        while True:
+            batch = self._request(f"/orgs/{org}/repos", {"limit": 50, "page": page})
+            if not batch:
+                break
+            repos.extend(batch)
+            if len(batch) < 50:
+                break
+            page += 1
+        return repos
+
+    def get_issues(self, org: str, repo: str, state: str = "open") -> List[Dict]:
+        issues = []
+        page = 1
+        while True:
+            batch = self._request(f"/repos/{org}/{repo}/issues",
+                                  {"state": state, "limit": 50, "page": page, "type": "issues"})
+            if not batch:
+                break
+            issues.extend(batch)
+            if len(batch) < 50:
+                break
+            page += 1
+        return issues
+
+    def add_label_to_issue(self, org: str, repo: str, issue_num: int, label_ids: List[int]) -> bool:
+        url = f"{self.base_url}/api/v1/repos/{org}/{repo}/issues/{issue_num}/labels"
+        data = json.dumps({"labels": label_ids}).encode()
+        req = urllib.request.Request(url, data=data, method="POST")
+        req.add_header("Authorization", f"token {self.token}")
+        req.add_header("Content-Type", "application/json")
+        try:
+            with urllib.request.urlopen(req, timeout=15) as resp:
+                return resp.status == 200
+        except Exception:
+            return False
+
+    def remove_label_from_issue(self, org: str, repo: str, issue_num: int, label_id: int) -> bool:
+        url = f"{self.base_url}/api/v1/repos/{org}/{repo}/issues/{issue_num}/labels/{label_id}"
+        req = urllib.request.Request(url, method="DELETE")
+        req.add_header("Authorization", f"token {self.token}")
+        try:
+            with urllib.request.urlopen(req, timeout=15) as resp:
+                return resp.status == 200
+        except Exception:
+            return False
+
+    def get_repo_labels(self, org: str, repo: str) -> List[Dict]:
+        labels = []
+        page = 1
+        while True:
+            batch = self._request(f"/repos/{org}/{repo}/labels", {"limit": 50, "page": page})
+            if not batch:
+                break
+            labels.extend(batch)
+            if len(batch) < 50:
+                break
+            page += 1
+        return labels
+
+    def add_comment(self, org: str, repo: str, issue_num: int, body: str) -> bool:
+        url = f"{self.base_url}/api/v1/repos/{org}/{repo}/issues/{issue_num}/comments"
+        data = json.dumps({"body": body}).encode()
+        req = urllib.request.Request(url, data=data, method="POST")
+        req.add_header("Authorization", f"token {self.token}")
+        req.add_header("Content-Type", "application/json")
+        try:
+            with urllib.request.urlopen(req, timeout=15) as resp:
+                return resp.status == 201
+        except Exception:
+            return False
+
+
+# ============================================================
+# Pipeline Signal Collectors
+# ============================================================
+
+def collect_knowledge_signals(knowledge_dir: str) -> List[PipelineSignal]:
+    """Analyze knowledge store for coverage gaps and staleness."""
+    signals = []
+    index_path = os.path.join(knowledge_dir, "index.json")
+
+    if not os.path.exists(index_path):
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="missing_index",
+            weight=0.8,
+            detail="knowledge/index.json not found — no knowledge base exists"
+        ))
+        return signals
+
+    try:
+        with open(index_path) as f:
+            data = json.load(f)
+    except (json.JSONDecodeError, IOError) as e:
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="corrupt_index",
+            weight=0.9,
+            detail=f"knowledge/index.json is corrupt: {e}"
+        ))
+        return signals
+
+    facts = data.get("facts", [])
+    total = len(facts)
+
+    if total == 0:
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="empty_knowledge",
+            weight=0.7,
+            detail="Knowledge store has 0 facts — harvester not running or not finding sessions"
+        ))
+        return signals
+
+    # Check staleness
+    stale_count = 0
+    missing_source = 0
+    for fact in facts:
+        status = fact.get("status", "unknown")
+        if status == "stale":
+            stale_count += 1
+        elif status in ("missing_source", "no_source"):
+            missing_source += 1
+
+    if stale_count > 0:
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="stale_knowledge",
+            weight=min(1.0, stale_count / max(1, total)),
+            detail=f"{stale_count}/{total} facts are stale (source files changed)"
+        ))
+
+    if missing_source > 0:
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="missing_sources",
+            weight=min(1.0, missing_source / max(1, total)),
+            detail=f"{missing_source}/{total} facts have missing source files"
+        ))
+
+    # Coverage by repo
+    repo_counts = Counter(f.get("repo", "unknown") for f in facts)
+    if len(repo_counts) < 3:
+        signals.append(PipelineSignal(
+            source="knowledge",
+            signal_type="low_coverage",
+            weight=0.5,
+            detail=f"Knowledge covers only {len(repo_counts)} repos — expand harvester scope",
+            affected_repos=list(repo_counts.keys())
+        ))
+
+    return signals
+
+
+def collect_staleness_signals(scripts_dir: str, knowledge_dir: str) -> List[PipelineSignal]:
+    """Run staleness checker if available."""
+    signals = []
+    checker = os.path.join(scripts_dir, "knowledge_staleness_check.py")
+    index_path = os.path.join(knowledge_dir, "index.json")
+
+    if not os.path.exists(checker) or not os.path.exists(index_path):
+        return signals
+
+    try:
+        import subprocess
+        result = subprocess.run(
+            ["python3", checker, "--index", index_path, "--json"],
+            capture_output=True, text=True, timeout=30
+        )
+        if result.returncode == 0:
+            data = json.loads(result.stdout)
+            stale = data.get("stale_count", 0)
+            total = data.get("total", 0)
+            if stale > 0:
+                signals.append(PipelineSignal(
+                    source="staleness",
+                    signal_type="stale_knowledge",
+                    weight=min(1.0, stale / max(1, total)),
+                    detail=f"Staleness checker found {stale}/{total} stale entries"
+                ))
+    except Exception:
+        pass
+
+    return signals
+
+
+def collect_metrics_signals(metrics_dir: str) -> List[PipelineSignal]:
+    """Analyze metrics directory for pipeline health."""
+    signals = []
+
+    if not os.path.isdir(metrics_dir):
+        return signals
+
+    files = os.listdir(metrics_dir)
+    if len(files) <= 1:  # Only .gitkeep
+        signals.append(PipelineSignal(
+            source="metrics",
+            signal_type="no_metrics",
+            weight=0.6,
+            detail="Metrics directory is empty — measurer pipeline not producing output"
+        ))
+
+    return signals
+
+
+# ============================================================
+# Priority Scoring Engine
+# ============================================================
+
+PRIORITY_LEVELS = ["P0", "P1", "P2", "P3", "none"]
+PRIORITY_LABELS = {"p0": "P0", "p1": "P1", "p2": "P2", "p3": "P3",
+                   "priority:critical": "P0", "priority:high": "P1",
+                   "priority:medium": "P2", "priority:low": "P3"}
+
+
+def extract_priority(labels: List[str]) -> Optional[str]:
+    """Extract priority level from issue labels."""
+    for label in labels:
+        lower = label.lower().strip()
+        if lower in PRIORITY_LABELS:
+            return PRIORITY_LABELS[lower]
+    return None
+
+
+def compute_issue_score(
+    issue: Dict,
+    repo: str,
+    signals: List[PipelineSignal],
+    now: datetime
+) -> IssueScore:
+    """Compute priority score for a single issue."""
+
+    labels = [l.get("name", "") if isinstance(l, dict) else l for l in issue.get("labels", [])]
+    title = issue.get("title", "")
+    issue_id = issue.get("number", 0)
+    current_priority = extract_priority(labels)
+
+    # Parse dates
+    created_str = issue.get("created_at", "")
+    try:
+        created = datetime.fromisoformat(created_str.replace("Z", "+00:00"))
+        age_days = (now - created.replace(tzinfo=None)).days
+    except (ValueError, AttributeError):
+        age_days = 0
+
+    comment_count = issue.get("comments", 0)
+    assignee = None
+    assignees = issue.get("assignees") or []
+    if assignees:
+        assignee = assignees[0].get("login") if isinstance(assignees[0], dict) else str(assignees[0])
+
+    # Base score
+    score = 0.0
+    reasons = []
+
+    # Age factor: older issues drift down unless actively discussed
+    if age_days > 90 and comment_count < 2:
+        score -= 15
+        reasons.append(f"Dormant: {age_days} days old with only {comment_count} comments")
+    elif age_days > 30:
+        score -= 5
+        reasons.append(f"Aging: {age_days} days old")
+
+    # Activity factor: recent discussion suggests urgency
+    if comment_count > 5:
+        score += 10
+        reasons.append(f"Active discussion: {comment_count} comments")
+    elif comment_count > 0:
+        score += 3
+
+    # Assignment factor: unassigned issues need triage
+    if not assignee:
+        score += 5
+        reasons.append("Unassigned — needs triage")
+
+    # Pipeline signal alignment
+    for signal in signals:
+        title_lower = title.lower()
+        if signal.signal_type == "stale_knowledge" and "stale" in title_lower:
+            score += signal.weight * 20
+            reasons.append(f"Matches signal: {signal.detail}")
+        elif signal.signal_type == "empty_knowledge" and ("harvester" in title_lower or "knowledge" in title_lower):
+            score += signal.weight * 25
+            reasons.append(f"Critical gap: {signal.detail}")
+        elif signal.signal_type == "no_metrics" and "measur" in title_lower:
+            score += signal.weight * 15
+            reasons.append(f"Pipeline gap: {signal.detail}")
+        elif signal.signal_type == "low_coverage" and any(r.lower() in title_lower for r in signal.affected_repos):
+            score += signal.weight * 10
+            reasons.append(f"Coverage gap: {signal.detail}")
+
+    # Keyword boosts
+    keyword_scores = {
+        "broken": 20, "bug": 15, "fix": 10, "error": 12, "fail": 15,
+        "security": 25, "auth": 20, "data loss": 30, "crash": 25,
+        "blocker": 20, "urgent": 15, "critical": 15,
+        "epic": 8, "feature": -3, "nice to have": -10, "someday": -15
+    }
+    title_lower = title.lower()
+    for keyword, boost in keyword_scores.items():
+        if keyword in title_lower:
+            score += boost
+            if boost > 0:
+                reasons.append(f"Keyword match: '{keyword}' (+{boost})")
+
+    # Label-based adjustments
+    for label in labels:
+        lower = label.lower()
+        if lower == "pipeline":
+            score += 5  # Pipeline issues are infrastructure
+        elif lower == "bug":
+            score += 12
+        elif lower == "enhancement":
+            score -= 2
+        elif lower == "documentation":
+            score -= 5
+        elif "epic" in lower:
+            score += 3
+
+    # Determine suggested priority
+    if score >= 40:
+        suggested = "P0"
+    elif score >= 25:
+        suggested = "P1"
+    elif score >= 10:
+        suggested = "P2"
+    elif score >= 0:
+        suggested = "P3"
+    else:
+        suggested = None  # Consider closing or deprioritizing
+
+    return IssueScore(
+        issue_id=issue_id,
+        repo=repo,
+        title=title,
+        current_labels=labels,
+        current_priority=current_priority,
+        suggested_priority=suggested,
+        score=round(score, 1),
+        reasons=reasons if reasons else ["No strong signals"],
+        age_days=age_days,
+        comment_count=comment_count,
+        assignee=assignee
+    )
+
+
+# ============================================================
+# Report Generation
+# ============================================================
+
+def generate_report(
+    scores: List[IssueScore],
+    signals: List[PipelineSignal],
+    org: str,
+    repos_scanned: List[str]
+) -> Dict[str, Any]:
+    """Generate the full priority report."""
+    now = datetime.now(timezone.utc).isoformat()
+
+    # Categorize changes
+    upgrades = [s for s in scores if s.suggested_priority and s.current_priority and
+                PRIORITY_LEVELS.index(s.suggested_priority) < PRIORITY_LEVELS.index(s.current_priority)]
+    downgrades = [s for s in scores if s.suggested_priority and s.current_priority and
+                  PRIORITY_LEVELS.index(s.suggested_priority) > PRIORITY_LEVELS.index(s.current_priority)]
+    new_assignments = [s for s in scores if s.suggested_priority and not s.current_priority]
+    no_change = [s for s in scores if s.suggested_priority == s.current_priority]
+
+    return {
+        "generated_at": now,
+        "org": org,
+        "repos_scanned": repos_scanned,
+        "total_issues": len(scores),
+        "signals": [asdict(s) for s in signals],
+        "summary": {
+            "suggested_upgrades": len(upgrades),
+            "suggested_downgrades": len(downgrades),
+            "suggested_new_priorities": len(new_assignments),
+            "unchanged": len(no_change)
+        },
+        "top_priority": [asdict(s) for s in sorted(scores, key=lambda x: x.score, reverse=True)[:10]],
+        "upgrades": [asdict(s) for s in upgrades],
+        "downgrades": [asdict(s) for s in downgrades],
+        "new_assignments": [asdict(s) for s in new_assignments],
+        "all_scores": [asdict(s) for s in sorted(scores, key=lambda x: x.score, reverse=True)]
+    }
+
+
+def generate_markdown_report(report: Dict[str, Any]) -> str:
+    """Generate human-readable markdown report."""
+    lines = []
+    lines.append("# Priority Rebalancer Report")
+    lines.append(f"**Generated:** {report['generated_at']}")
+    lines.append(f"**Org:** {report['org']}")
+    lines.append(f"**Repos scanned:** {', '.join(report['repos_scanned'])}")
+    lines.append(f"**Issues analyzed:** {report['total_issues']}")
+    lines.append("")
+
+    # Signals
+    if report["signals"]:
+        lines.append("## Pipeline Signals")
+        for sig in report["signals"]:
+            weight_bar = "█" * int(sig["weight"] * 10) + "░" * (10 - int(sig["weight"] * 10))
+            lines.append(f"- [{weight_bar}] **{sig['source']}/{sig['signal_type']}** — {sig['detail']}")
+        lines.append("")
+
+    # Summary
+    s = report["summary"]
+    lines.append("## Summary")
+    lines.append(f"- Suggested upgrades: **{s['suggested_upgrades']}**")
+    lines.append(f"- Suggested downgrades: **{s['suggested_downgrades']}**")
+    lines.append(f"- New priority assignments: **{s['suggested_new_priorities']}**")
+    lines.append(f"- Unchanged: **{s['unchanged']}**")
+    lines.append("")
+
+    # Top 10
+    lines.append("## Top 10 by Score")
+    for i, item in enumerate(report["top_priority"][:10], 1):
+        cur = item["current_priority"] or "none"
+        sug = item["suggested_priority"] or "none"
+        arrow = "↑" if PRIORITY_LEVELS.index(sug) < PRIORITY_LEVELS.index(cur) else "↓" if PRIORITY_LEVELS.index(sug) > PRIORITY_LEVELS.index(cur) else "→"
+        lines.append(f"{i}. **[{item['repo']}#{item['issue_id']}]** {item['title']}")
+        lines.append(f"   Score: {item['score']} | Current: {cur} {arrow} Suggested: {sug}")
+        lines.append(f"   Reasons: {'; '.join(item['reasons'][:3])}")
+        lines.append("")
+
+    # Upgrades
+    if report["upgrades"]:
+        lines.append("## Suggested Upgrades")
+        for item in report["upgrades"]:
+            lines.append(f"- **[{item['repo']}#{item['issue_id']}]** {item['title']}")
+            lines.append(f"  {item['current_priority']} → {item['suggested_priority']} (score: {item['score']})")
+            lines.append(f"  {'; '.join(item['reasons'][:2])}")
+        lines.append("")
+
+    # Downgrades
+    if report["downgrades"]:
+        lines.append("## Suggested Downgrades")
+        for item in report["downgrades"]:
+            lines.append(f"- **[{item['repo']}#{item['issue_id']}]** {item['title']}")
+            lines.append(f"  {item['current_priority']} → {item['suggested_priority']} (score: {item['score']})")
+            lines.append(f"  {'; '.join(item['reasons'][:2])}")
+        lines.append("")
+
+    # New assignments
+    if report["new_assignments"]:
+        lines.append("## New Priority Suggestions (currently unlabelled)")
+        for item in report["new_assignments"][:20]:
+            lines.append(f"- **[{item['repo']}#{item['issue_id']}]** {item['title']}")
+            lines.append(f"  Suggested: {item['suggested_priority']} (score: {item['score']})")
+            lines.append(f"  {'; '.join(item['reasons'][:2])}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def main():
+    parser = argparse.ArgumentParser(description="Priority Rebalancer — re-score issues based on pipeline data")
+    parser.add_argument("--org", default="Timmy_Foundation", help="Gitea org name")
+    parser.add_argument("--repo", help="Single repo to analyze (default: all)")
+    parser.add_argument("--base-url", default="https://forge.alexanderwhitestone.com", help="Gitea base URL")
+    parser.add_argument("--knowledge-dir", default=None, help="Path to knowledge directory")
+    parser.add_argument("--metrics-dir", default=None, help="Path to metrics directory")
+    parser.add_argument("--scripts-dir", default=None, help="Path to scripts directory")
+    parser.add_argument("--output-dir", default=None, help="Path to output directory")
+    parser.add_argument("--dry-run", action="store_true", help="Show what would change without applying")
+    parser.add_argument("--apply", action="store_true", help="Apply priority changes via API")
+    parser.add_argument("--json", action="store_true", help="Output JSON instead of markdown")
+
+    args = parser.parse_args()
+
+    # Resolve paths relative to script location
+    script_dir = Path(__file__).parent
+    repo_root = script_dir.parent
+
+    knowledge_dir = args.knowledge_dir or str(repo_root / "knowledge")
+    metrics_dir = args.metrics_dir or str(repo_root / "metrics")
+    scripts_dir = args.scripts_dir or str(repo_root / "scripts")
+    output_dir = args.output_dir or str(repo_root / "metrics")
+
+    # Get token
+    token = os.environ.get("GITEA_TOKEN")
+    if not token:
+        token_path = os.path.expanduser("~/.config/gitea/token")
+        if os.path.exists(token_path):
+            with open(token_path) as f:
+                token = f.read().strip()
+
+    if not token:
+        print("Error: No Gitea token found. Set GITEA_TOKEN or create ~/.config/gitea/token", file=sys.stderr)
+        sys.exit(1)
+
+    client = GiteaClient(args.base_url, token)
+    now = datetime.utcnow()
+
+    # Collect pipeline signals
+    print("Collecting pipeline signals...", file=sys.stderr)
+    signals = []
+    signals.extend(collect_knowledge_signals(knowledge_dir))
+    signals.extend(collect_staleness_signals(scripts_dir, knowledge_dir))
+    signals.extend(collect_metrics_signals(metrics_dir))
+    print(f"  Found {len(signals)} signals", file=sys.stderr)
+
+    # Get repos
+    if args.repo:
+        repos = [{"name": args.repo}]
+    else:
+        repos = client.get_org_repos(args.org)
+
+    repo_names = [r["name"] for r in repos]
+    print(f"Scanning {len(repo_names)} repos: {', '.join(repo_names[:5])}{'...' if len(repo_names) > 5 else ''}", file=sys.stderr)
+
+    # Score all issues
+    all_scores = []
+    for repo in repos:
+        repo_name = repo["name"]
+        issues = client.get_issues(args.org, repo_name)
+        print(f"  {repo_name}: {len(issues)} open issues", file=sys.stderr)
+
+        for issue in issues:
+            if issue.get("pull_request"):
+                continue
+            score = compute_issue_score(issue, repo_name, signals, now)
+            all_scores.append(score)
+
+    # Generate report
+    report = generate_report(all_scores, signals, args.org, repo_names)
+
+    # Output
+    os.makedirs(output_dir, exist_ok=True)
+
+    if args.json:
+        print(json.dumps(report, indent=2))
+    else:
+        md = generate_markdown_report(report)
+        print(md)
+
+    # Write files
+    report_path = os.path.join(output_dir, "priority_report.json")
+    with open(report_path, "w") as f:
+        json.dump(report, f, indent=2)
+    print(f"\nFull report: {report_path}", file=sys.stderr)
+
+    md_path = os.path.join(output_dir, "priority_suggestions.md")
+    with open(md_path, "w") as f:
+        f.write(generate_markdown_report(report))
+    print(f"Suggestions: {md_path}", file=sys.stderr)
+
+    # Apply changes if requested
+    if args.apply:
+        print("\nApplying priority changes...", file=sys.stderr)
+        applied = 0
+
+        # Get label IDs for priority labels
+        priority_label_map = {}
+        for repo_name in repo_names:
+            labels = client.get_repo_labels(args.org, repo_name)
+            for label in labels:
+                name = label.get("name", "").lower()
+                if name in ("p0", "p1", "p2", "p3"):
+                    priority_label_map[(repo_name, name)] = label["id"]
+
+        for score in all_scores:
+            if score.suggested_priority and score.suggested_priority != score.current_priority:
+                sug_lower = score.suggested_priority.lower()
+                label_id = priority_label_map.get((score.repo, sug_lower))
+                if label_id:
+                    ok = client.add_label_to_issue(args.org, score.repo, score.issue_id, [label_id])
+                    if ok:
+                        applied += 1
+                        print(f"  Applied: [{score.repo}#{score.issue_id}] → {score.suggested_priority}", file=sys.stderr)
+
+                    # Add comment explaining the change
+                    comment = f"**Priority Rebalancer** suggested: **{score.suggested_priority}** (was: {score.current_priority or 'none'})\n\n"
+                    comment += f"Score: {score.score}\n"
+                    comment += f"Reasons:\n"
+                    for r in score.reasons[:5]:
+                        comment += f"- {r}\n"
+                    client.add_comment(args.org, score.repo, score.issue_id, comment)
+
+        print(f"Applied {applied} priority changes", file=sys.stderr)
+
+    elif args.dry_run:
+        print(f"\nDry run — {report['summary']['suggested_upgrades'] + report['summary']['suggested_downgrades'] + report['summary']['suggested_new_priorities']} changes would be applied", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/session_pair_harvester.py

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

scripts/test_knowledge_staleness.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""Tests for scripts/knowledge_staleness_check.py — 8 tests."""
+
+import json
+import os
+import sys
+import tempfile
+
+sys.path.insert(0, os.path.dirname(__file__) or ".")
+import importlib.util
+spec = importlib.util.spec_from_file_location("ks", os.path.join(os.path.dirname(__file__) or ".", "knowledge_staleness_check.py"))
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+check_staleness = mod.check_staleness
+fix_hashes = mod.fix_hashes
+compute_file_hash = mod.compute_file_hash
+
+
+def test_fresh_entry():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        src = os.path.join(tmpdir, "source.py")
+        with open(src, "w") as f:
+            f.write("print('hello')")
+        h = compute_file_hash(src)
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "hello", "source_file": "source.py", "source_hash": h}]}, f)
+        results = check_staleness(idx, tmpdir)
+        assert results[0]["status"] == "fresh"
+        print("PASS: test_fresh_entry")
+
+
+def test_stale_entry():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        src = os.path.join(tmpdir, "source.py")
+        with open(src, "w") as f:
+            f.write("original content")
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "old", "source_file": "source.py", "source_hash": "sha256:wrong"}]}, f)
+        # Now change the source
+        with open(src, "w") as f:
+            f.write("modified content")
+        results = check_staleness(idx, tmpdir)
+        assert results[0]["status"] == "stale"
+        print("PASS: test_stale_entry")
+
+
+def test_missing_source():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "gone", "source_file": "nonexistent.py", "source_hash": "sha256:abc"}]}, f)
+        results = check_staleness(idx, tmpdir)
+        assert results[0]["status"] == "missing_source"
+        print("PASS: test_missing_source")
+
+
+def test_no_hash():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        src = os.path.join(tmpdir, "source.py")
+        with open(src, "w") as f:
+            f.write("content")
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "no hash", "source_file": "source.py"}]}, f)
+        results = check_staleness(idx, tmpdir)
+        assert results[0]["status"] == "no_hash"
+        assert results[0]["current_hash"].startswith("sha256:")
+        print("PASS: test_no_hash")
+
+
+def test_no_source_field():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "orphan"}]}, f)
+        results = check_staleness(idx, tmpdir)
+        assert results[0]["status"] == "no_source"
+        print("PASS: test_no_source_field")
+
+
+def test_fix_hashes():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        src = os.path.join(tmpdir, "source.py")
+        with open(src, "w") as f:
+            f.write("content for hashing")
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [{"fact": "needs hash", "source_file": "source.py"}]}, f)
+        fixed = fix_hashes(idx, tmpdir)
+        assert fixed == 1
+        # Verify hash was added
+        with open(idx) as f:
+            data = json.load(f)
+        assert data["facts"][0]["source_hash"].startswith("sha256:")
+        print("PASS: test_fix_hashes")
+
+
+def test_empty_index():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": []}, f)
+        results = check_staleness(idx, tmpdir)
+        assert results == []
+        print("PASS: test_empty_index")
+
+
+def test_compute_hash_nonexistent():
+    h = compute_file_hash("/nonexistent/path/file.py")
+    assert h is None
+    print("PASS: test_compute_hash_nonexistent")
+
+
+def run_all():
+    test_fresh_entry()
+    test_stale_entry()
+    test_missing_source()
+    test_no_hash()
+    test_no_source_field()
+    test_fix_hashes()
+    test_empty_index()
+    test_compute_hash_nonexistent()
+    print("\nAll 8 tests passed!")
+
+
+if __name__ == "__main__":
+    run_all()

scripts/test_priority_rebalancer.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python3
+"""
+Tests for Priority Rebalancer
+"""
+
+import json
+import os
+import sys
+import tempfile
+from datetime import datetime, timedelta
+from pathlib import Path
+
+# Add script dir to path
+sys.path.insert(0, str(Path(__file__).parent))
+
+from priority_rebalancer import (
+    GiteaClient,
+    IssueScore,
+    PipelineSignal,
+    compute_issue_score,
+    collect_knowledge_signals,
+    collect_metrics_signals,
+    extract_priority,
+    generate_report,
+    generate_markdown_report,
+    PRIORITY_LEVELS,
+)
+
+# ============================================================
+# Test Helpers
+# ============================================================
+
+PASS = 0
+FAIL = 0
+
+def test(name):
+    def decorator(fn):
+        global PASS, FAIL
+        try:
+            fn()
+            PASS += 1
+            print(f"  [PASS] {name}")
+        except Exception as e:
+            FAIL += 1
+            print(f"  [FAIL] {name}: {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")
+
+
+# ============================================================
+# Priority Extraction Tests
+# ============================================================
+
+print("=== Priority Rebalancer Tests ===\n")
+
+print("-- Priority Extraction --")
+
+@test("extract P0 from label")
+def _():
+    assert_eq(extract_priority(["P0", "bug"]), "P0")
+
+@test("extract P1 from priority:high")
+def _():
+    assert_eq(extract_priority(["priority:high"]), "P1")
+
+@test("extract P2 from priority:medium")
+def _():
+    assert_eq(extract_priority(["priority:medium"]), "P2")
+
+@test("extract P3 from priority:low")
+def _():
+    assert_eq(extract_priority(["priority:low"]), "P3")
+
+@test("returns None for no priority")
+def _():
+    assert_eq(extract_priority(["bug", "enhancement"]), None)
+
+@test("case insensitive")
+def _():
+    assert_eq(extract_priority(["p1"]), "P1")
+    assert_eq(extract_priority(["PRIORITY:CRITICAL"]), "P0")
+
+
+# ============================================================
+# Issue Scoring Tests
+# ============================================================
+
+print("\n-- Issue Scoring --")
+
+def make_issue(**kwargs):
+    defaults = {
+        "number": 1,
+        "title": "Test issue",
+        "labels": [],
+        "created_at": (datetime.utcnow() - timedelta(days=5)).isoformat() + "Z",
+        "comments": 0,
+        "assignees": None,
+    }
+    defaults.update(kwargs)
+    return defaults
+
+@test("bug gets score boost")
+def _():
+    issue = make_issue(title="Incorrect output format", labels=["bug"])
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(score.score > 0, f"Bug should boost score, got {score.score}")
+    # Bug label alone should be P2 or P3 (not P0)
+    assert_true(score.suggested_priority in ("P2", "P3"),
+                f"Bug label alone should be P2/P3, got {score.suggested_priority}")
+
+@test("security gets high score")
+def _():
+    issue = make_issue(title="Security: auth bypass", labels=["bug"])
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(score.score >= 25, f"Security should score high, got {score.score}")
+
+@test("old dormant issue gets penalized")
+def _():
+    issue = make_issue(
+        title="Some old feature",
+        created_at=(datetime.utcnow() - timedelta(days=120)).isoformat() + "Z",
+        comments=0
+    )
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(score.score < 0, f"Old dormant should be negative, got {score.score}")
+    assert_true(any("Dormant" in r for r in score.reasons), "Should mention dormancy")
+
+@test("active discussion boosts score")
+def _():
+    issue = make_issue(title="Important fix", comments=8)
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(score.score > 5, f"Active discussion should boost, got {score.score}")
+    assert_true(any("Active" in r for r in score.reasons))
+
+@test("unassigned gets slight boost")
+def _():
+    issue = make_issue(title="Fix bug", assignees=None)
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(any("Unassigned" in r for r in score.reasons))
+
+@test("assigned issue notes assignee")
+def _():
+    issue = make_issue(title="Fix bug", assignees=[{"login": "alice"}])
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_eq(score.assignee, "alice")
+
+@test("nice-to-have gets penalized")
+def _():
+    issue = make_issue(title="Nice to have: fancy animation")
+    score = compute_issue_score(issue, "test-repo", [], datetime.utcnow())
+    assert_true(score.score < 0, f"Nice-to-have should be negative, got {score.score}")
+
+
+# ============================================================
+# Pipeline Signal Tests
+# ============================================================
+
+print("\n-- Pipeline Signals --")
+
+@test("signal alignment boosts matching issues")
+def _():
+    signals = [PipelineSignal(
+        source="knowledge",
+        signal_type="stale_knowledge",
+        weight=0.8,
+        detail="20 stale facts"
+    )]
+    issue = make_issue(title="Fix stale knowledge entries")
+    score = compute_issue_score(issue, "test-repo", signals, datetime.utcnow())
+    assert_true(any("Matches signal" in r for r in score.reasons))
+
+@test("empty knowledge boosts harvester issues")
+def _():
+    signals = [PipelineSignal(
+        source="knowledge",
+        signal_type="empty_knowledge",
+        weight=0.7,
+        detail="0 facts"
+    )]
+    issue = make_issue(title="Implement harvester pipeline")
+    score = compute_issue_score(issue, "test-repo", signals, datetime.utcnow())
+    assert_true(any("Critical gap" in r for r in score.reasons))
+
+
+# ============================================================
+# Knowledge Signal Collection Tests
+# ============================================================
+
+print("\n-- Knowledge Signal Collection --")
+
+@test("missing index generates signal")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        signals = collect_knowledge_signals(tmpdir)
+        assert_true(len(signals) > 0)
+        assert_eq(signals[0].signal_type, "missing_index")
+
+@test("empty knowledge generates signal")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": []}, f)
+        signals = collect_knowledge_signals(tmpdir)
+        assert_true(any(s.signal_type == "empty_knowledge" for s in signals))
+
+@test("corrupt index generates signal")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            f.write("not json {{{")
+        signals = collect_knowledge_signals(tmpdir)
+        assert_true(any(s.signal_type == "corrupt_index" for s in signals))
+
+@test("knowledge with facts passes")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idx = os.path.join(tmpdir, "index.json")
+        with open(idx, "w") as f:
+            json.dump({"facts": [
+                {"id": 1, "repo": "test", "status": "fresh"},
+                {"id": 2, "repo": "test", "status": "fresh"},
+            ]}, f)
+        signals = collect_knowledge_signals(tmpdir)
+        # Should not generate missing_index or empty_knowledge
+        assert_false(any(s.signal_type in ("missing_index", "empty_knowledge") for s in signals))
+
+
+# ============================================================
+# Metrics Signal Collection Tests
+# ============================================================
+
+print("\n-- Metrics Signal Collection --")
+
+@test("empty metrics dir generates signal")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        signals = collect_metrics_signals(tmpdir)
+        assert_true(any(s.signal_type == "no_metrics" for s in signals))
+
+@test("metrics with files passes")
+def _():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Create files (simulating real metrics dir with .gitkeep + actual files)
+        with open(os.path.join(tmpdir, ".gitkeep"), "w") as f:
+            f.write("")
+        with open(os.path.join(tmpdir, "report.json"), "w") as f:
+            f.write("{}")
+        signals = collect_metrics_signals(tmpdir)
+        assert_false(any(s.signal_type == "no_metrics" for s in signals))
+
+
+# ============================================================
+# Report Generation Tests
+# ============================================================
+
+print("\n-- Report Generation --")
+
+@test("report has correct structure")
+def _():
+    scores = [
+        IssueScore(1, "repo1", "Bug fix", ["bug"], None, "P1", 30.0, ["test"], 5, 3, None),
+        IssueScore(2, "repo1", "Feature", ["enhancement"], "P3", None, -5.0, ["test"], 60, 0, "alice"),
+    ]
+    signals = [PipelineSignal("knowledge", "stale_knowledge", 0.5, "10 stale")]
+    report = generate_report(scores, signals, "test-org", ["repo1"])
+
+    assert_eq(report["org"], "test-org")
+    assert_eq(report["total_issues"], 2)
+    assert_true("generated_at" in report)
+    assert_true("summary" in report)
+    assert_true("top_priority" in report)
+    assert_eq(report["summary"]["suggested_new_priorities"], 1)
+
+@test("markdown report is non-empty")
+def _():
+    scores = [IssueScore(1, "repo1", "Test", ["bug"], None, "P2", 15.0, ["reason"], 5, 0, None)]
+    report = generate_report(scores, [], "test-org", ["repo1"])
+    md = generate_markdown_report(report)
+    assert_true(len(md) > 100)
+    assert_true("Priority Rebalancer Report" in md)
+    assert_true("Top 10" in md)
+
+
+# ============================================================
+# Summary
+# ============================================================
+
+print(f"\n=== Summary ===")
+print(f"Total: {PASS + FAIL} | Passed: {PASS} | Failed: {FAIL}")
+
+if FAIL > 0:
+    sys.exit(1)

scripts/test_session_pair_harvester.py

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