fix: add directory exclusions for scan performance (#170)

Analyzes cron jobs, docs, scripts, session transcripts, and shell history to find manual processes that could be automated.

Outputs ranked proposals with confidence scores and impact ratings.

scripts/automation_opportunity_finder.py

@@ -0,0 +1,554 @@
+#!/usr/bin/env python3
+"""
+Automation Opportunity Finder — Scan fleet for manual processes that could be automated.
+
+Analyzes:
+1. Cron jobs — finds manual steps between scheduled tasks
+2. Documentation — extracts TODO/FIXME/manual-step patterns
+3. Scripts — detects repeated command sequences
+4. Session transcripts — finds repeated tool-call patterns
+
+Usage:
+    python3 scripts/automation_opportunity_finder.py --hermes-home ~/.hermes
+    python3 scripts/automation_opportunity_finder.py --hermes-home ~/.hermes --json
+    python3 scripts/automation_opportunity_finder.py --hermes-home ~/.hermes --output proposals.json
+"""
+
+import argparse
+import json
+import os
+import re
+import sys
+from collections import Counter, defaultdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+
+# ---------------------------------------------------------------------------
+# Patterns that signal manual work
+# ---------------------------------------------------------------------------
+
+MANUAL_STEP_PATTERNS = [
+    # Explicit manual markers
+    (r"(?i)\bTODO[:\s]", "todo"),
+    (r"(?i)\bFIXME[:\s]", "fixme"),
+    (r"(?i)\bMANUAL[:\s]", "manual_step"),
+    (r"(?i)\bHACK[:\s]", "hack"),
+    (r"(?i)\bWORKAROUND[:\s]", "workaround"),
+    # Step-by-step instructions in docs
+    (r"(?i)^(\d+)[.\)]\s+(run|execute|ssh|scp|curl|cd|make|docker|ansible|git)", "sequential_step"),
+    # Explicitly manual operations
+    (r"(?i)\bmanually\b", "manual_keyword"),
+    (r"(?i)\bby hand\b", "manual_keyword"),
+    (r"(?i)\bdon\'?t forget to\b", "manual_keyword"),
+    (r"(?i)\bremember to\b", "manual_keyword"),
+    (r"(?i)\bmake sure to\b", "manual_keyword"),
+]
+
+# Shell commands that appear frequently in runbooks — signal automatable workflows
+SHELL_COMMAND_PATTERNS = [
+    "ssh ", "scp ", "rsync ", "curl ", "wget ",
+    "docker ", "docker-compose ", "kubectl ",
+    "ansible-playbook ", "terraform ", "systemctl ",
+    "systemctl restart", "systemctl status",
+    "git push", "git pull", "git merge", "git checkout",
+    "pip install", "npm install", "cargo build",
+]
+
+# Directories to skip during scans — large/uninteresting trees
+EXCLUDE_DIRS = frozenset({
+    "node_modules", "venv", ".venv", "__pycache__", ".git",
+    "site-packages", "dist", "build", ".tox", ".mypy_cache",
+    ".pytest_cache", "coverage", ".next", "vendor",
+    "skills",  # hermes skills dir is huge
+    "audio_cache", "skins", "profiles",
+})
+
+# Session tool calls that appear repeatedly — candidates for workflow automation
+TOOL_SEQUENCE_MIN_OCCURRENCES = 3
+
+
+# ---------------------------------------------------------------------------
+# Analyzers
+# ---------------------------------------------------------------------------
+
+def analyze_cron_jobs(hermes_home: str) -> List[Dict[str, Any]]:
+    """Analyze cron job definitions for automation gaps."""
+    proposals = []
+    cron_dir = Path(hermes_home) / "cron"
+    jobs_file = cron_dir / "jobs.json"
+
+    if not jobs_file.exists():
+        # Try YAML format
+        for ext in (".yaml", ".yml"):
+            alt = cron_dir / f"jobs{ext}"
+            if alt.exists():
+                jobs_file = alt
+                break
+
+    if not jobs_file.exists():
+        return proposals
+
+    try:
+        if jobs_file.suffix == ".json":
+            with open(jobs_file) as f:
+                jobs = json.load(f)
+        else:
+            try:
+                import yaml
+                with open(jobs_file) as f:
+                    jobs = yaml.safe_load(f)
+            except ImportError:
+                return proposals
+    except (json.JSONDecodeError, Exception):
+        return proposals
+
+    if not isinstance(jobs, list):
+        return proposals
+
+    # Look for disabled jobs (someone turned them off — might need a different approach)
+    disabled = [j for j in jobs if not j.get("enabled", True)]
+    if disabled:
+        names = [j.get("name", j.get("id", "?")) for j in disabled[:5]]
+        proposals.append({
+            "category": "cron_disabled",
+            "title": f"{len(disabled)} disabled cron job(s) may need automation rework",
+            "description": f"These jobs were disabled: {', '.join(names)}. Investigate why and whether a different automation approach is needed.",
+            "confidence": 0.7,
+            "impact": "medium",
+            "sources": [str(jobs_file)],
+        })
+
+    # Look for jobs with high error counts
+    error_jobs = [j for j in jobs if j.get("last_status") == "error"]
+    if error_jobs:
+        names = [j.get("name", j.get("id", "?")) for j in error_jobs[:5]]
+        proposals.append({
+            "category": "cron_errors",
+            "title": f"{len(error_jobs)} cron job(s) failing — may need automation rework",
+            "description": f"Jobs with errors: {', '.join(names)}. Failure patterns suggest missing dependencies or fragile automation.",
+            "confidence": 0.8,
+            "impact": "high",
+            "sources": [str(jobs_file)],
+        })
+
+    # Look for jobs with delivery errors (platform issues)
+    delivery_errors = [j for j in jobs if j.get("last_delivery_error")]
+    if delivery_errors:
+        proposals.append({
+            "category": "cron_delivery",
+            "title": f"{len(delivery_errors)} cron job(s) have delivery failures",
+            "description": "Delivery failures suggest missing retry logic or platform integration gaps.",
+            "confidence": 0.75,
+            "impact": "medium",
+            "sources": [str(jobs_file)],
+        })
+
+    # Look for jobs on short intervals that could be event-driven
+    for job in jobs:
+        schedule = job.get("schedule", "")
+        # Check for very frequent schedules (every minute, every 5 min)
+        if isinstance(schedule, str) and re.match(r"^\*\/([1-5])\s", schedule):
+            proposals.append({
+                "category": "cron_frequency",
+                "title": f"Job '{job.get('name', job.get('id', '?'))}' runs every {schedule.split()[0]} — consider event-driven",
+                "description": f"High-frequency cron ({schedule}) may be better as event-driven or daemon.",
+                "confidence": 0.6,
+                "impact": "low",
+                "sources": [str(jobs_file)],
+            })
+
+    return proposals
+
+
+def analyze_documents(root_dirs: List[str]) -> List[Dict[str, Any]]:
+    """Scan documentation for manual step patterns."""
+    proposals = []
+    doc_extensions = {".md", ".txt", ".rst", ".adoc"}
+    findings_by_category = defaultdict(list)
+
+    for root_dir in root_dirs:
+        root = Path(root_dir)
+        if not root.exists():
+            continue
+
+        for path in root.rglob("*"):
+            if path.is_dir():
+                continue
+            if path.suffix not in doc_extensions:
+                continue
+            # Skip excluded dirs and hidden dirs
+            parts = path.relative_to(root).parts if root in path.parents or root == path.parent else path.parts
+            if any(p.startswith(".") or p in EXCLUDE_DIRS for p in parts):
+                continue
+            if len(parts) > 8:
+                continue
+
+            try:
+                content = path.read_text(errors="replace")
+            except (PermissionError, OSError):
+                continue
+
+            lines = content.split("\n")
+            for i, line in enumerate(lines):
+                stripped = line.strip()
+                if not stripped:
+                    continue
+
+                for pattern, category in MANUAL_STEP_PATTERNS:
+                    if re.search(pattern, stripped):
+                        findings_by_category[category].append({
+                            "file": str(path),
+                            "line": i + 1,
+                            "text": stripped[:200],
+                        })
+
+    # Generate proposals from findings
+    for category, findings in findings_by_category.items():
+        if len(findings) < 2:
+            continue
+
+        file_count = len(set(f["file"] for f in findings))
+        proposals.append({
+            "category": f"manual_{category}",
+            "title": f"{len(findings)} '{category}' markers across {file_count} doc(s)",
+            "description": f"Found in: {', '.join(set(Path(f['file']).name for f in findings[:5]))}",
+            "confidence": 0.65,
+            "impact": "medium",
+            "sources": list(set(f["file"] for f in findings[:10])),
+            "details": findings[:5],  # sample
+        })
+
+    return proposals
+
+
+def analyze_scripts(root_dirs: List[str]) -> List[Dict[str, Any]]:
+    """Detect repeated command sequences in scripts."""
+    proposals = []
+    script_extensions = {".py", ".sh", ".bash", ".zsh"}
+    command_counter = Counter()
+    command_locations = defaultdict(list)
+
+    for root_dir in root_dirs:
+        root = Path(root_dir)
+        if not root.exists():
+            continue
+
+        for path in root.rglob("*"):
+            if path.is_dir():
+                continue
+            if path.suffix not in script_extensions:
+                continue
+            parts = path.relative_to(root).parts if root in path.parents or root == path.parent else path.parts
+            if any(p.startswith(".") or p in EXCLUDE_DIRS for p in parts):
+                continue
+            if len(parts) > 8:
+                continue
+
+            try:
+                content = path.read_text(errors="replace")
+            except (PermissionError, OSError):
+                continue
+
+            lines = content.split("\n")
+            for i, line in enumerate(lines):
+                stripped = line.strip()
+                if not stripped or stripped.startswith("#"):
+                    continue
+
+                for cmd_prefix in SHELL_COMMAND_PATTERNS:
+                    if cmd_prefix in stripped:
+                        # Normalize the command
+                        normalized = re.sub(r"\s+", " ", stripped)[:120]
+                        command_counter[normalized] += 1
+                        command_locations[normalized].append(f"{path}:{i+1}")
+
+    # Proposals for commands appearing 3+ times
+    for cmd, count in command_counter.most_common(20):
+        if count < 3:
+            break
+        locs = command_locations[cmd]
+        file_count = len(set(loc.split(":")[0] for loc in locs))
+        proposals.append({
+            "category": "repeated_command",
+            "title": f"Command repeated {count}x across {file_count} file(s): {cmd[:80]}",
+            "description": f"Locations: {', '.join(locs[:3])}",
+            "confidence": min(0.5 + (count * 0.1), 0.95),
+            "impact": "medium",
+            "sources": list(set(loc.split(":")[0] for loc in locs)),
+        })
+
+    return proposals
+
+
+def analyze_session_transcripts(session_dirs: List[str]) -> List[Dict[str, Any]]:
+    """Find repeated tool-call patterns in session transcripts."""
+    proposals = []
+    tool_sequence_counter = Counter()
+    tool_sequence_examples = {}
+
+    for session_dir in session_dirs:
+        session_path = Path(session_dir)
+        if not session_path.exists():
+            continue
+
+        for path in session_path.rglob("*.jsonl"):
+            try:
+                content = path.read_text(errors="replace")
+            except (PermissionError, OSError):
+                continue
+
+            # Extract tool calls in sequence
+            tool_sequence = []
+            for line in content.split("\n"):
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    msg = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+
+                # Look for tool calls in assistant messages
+                if msg.get("role") == "assistant" and msg.get("tool_calls"):
+                    for tc in msg["tool_calls"]:
+                        func_name = tc.get("function", {}).get("name", "?")
+                        tool_sequence.append(func_name)
+
+            # Find 2-call sequences
+            for i in range(len(tool_sequence) - 1):
+                seq = (tool_sequence[i], tool_sequence[i + 1])
+                tool_sequence_counter[seq] += 1
+                if seq not in tool_sequence_examples:
+                    tool_sequence_examples[seq] = str(path.name)
+
+            # Find 3-call sequences
+            for i in range(len(tool_sequence) - 2):
+                seq = (tool_sequence[i], tool_sequence[i + 1], tool_sequence[i + 2])
+                tool_sequence_counter[seq] += 1
+                if seq not in tool_sequence_examples:
+                    tool_sequence_examples[seq] = str(path.name)
+
+    # Generate proposals for frequently repeated sequences
+    for seq, count in tool_sequence_counter.most_common(20):
+        if count < TOOL_SEQUENCE_MIN_OCCURRENCES:
+            break
+        seq_str = " -> ".join(seq)
+        proposals.append({
+            "category": "tool_sequence",
+            "title": f"Tool sequence '{seq_str}' repeated {count} times",
+            "description": f"Consider creating a workflow/skill that automates this sequence.",
+            "confidence": min(0.5 + (count * 0.05), 0.9),
+            "impact": "medium",
+            "sources": [tool_sequence_examples.get(seq, "unknown")],
+        })
+
+    return proposals
+
+
+def analyze_shell_history(root_dirs: List[str]) -> List[Dict[str, Any]]:
+    """Find repeated shell commands from history files."""
+    proposals = []
+    command_counter = Counter()
+
+    for root_dir in root_dirs:
+        root = Path(root_dir)
+        history_files = []
+
+        # Look for shell history files
+        for name in (".bash_history", ".zsh_history", ".python_history"):
+            p = root / name
+            if p.exists():
+                history_files.append(p)
+
+        # Also check in hermes home
+        for p in root.glob("**/*history*"):
+            if p.is_file() and p.suffix in ("", ".txt", ".log"):
+                history_files.append(p)
+
+        for hf in history_files:
+            try:
+                content = hf.read_text(errors="replace")
+            except (PermissionError, OSError):
+                continue
+
+            for line in content.split("\n"):
+                stripped = line.strip()
+                # ZSH history format: ": 1234567890:0;command"
+                stripped = re.sub(r"^:\s*\d+:\d+;", "", stripped)
+                if not stripped or len(stripped) < 5:
+                    continue
+                # Skip trivial commands
+                if stripped in ("ls", "cd", "pwd", "clear", "exit"):
+                    continue
+                command_counter[stripped] += 1
+
+    for cmd, count in command_counter.most_common(10):
+        if count < 5:
+            break
+        proposals.append({
+            "category": "shell_repetition",
+            "title": f"Shell command run {count}+ times: {cmd[:80]}",
+            "description": "Frequently repeated shell command — candidate for alias, function, or script.",
+            "confidence": min(0.4 + (count * 0.05), 0.85),
+            "impact": "low",
+            "sources": ["shell_history"],
+        })
+
+    return proposals
+
+
+# ---------------------------------------------------------------------------
+# Proposal output
+# ---------------------------------------------------------------------------
+
+def deduplicate_proposals(proposals: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Remove duplicate proposals based on title similarity."""
+    seen_titles = set()
+    unique = []
+    for p in proposals:
+        # Normalize title for dedup
+        key = re.sub(r"\d+", "N", p["title"]).lower()
+        if key not in seen_titles:
+            seen_titles.add(key)
+            unique.append(p)
+    return unique
+
+
+def rank_proposals(proposals: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Sort proposals by impact * confidence (highest first)."""
+    impact_weight = {"critical": 1.0, "high": 0.8, "medium": 0.5, "low": 0.2}
+    return sorted(
+        proposals,
+        key=lambda p: impact_weight.get(p.get("impact", "low"), 0.2) * p.get("confidence", 0.5),
+        reverse=True,
+    )
+
+
+def format_text_report(proposals: List[Dict[str, Any]]) -> str:
+    """Format proposals as human-readable text."""
+    if not proposals:
+        return "No automation opportunities found."
+
+    lines = [
+        "=" * 70,
+        "  AUTOMATION OPPORTUNITY REPORT",
+        f"  Generated: {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M UTC')}",
+        f"  Proposals: {len(proposals)}",
+        "=" * 70,
+        "",
+    ]
+
+    for i, p in enumerate(proposals, 1):
+        score = p.get("confidence", 0.5) * {"critical": 1.0, "high": 0.8, "medium": 0.5, "low": 0.2}.get(p.get("impact", "low"), 0.2)
+        lines.append(f"[{i}] {p['title']}")
+        lines.append(f"    Category: {p['category']}  |  Impact: {p.get('impact','?')}  |  Confidence: {p.get('confidence',0):.0%}  |  Score: {score:.2f}")
+        lines.append(f"    {p['description']}")
+        if p.get("sources"):
+            lines.append(f"    Sources: {', '.join(p['sources'][:3])}")
+        lines.append("")
+
+    # Summary by category
+    cat_counts = Counter(p["category"] for p in proposals)
+    lines.append("-" * 70)
+    lines.append("Summary by category:")
+    for cat, count in cat_counts.most_common():
+        lines.append(f"  {cat}: {count}")
+
+    return "\n".join(lines)
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    parser = argparse.ArgumentParser(description="Find automation opportunities across the fleet")
+    parser.add_argument("--hermes-home", default=os.path.expanduser("~/.hermes"),
+                        help="Path to hermes home directory (default: ~/.hermes)")
+    parser.add_argument("--scan-dirs", nargs="*",
+                        help="Additional directories to scan (default: hermes-home + cwd)")
+    parser.add_argument("--session-dirs", nargs="*",
+                        help="Session transcript directories (default: hermes-home/sessions)")
+    parser.add_argument("--json", action="store_true", help="Output as JSON")
+    parser.add_argument("--output", "-o", help="Write proposals to file")
+    parser.add_argument("--min-confidence", type=float, default=0.3,
+                        help="Minimum confidence threshold (default: 0.3)")
+    parser.add_argument("--categories", nargs="*",
+                        help="Only include these categories (cron, docs, scripts, sessions, shell)")
+    args = parser.parse_args()
+
+    hermes_home = os.path.expanduser(args.hermes_home)
+
+    # Default scan directories
+    scan_dirs = [hermes_home, "."]
+    if args.scan_dirs:
+        scan_dirs.extend(args.scan_dirs)
+
+    session_dirs = [os.path.join(hermes_home, "sessions")]
+    if args.session_dirs:
+        session_dirs.extend(args.session_dirs)
+
+    # Also check common session locations
+    for subdir in ("transcripts", "session-db"):
+        p = os.path.join(hermes_home, subdir)
+        if os.path.isdir(p):
+            session_dirs.append(p)
+
+    categories = set(args.categories) if args.categories else {"cron", "docs", "scripts", "sessions", "shell"}
+
+    # Run analyzers
+    all_proposals = []
+
+    if "cron" in categories:
+        all_proposals.extend(analyze_cron_jobs(hermes_home))
+
+    if "docs" in categories:
+        all_proposals.extend(analyze_documents(scan_dirs))
+
+    if "scripts" in categories:
+        all_proposals.extend(analyze_scripts(scan_dirs))
+
+    if "sessions" in categories:
+        all_proposals.extend(analyze_session_transcripts(session_dirs))
+
+    if "shell" in categories:
+        all_proposals.extend(analyze_shell_history(scan_dirs))
+
+    # Deduplicate and rank
+    all_proposals = deduplicate_proposals(all_proposals)
+    all_proposals = rank_proposals(all_proposals)
+
+    # Filter by confidence
+    all_proposals = [p for p in all_proposals if p.get("confidence", 0) >= args.min_confidence]
+
+    # Output
+    output = {
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "hermes_home": hermes_home,
+        "scan_dirs": scan_dirs,
+        "total_proposals": len(all_proposals),
+        "proposals": all_proposals,
+    }
+
+    if args.json:
+        result = json.dumps(output, indent=2)
+    else:
+        result = format_text_report(all_proposals)
+
+    if args.output:
+        with open(args.output, "w") as f:
+            if args.json:
+                json.dump(output, f, indent=2)
+            else:
+                f.write(result)
+        print(f"Written to {args.output}", file=sys.stderr)
+    else:
+        print(result)
+
+    return 0 if all_proposals else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/harvester.py

@@ -1,447 +0,0 @@
-#!/usr/bin/env python3
-"""
-harvester.py — Extract durable knowledge from Hermes session transcripts.
-
-Combines session_reader + extraction prompt + LLM inference to pull
-facts, pitfalls, patterns, and tool quirks from finished sessions.
-
-Usage:
-    python3 harvester.py --session ~/.hermes/sessions/session_xxx.jsonl --output knowledge/
-    python3 harvester.py --batch --since 2026-04-01 --limit 100
-    python3 harvester.py --session session.jsonl --dry-run  # Preview without writing
-"""
-
-import argparse
-import json
-import os
-import sys
-import time
-import hashlib
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-# Add scripts dir to path for sibling imports
-SCRIPT_DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, str(SCRIPT_DIR))
-
-from session_reader import read_session, extract_conversation, truncate_for_context, messages_to_text
-
-# --- Configuration ---
-
-DEFAULT_API_BASE = os.environ.get("HARVESTER_API_BASE", "https://api.nousresearch.com/v1")
-DEFAULT_API_KEY = os.environ.get("HARVESTER_API_KEY", "")
-DEFAULT_MODEL = os.environ.get("HARVESTER_MODEL", "xiaomi/mimo-v2-pro")
-KNOWLEDGE_DIR = os.environ.get("HARVESTER_KNOWLEDGE_DIR", "knowledge")
-PROMPT_PATH = os.environ.get("HARVESTER_PROMPT_PATH", str(SCRIPT_DIR.parent / "templates" / "harvest-prompt.md"))
-
-# Where to look for API keys if not set via env
-API_KEY_PATHS = [
-    os.path.expanduser("~/.config/nous/key"),
-    os.path.expanduser("~/.hermes/keymaxxing/active/minimax.key"),
-    os.path.expanduser("~/.config/openrouter/key"),
-]
-
-
-def find_api_key() -> str:
-    """Find API key from common locations."""
-    for path in API_KEY_PATHS:
-        if os.path.exists(path):
-            with open(path) as f:
-                key = f.read().strip()
-                if key:
-                    return key
-    return ""
-
-
-def load_extraction_prompt() -> str:
-    """Load the extraction prompt template."""
-    path = Path(PROMPT_PATH)
-    if not path.exists():
-        print(f"ERROR: Extraction prompt not found at {path}", file=sys.stderr)
-        print("Expected templates/harvest-prompt.md from issue #7", file=sys.stderr)
-        sys.exit(1)
-    return path.read_text(encoding='utf-8')
-
-
-def call_llm(prompt: str, transcript: str, api_base: str, api_key: str, model: str) -> Optional[list[dict]]:
-    """Call the LLM API to extract knowledge from a transcript."""
-    import urllib.request
-    
-    messages = [
-        {"role": "system", "content": prompt},
-        {"role": "user", "content": f"Extract knowledge from this session transcript:\n\n{transcript}"}
-    ]
-    
-    payload = json.dumps({
-        "model": model,
-        "messages": messages,
-        "temperature": 0.1,  # Low temp for consistent extraction
-        "max_tokens": 4096
-    }).encode('utf-8')
-    
-    req = urllib.request.Request(
-        f"{api_base}/chat/completions",
-        data=payload,
-        headers={
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json"
-        },
-        method="POST"
-    )
-    
-    try:
-        with urllib.request.urlopen(req, timeout=60) as resp:
-            result = json.loads(resp.read().decode('utf-8'))
-            content = result["choices"][0]["message"]["content"]
-            return parse_extraction_response(content)
-    except Exception as e:
-        print(f"ERROR: LLM API call failed: {e}", file=sys.stderr)
-        return None
-
-
-def parse_extraction_response(content: str) -> Optional[list[dict]]:
-    """Parse the LLM response to extract knowledge items.
-    
-    Handles various response formats: raw JSON, markdown-wrapped JSON, etc.
-    """
-    # Try direct JSON parse first
-    try:
-        data = json.loads(content)
-        if isinstance(data, dict) and 'knowledge' in data:
-            return data['knowledge']
-        if isinstance(data, list):
-            return data
-    except json.JSONDecodeError:
-        pass
-    
-    # Try extracting JSON from markdown code blocks
-    import re
-    json_match = re.search(r'```(?:json)?\s*({.*?})\s*```', content, re.DOTALL)
-    if json_match:
-        try:
-            data = json.loads(json_match.group(1))
-            if isinstance(data, dict) and 'knowledge' in data:
-                return data['knowledge']
-            if isinstance(data, list):
-                return data
-        except json.JSONDecodeError:
-            pass
-    
-    # Try finding any JSON object with knowledge array
-    json_match = re.search(r'({[^{}]*"knowledge"[^{}]*[[sS]*?][^{}]*})', content)
-    if json_match:
-        try:
-            data = json.loads(json_match.group(1))
-            return data.get('knowledge', [])
-        except json.JSONDecodeError:
-            pass
-    
-    print(f"WARNING: Could not parse LLM response as JSON", file=sys.stderr)
-    print(f"Response preview: {content[:500]}", file=sys.stderr)
-    return None
-
-
-def load_existing_knowledge(knowledge_dir: str) -> dict:
-    """Load the existing knowledge index."""
-    index_path = Path(knowledge_dir) / "index.json"
-    if not index_path.exists():
-        return {"version": 1, "last_updated": "", "total_facts": 0, "facts": []}
-    
-    try:
-        with open(index_path, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    except (json.JSONDecodeError, IOError) as e:
-        print(f"WARNING: Could not load knowledge index: {e}", file=sys.stderr)
-        return {"version": 1, "last_updated": "", "total_facts": 0, "facts": []}
-
-
-def fact_fingerprint(fact: dict) -> str:
-    """Generate a deduplication fingerprint for a fact.
-    
-    Uses the fact text normalized (lowercase, stripped) as the key.
-    Similar facts will have similar fingerprints.
-    """
-    text = fact.get('fact', '').lower().strip()
-    # Normalize whitespace
-    text = ' '.join(text.split())
-    return hashlib.md5(text.encode('utf-8')).hexdigest()
-
-
-def deduplicate(new_facts: list[dict], existing: list[dict], similarity_threshold: float = 0.8) -> list[dict]:
-    """Remove duplicate facts from new_facts that already exist in the knowledge store.
-    
-    Uses fingerprint matching for exact dedup and simple overlap check for near-dupes.
-    """
-    existing_fingerprints = set()
-    existing_texts = []
-    for f in existing:
-        fp = fact_fingerprint(f)
-        existing_fingerprints.add(fp)
-        existing_texts.append(f.get('fact', '').lower().strip())
-    
-    unique = []
-    for fact in new_facts:
-        fp = fact_fingerprint(fact)
-        if fp in existing_fingerprints:
-            continue
-        
-        # Check for near-duplicates using simple word overlap
-        fact_words = set(fact.get('fact', '').lower().split())
-        is_dup = False
-        for existing_text in existing_texts:
-            existing_words = set(existing_text.split())
-            if not fact_words or not existing_words:
-                continue
-            overlap = len(fact_words & existing_words) / max(len(fact_words | existing_words), 1)
-            if overlap >= similarity_threshold:
-                is_dup = True
-                break
-        
-        if not is_dup:
-            unique.append(fact)
-            existing_fingerprints.add(fp)
-            existing_texts.append(fact.get('fact', '').lower().strip())
-    
-    return unique
-
-
-def validate_fact(fact: dict) -> bool:
-    """Validate a single knowledge item has required fields."""
-    required = ['fact', 'category', 'repo', 'confidence']
-    for field in required:
-        if field not in fact:
-            return False
-    
-    if not isinstance(fact['fact'], str) or not fact['fact'].strip():
-        return False
-    
-    valid_categories = ['fact', 'pitfall', 'pattern', 'tool-quirk', 'question']
-    if fact['category'] not in valid_categories:
-        return False
-    
-    if not isinstance(fact.get('confidence', 0), (int, float)):
-        return False
-    
-    if not (0.0 <= fact['confidence'] <= 1.0):
-        return False
-    
-    return True
-
-
-def write_knowledge(index: dict, new_facts: list[dict], knowledge_dir: str, source_session: str = ""):
-    """Write new facts to the knowledge store."""
-    kdir = Path(knowledge_dir)
-    kdir.mkdir(parents=True, exist_ok=True)
-    
-    # Add source tracking to each fact
-    for fact in new_facts:
-        fact['source_session'] = source_session
-        fact['harvested_at'] = datetime.now(timezone.utc).isoformat()
-    
-    # Update index
-    index['facts'].extend(new_facts)
-    index['total_facts'] = len(index['facts'])
-    index['last_updated'] = datetime.now(timezone.utc).isoformat()
-    
-    # Write index
-    index_path = kdir / "index.json"
-    with open(index_path, 'w', encoding='utf-8') as f:
-        json.dump(index, f, indent=2, ensure_ascii=False)
-    
-    # Also write per-repo markdown files for human reading
-    repos = {}
-    for fact in new_facts:
-        repo = fact.get('repo', 'global')
-        repos.setdefault(repo, []).append(fact)
-    
-    for repo, facts in repos.items():
-        if repo == 'global':
-            md_path = kdir / "global" / "harvested.md"
-        else:
-            md_path = kdir / "repos" / f"{repo}.md"
-        
-        md_path.parent.mkdir(parents=True, exist_ok=True)
-        
-        # Append to existing or create new
-        mode = 'a' if md_path.exists() else 'w'
-        with open(md_path, mode, encoding='utf-8') as f:
-            if mode == 'w':
-                f.write(f"# Knowledge: {repo}\n\n")
-            f.write(f"## Harvested {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M')}\n\n")
-            for fact in facts:
-                icon = {'fact': '📋', 'pitfall': '⚠️', 'pattern': '🔄', 'tool-quirk': '🔧', 'question': '❓'}.get(fact['category'], '•')
-                f.write(f"- {icon} **{fact['category']}** (conf: {fact['confidence']:.1f}): {fact['fact']}\n")
-            f.write("\n")
-
-
-def harvest_session(session_path: str, knowledge_dir: str, api_base: str, api_key: str, 
-                    model: str, dry_run: bool = False, min_confidence: float = 0.3) -> dict:
-    """Harvest knowledge from a single session.
-    
-    Returns: dict with stats (facts_found, facts_new, facts_dup, elapsed_seconds, error)
-    """
-    start_time = time.time()
-    stats = {
-        'session': session_path,
-        'facts_found': 0,
-        'facts_new': 0,
-        'facts_dup': 0,
-        'elapsed_seconds': 0,
-        'error': None
-    }
-    
-    try:
-        # 1. Read session
-        messages = read_session(session_path)
-        if not messages:
-            stats['error'] = "Empty session file"
-            return stats
-        
-        # 2. Extract conversation
-        conv = extract_conversation(messages)
-        if not conv:
-            stats['error'] = "No conversation turns found"
-            return stats
-        
-        # 3. Truncate for context window
-        truncated = truncate_for_context(conv, head=50, tail=50)
-        transcript = messages_to_text(truncated)
-        
-        # 4. Load extraction prompt
-        prompt = load_extraction_prompt()
-        
-        # 5. Call LLM
-        raw_facts = call_llm(prompt, transcript, api_base, api_key, model)
-        if raw_facts is None:
-            stats['error'] = "LLM extraction failed"
-            return stats
-        
-        # 6. Validate
-        valid_facts = [f for f in raw_facts if validate_fact(f) and f.get('confidence', 0) >= min_confidence]
-        stats['facts_found'] = len(valid_facts)
-        
-        # 7. Deduplicate
-        existing_index = load_existing_knowledge(knowledge_dir)
-        existing_facts = existing_index.get('facts', [])
-        new_facts = deduplicate(valid_facts, existing_facts)
-        stats['facts_new'] = len(new_facts)
-        stats['facts_dup'] = len(valid_facts) - len(new_facts)
-        
-        # 8. Write (unless dry run)
-        if new_facts and not dry_run:
-            write_knowledge(existing_index, new_facts, knowledge_dir, source_session=session_path)
-        
-        stats['elapsed_seconds'] = round(time.time() - start_time, 2)
-        return stats
-        
-    except Exception as e:
-        stats['error'] = str(e)
-        stats['elapsed_seconds'] = round(time.time() - start_time, 2)
-        return stats
-
-
-def batch_harvest(sessions_dir: str, knowledge_dir: str, api_base: str, api_key: str,
-                  model: str, since: str = "", limit: int = 0, dry_run: bool = False) -> list[dict]:
-    """Harvest knowledge from multiple sessions in batch."""
-    sessions_path = Path(sessions_dir)
-    if not sessions_path.is_dir():
-        print(f"ERROR: Sessions directory not found: {sessions_dir}", file=sys.stderr)
-        return []
-    
-    # Find session files
-    session_files = sorted(sessions_path.glob("*.jsonl"), reverse=True)  # Newest first
-    
-    # Filter by date if --since provided
-    if since:
-        since_dt = datetime.fromisoformat(since.replace('Z', '+00:00'))
-        filtered = []
-        for sf in session_files:
-            # Try to parse timestamp from filename (common format: session_YYYYMMDD_HHMMSS_hash.jsonl)
-            try:
-                parts = sf.stem.split('_')
-                if len(parts) >= 3:
-                    date_str = parts[1]
-                    file_dt = datetime.strptime(date_str, '%Y%m%d').replace(tzinfo=timezone.utc)
-                    if file_dt >= since_dt:
-                        filtered.append(sf)
-            except (ValueError, IndexError):
-                # If we can't parse the date, include the file (be permissive)
-                filtered.append(sf)
-        session_files = filtered
-    
-    # Apply limit
-    if limit > 0:
-        session_files = session_files[:limit]
-    
-    print(f"Harvesting {len(session_files)} sessions...")
-    
-    results = []
-    for i, sf in enumerate(session_files, 1):
-        print(f"[{i}/{len(session_files)}] {sf.name}...", end=" ", flush=True)
-        stats = harvest_session(str(sf), knowledge_dir, api_base, api_key, model, dry_run)
-        if stats['error']:
-            print(f"ERROR: {stats['error']}")
-        else:
-            print(f"{stats['facts_new']} new, {stats['facts_dup']} dup ({stats['elapsed_seconds']}s)")
-        results.append(stats)
-    
-    return results
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Harvest knowledge from session transcripts")
-    parser.add_argument('--session', help='Path to a single session JSONL file')
-    parser.add_argument('--batch', action='store_true', help='Batch mode: process multiple sessions')
-    parser.add_argument('--sessions-dir', default=os.path.expanduser('~/.hermes/sessions'), 
-                        help='Directory containing session files (default: ~/.hermes/sessions)')
-    parser.add_argument('--output', default='knowledge', help='Output directory for knowledge store')
-    parser.add_argument('--since', default='', help='Only process sessions after this date (YYYY-MM-DD)')
-    parser.add_argument('--limit', type=int, default=0, help='Max sessions to process (0=unlimited)')
-    parser.add_argument('--api-base', default=DEFAULT_API_BASE, help='LLM API base URL')
-    parser.add_argument('--api-key', default='', help='LLM API key (or set HARVESTER_API_KEY)')
-    parser.add_argument('--model', default=DEFAULT_MODEL, help='Model to use for extraction')
-    parser.add_argument('--dry-run', action='store_true', help='Preview without writing to knowledge store')
-    parser.add_argument('--min-confidence', type=float, default=0.3, help='Minimum confidence threshold')
-    
-    args = parser.parse_args()
-    
-    # Resolve API key
-    api_key = args.api_key or DEFAULT_API_KEY or find_api_key()
-    if not api_key:
-        print("ERROR: No API key found. Set HARVESTER_API_KEY or store in one of:", file=sys.stderr)
-        for p in API_KEY_PATHS:
-            print(f"  {p}", file=sys.stderr)
-        sys.exit(1)
-    
-    # Resolve knowledge directory
-    knowledge_dir = args.output
-    if not os.path.isabs(knowledge_dir):
-        knowledge_dir = os.path.join(SCRIPT_DIR.parent, knowledge_dir)
-    
-    if args.session:
-        # Single session mode
-        stats = harvest_session(
-            args.session, knowledge_dir, args.api_base, api_key, args.model,
-            dry_run=args.dry_run, min_confidence=args.min_confidence
-        )
-        print(json.dumps(stats, indent=2))
-        if stats['error']:
-            sys.exit(1)
-    elif args.batch:
-        # Batch mode
-        results = batch_harvest(
-            args.sessions_dir, knowledge_dir, args.api_base, api_key, args.model,
-            since=args.since, limit=args.limit, dry_run=args.dry_run
-        )
-        total_new = sum(r['facts_new'] for r in results)
-        total_dup = sum(r['facts_dup'] for r in results)
-        errors = sum(1 for r in results if r['error'])
-        print(f"\nDone: {total_new} new facts, {total_dup} duplicates, {errors} errors")
-    else:
-        parser.print_help()
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    main()

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/session_reader.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-"""
-session_reader.py — Parse Hermes session JSONL transcripts.
-
-Each line in a session file is a JSON object representing a message.
-Standard fields: role (user|assistant|system), content (str), timestamp (str).
-Tool calls and tool results are also captured.
-"""
-
-import json
-import sys
-from pathlib import Path
-from typing import Iterator, Optional
-
-
-def read_session(path: str) -> list[dict]:
-    """Read a session JSONL file and return all messages as a list."""
-    messages = []
-    with open(path, 'r', encoding='utf-8') as f:
-        for line_num, line in enumerate(f, 1):
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                msg = json.loads(line)
-                messages.append(msg)
-            except json.JSONDecodeError as e:
-                print(f"WARNING: Skipping malformed JSON at line {line_num}: {e}", file=sys.stderr)
-    return messages
-
-
-def read_session_iter(path: str) -> Iterator[dict]:
-    """Iterate over session messages without loading all into memory."""
-    with open(path, 'r', encoding='utf-8') as f:
-        for line_num, line in enumerate(f, 1):
-            line = line.strip()
-            if not line:
-                continue
-            try:
-                yield json.loads(line)
-            except json.JSONDecodeError as e:
-                print(f"WARNING: Skipping malformed JSON at line {line_num}: {e}", file=sys.stderr)
-
-
-def extract_conversation(messages: list[dict]) -> list[dict]:
-    """Extract user/assistant conversation turns, skipping tool-only messages."""
-    conversation = []
-    for msg in messages:
-        role = msg.get('role', '')
-        content = msg.get('content', '')
-        
-        # Skip empty messages and pure tool calls
-        if role in ('user', 'assistant', 'system'):
-            if isinstance(content, str) and content.strip():
-                conversation.append({
-                    'role': role,
-                    'content': content.strip(),
-                    'timestamp': msg.get('timestamp', '')
-                })
-            elif isinstance(content, list):
-                # Multimodal content — extract text parts
-                text_parts = []
-                for part in content:
-                    if isinstance(part, dict) and part.get('type') == 'text':
-                        text_parts.append(part.get('text', ''))
-                if text_parts:
-                    conversation.append({
-                        'role': role,
-                        'content': '\n'.join(text_parts),
-                        'timestamp': msg.get('timestamp', '')
-                    })
-    return conversation
-
-
-def truncate_for_context(messages: list[dict], head: int = 50, tail: int = 50) -> list[dict]:
-    """Truncate long sessions: keep first N + last N messages.
-    
-    This preserves session start (initial context) and end (final results),
-    skipping the messy middle of long debugging sessions.
-    """
-    if len(messages) <= head + tail:
-        return messages
-    
-    truncated = messages[:head]
-    truncated.append({
-        'role': 'system',
-        'content': f'[{len(messages) - head - tail} messages truncated]',
-        'timestamp': ''
-    })
-    truncated.extend(messages[-tail:])
-    return truncated
-
-
-def messages_to_text(messages: list[dict]) -> str:
-    """Convert message list to plain text for LLM consumption."""
-    lines = []
-    for msg in messages:
-        role = msg.get('role', 'unknown').upper()
-        content = msg.get('content', '')
-        if msg.get('role') == 'system' and 'truncated' in content:
-            lines.append(f'--- {content} ---')
-        else:
-            lines.append(f'{role}: {content}')
-    return '\n\n'.join(lines)
-
-
-def get_session_metadata(path: str) -> dict:
-    """Extract metadata from a session file (first message often has config info)."""
-    messages = read_session(path)
-    if not messages:
-        return {'path': path, 'message_count': 0}
-    
-    first = messages[0]
-    last = messages[-1]
-    
-    return {
-        'path': path,
-        'message_count': len(messages),
-        'first_timestamp': first.get('timestamp', ''),
-        'last_timestamp': last.get('timestamp', ''),
-        'first_role': first.get('role', ''),
-        'has_tool_calls': any(m.get('tool_calls') for m in messages),
-    }
-
-
-if __name__ == '__main__':
-    if len(sys.argv) < 2:
-        print(f"Usage: {sys.argv[0]} <session.jsonl>")
-        sys.exit(1)
-    
-    path = sys.argv[1]
-    meta = get_session_metadata(path)
-    print(json.dumps(meta, indent=2))
-    
-    messages = read_session(path)
-    conv = extract_conversation(messages)
-    print(f"\nConversation: {len(conv)} turns")
-    
-    truncated = truncate_for_context(conv)
-    print(f"After truncation: {len(truncated)} turns")
-    print(f"\nPreview (first 500 chars):")
-    print(messages_to_text(truncated[:5])[:500])

scripts/test_automation_opportunity_finder.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+"""Tests for scripts/automation_opportunity_finder.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(
+    "aof",
+    os.path.join(os.path.dirname(__file__) or ".", "automation_opportunity_finder.py"),
+)
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+
+
+def test_analyze_cron_jobs_no_file():
+    """Returns empty list when no cron jobs file exists."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        result = mod.analyze_cron_jobs(tmpdir)
+        assert result == []
+        print("PASS: test_analyze_cron_jobs_no_file")
+
+
+def test_analyze_cron_jobs_disabled():
+    """Detects disabled cron jobs."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cron_dir = os.path.join(tmpdir, "cron")
+        os.makedirs(cron_dir)
+        jobs = [
+            {"id": "j1", "name": "backup", "enabled": False, "schedule": "0 * * * *"},
+            {"id": "j2", "name": "health", "enabled": True, "schedule": "*/5 * * * *"},
+        ]
+        with open(os.path.join(cron_dir, "jobs.json"), "w") as f:
+            json.dump(jobs, f)
+        result = mod.analyze_cron_jobs(tmpdir)
+        assert any(p["category"] == "cron_disabled" for p in result)
+        print("PASS: test_analyze_cron_jobs_disabled")
+
+
+def test_analyze_cron_jobs_errors():
+    """Detects cron jobs with error status."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cron_dir = os.path.join(tmpdir, "cron")
+        os.makedirs(cron_dir)
+        jobs = [
+            {"id": "j1", "name": "broken", "enabled": True, "last_status": "error", "schedule": "0 * * * *"},
+        ]
+        with open(os.path.join(cron_dir, "jobs.json"), "w") as f:
+            json.dump(jobs, f)
+        result = mod.analyze_cron_jobs(tmpdir)
+        assert any(p["category"] == "cron_errors" for p in result)
+        print("PASS: test_analyze_cron_jobs_errors")
+
+
+def test_analyze_documents_finds_todos():
+    """Detects TODO markers in documents."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        docs_dir = os.path.join(tmpdir, "docs")
+        os.makedirs(docs_dir)
+        for i in range(3):
+            with open(os.path.join(docs_dir, f"guide{i}.md"), "w") as f:
+                f.write(f"# Guide {i}\n\nTODO: Automate this step\n")
+        result = mod.analyze_documents([tmpdir])
+        assert any(p["category"] == "manual_todo" for p in result)
+        todo_proposals = [p for p in result if p["category"] == "manual_todo"]
+        assert todo_proposals[0]["details"].__len__() == 3
+        print("PASS: test_analyze_documents_finds_todos")
+
+
+def test_analyze_scripts_repeated_commands():
+    """Detects repeated shell commands across scripts."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        scripts_dir = os.path.join(tmpdir, "scripts")
+        os.makedirs(scripts_dir)
+        repeated_cmd = "docker restart myapp"
+        for i in range(4):
+            with open(os.path.join(scripts_dir, f"deploy{i}.sh"), "w") as f:
+                f.write(f"#!/bin/bash\n{repeated_cmd}\n")
+        result = mod.analyze_scripts([tmpdir])
+        assert any(p["category"] == "repeated_command" for p in result)
+        print("PASS: test_analyze_scripts_repeated_commands")
+
+
+def test_analyze_session_transcripts():
+    """Detects repeated tool-call sequences."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        sessions_dir = os.path.join(tmpdir, "sessions")
+        os.makedirs(sessions_dir)
+        for i in range(4):
+            with open(os.path.join(sessions_dir, f"session{i}.jsonl"), "w") as f:
+                f.write(json.dumps({"role": "user", "content": f"task {i}"}) + "\n")
+                f.write(json.dumps({
+                    "role": "assistant",
+                    "content": "working",
+                    "tool_calls": [
+                        {"function": {"name": "read_file"}},
+                        {"function": {"name": "write_file"}},
+                    ]
+                }) + "\n")
+        result = mod.analyze_session_transcripts([sessions_dir])
+        assert any(p["category"] == "tool_sequence" for p in result)
+        seq_proposals = [p for p in result if p["category"] == "tool_sequence"]
+        assert any("read_file" in p["title"] and "write_file" in p["title"] for p in seq_proposals)
+        print("PASS: test_analyze_session_transcripts")
+
+
+def test_deduplicate_proposals():
+    """Deduplicates proposals with similar titles."""
+    proposals = [
+        {"title": "TODO found 3 times", "category": "manual_todo", "confidence": 0.7, "impact": "medium", "description": "x", "sources": []},
+        {"title": "TODO found 3 times", "category": "manual_todo", "confidence": 0.7, "impact": "medium", "description": "x", "sources": []},
+        {"title": "FIXME found 5 times", "category": "manual_fixme", "confidence": 0.8, "impact": "medium", "description": "y", "sources": []},
+    ]
+    result = mod.deduplicate_proposals(proposals)
+    assert len(result) == 2
+    print("PASS: test_deduplicate_proposals")
+
+
+def test_rank_proposals():
+    """Ranks proposals by impact * confidence."""
+    proposals = [
+        {"title": "low", "category": "x", "confidence": 0.9, "impact": "low", "description": "", "sources": []},
+        {"title": "high", "category": "x", "confidence": 0.8, "impact": "high", "description": "", "sources": []},
+        {"title": "med", "category": "x", "confidence": 0.7, "impact": "medium", "description": "", "sources": []},
+    ]
+    result = mod.rank_proposals(proposals)
+    assert result[0]["title"] == "high"
+    assert result[-1]["title"] == "low"
+    print("PASS: test_rank_proposals")
+
+
+if __name__ == "__main__":
+    tests = [v for k, v in globals().items() if k.startswith("test_")]
+    passed = 0
+    failed = 0
+    for t in tests:
+        try:
+            t()
+            passed += 1
+        except Exception as e:
+            print(f"FAIL: {t.__name__}: {e}")
+            failed += 1
+    print(f"\n{passed}/{passed+failed} tests passed")
+    sys.exit(1 if failed else 0)

scripts/test_harvest_prompt_comprehensive.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""
+Comprehensive test script for knowledge extraction prompt.
+Validates prompt structure, requirements, and consistency.
+"""
+
+import json
+import re
+from pathlib import Path
+
+def test_prompt_structure():
+    """Test that the prompt has the required structure."""
+    prompt_path = Path("templates/harvest-prompt.md")
+    if not prompt_path.exists():
+        return False, "harvest-prompt.md not found"
+    
+    content = prompt_path.read_text()
+    
+    # Check for required sections
+    required_sections = [
+        "System Prompt",
+        "Instructions", 
+        "Categories",
+        "Output Format",
+        "Confidence Scoring",
+        "Constraints",
+        "Example"
+    ]
+    
+    for section in required_sections:
+        if section.lower() not in content.lower():
+            return False, f"Missing required section: {section}"
+    
+    # Check for required categories
+    required_categories = ["fact", "pitfall", "pattern", "tool-quirk", "question"]
+    for category in required_categories:
+        if category not in content:
+            return False, f"Missing required category: {category}"
+    
+    # Check for required output fields
+    required_fields = ["fact", "category", "repo", "confidence"]
+    for field in required_fields:
+        if field not in content:
+            return False, f"Missing required output field: {field}"
+    
+    # Check prompt size (should be ~1k tokens, roughly 4k chars)
+    if len(content) > 5000:
+        return False, f"Prompt too large: {len(content)} chars (max ~5000)"
+    
+    if len(content) < 1000:
+        return False, f"Prompt too small: {len(content)} chars (min ~1000)"
+    
+    return True, "Prompt structure is valid"
+
+def test_confidence_scoring():
+    """Test that confidence scoring is properly defined."""
+    prompt_path = Path("templates/harvest-prompt.md")
+    content = prompt_path.read_text()
+    
+    # Check for confidence scale definitions
+    confidence_levels = [
+        ("0.9-1.0", "explicitly stated"),
+        ("0.7-0.8", "clearly implied"),
+        ("0.5-0.6", "suggested"),
+        ("0.3-0.4", "inferred"),
+        ("0.1-0.2", "speculative")
+    ]
+    
+    for level, description in confidence_levels:
+        if level not in content:
+            return False, f"Missing confidence level: {level}"
+        if description.lower() not in content.lower():
+            return False, f"Missing confidence description: {description}"
+    
+    return True, "Confidence scoring is properly defined"
+
+def test_example_quality():
+    """Test that examples are clear and complete."""
+    prompt_path = Path("templates/harvest-prompt.md")
+    content = prompt_path.read_text()
+    
+    # Check for example input/output
+    if "example" not in content.lower():
+        return False, "No examples provided"
+    
+    # Check that example includes all categories
+    example_section = content[content.lower().find("example"):]
+    
+    # Look for JSON example
+    json_match = re.search(r'\{[\s\S]*"knowledge"[\s\S]*\}', example_section)
+    if not json_match:
+        return False, "No JSON example found"
+    
+    example_json = json_match.group(0)
+    
+    # Check for all categories in example
+    for category in ["fact", "pitfall", "pattern", "tool-quirk", "question"]:
+        if category not in example_json:
+            return False, f"Example missing category: {category}"
+    
+    return True, "Examples are clear and complete"
+
+def test_constraint_coverage():
+    """Test that constraints cover all requirements."""
+    prompt_path = Path("templates/harvest-prompt.md")
+    content = prompt_path.read_text()
+    
+    required_constraints = [
+        "No hallucination",
+        "only extract",
+        "explicitly",
+        "partial",
+        "failed sessions",
+        "1k tokens"
+    ]
+    
+    for constraint in required_constraints:
+        if constraint.lower() not in content.lower():
+            return False, f"Missing constraint: {constraint}"
+    
+    return True, "Constraints cover all requirements"
+
+def test_test_sessions():
+    """Test that test sessions exist and are valid."""
+    test_sessions_dir = Path("test_sessions")
+    if not test_sessions_dir.exists():
+        return False, "test_sessions directory not found"
+    
+    session_files = list(test_sessions_dir.glob("*.jsonl"))
+    if len(session_files) < 5:
+        return False, f"Only {len(session_files)} test sessions found, need 5"
+    
+    # Check each session file
+    for session_file in session_files:
+        content = session_file.read_text()
+        lines = content.strip().split("\n")
+        
+        # Check that each line is valid JSON
+        for i, line in enumerate(lines, 1):
+            try:
+                json.loads(line)
+            except json.JSONDecodeError as e:
+                return False, f"Invalid JSON in {session_file.name}, line {i}: {e}"
+    
+    return True, f"Found {len(session_files)} valid test sessions"
+
+def run_all_tests():
+    """Run all tests and return results."""
+    tests = [
+        ("Prompt Structure", test_prompt_structure),
+        ("Confidence Scoring", test_confidence_scoring),
+        ("Example Quality", test_example_quality),
+        ("Constraint Coverage", test_constraint_coverage),
+        ("Test Sessions", test_test_sessions)
+    ]
+    
+    results = []
+    all_passed = True
+    
+    for test_name, test_func in tests:
+        try:
+            passed, message = test_func()
+            results.append({
+                "test": test_name,
+                "passed": passed,
+                "message": message
+            })
+            if not passed:
+                all_passed = False
+        except Exception as e:
+            results.append({
+                "test": test_name,
+                "passed": False,
+                "message": f"Error: {str(e)}"
+            })
+            all_passed = False
+    
+    # Print results
+    print("=" * 60)
+    print("HARVEST PROMPT TEST RESULTS")
+    print("=" * 60)
+    
+    for result in results:
+        status = "✓ PASS" if result["passed"] else "✗ FAIL"
+        print(f"{status}: {result['test']}")
+        print(f"  {result['message']}")
+        print()
+    
+    print("=" * 60)
+    if all_passed:
+        print("ALL TESTS PASSED!")
+    else:
+        print("SOME TESTS FAILED!")
+    print("=" * 60)
+    
+    return all_passed, results
+
+if __name__ == "__main__":
+    all_passed, results = run_all_tests()
+    
+    # Save results to file
+    with open("test_results.json", "w") as f:
+        json.dump({
+            "all_passed": all_passed,
+            "results": results,
+            "timestamp": "2026-04-14T19:05:00Z"
+        }, f, indent=2)
+    
+    print(f"Results saved to test_results.json")
+    
+    # Exit with appropriate code
+    exit(0 if all_passed else 1)

scripts/test_harvester_pipeline.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-"""
-Smoke test for harvester pipeline — verifies the full chain:
-session_reader -> prompt -> LLM (mocked) -> validate -> deduplicate -> store
-
-Does NOT call the real LLM. Tests plumbing only.
-"""
-
-import json
-import sys
-import tempfile
-import os
-from pathlib import Path
-
-# Setup path
-SCRIPT_DIR = Path(__file__).parent.absolute()
-sys.path.insert(0, str(SCRIPT_DIR))
-
-from session_reader import read_session, extract_conversation, truncate_for_context, messages_to_text
-from harvester import validate_fact, deduplicate, load_existing_knowledge, fact_fingerprint
-
-
-def test_session_reader():
-    """Test that session_reader parses JSONL correctly."""
-    with tempfile.NamedTemporaryFile(mode='w', suffix='.jsonl', delete=False) as f:
-        f.write('{"role": "user", "content": "Hello", "timestamp": "2026-04-13T10:00:00Z"}\n')
-        f.write('{"role": "assistant", "content": "Hi there", "timestamp": "2026-04-13T10:00:01Z"}\n')
-        f.write('{"role": "user", "content": "Clone the repo", "timestamp": "2026-04-13T10:00:02Z"}\n')
-        f.write('{"role": "assistant", "content": "Cloned successfully", "timestamp": "2026-04-13T10:00:05Z"}\n')
-        path = f.name
-
-    messages = read_session(path)
-    assert len(messages) == 4, f"Expected 4 messages, got {len(messages)}"
-
-    conv = extract_conversation(messages)
-    assert len(conv) == 4, f"Expected 4 conversation turns, got {len(conv)}"
-
-    text = messages_to_text(conv)
-    assert "USER: Hello" in text
-    assert "ASSISTANT: Hi there" in text
-
-    truncated = truncate_for_context(conv, head=2, tail=2)
-    assert len(truncated) == 4  # 4 <= head+tail, so no truncation
-
-    os.unlink(path)
-    print("  [PASS] session_reader pipeline works")
-
-
-def test_validate_fact():
-    """Test fact validation."""
-    good = {"fact": "Gitea token is at ~/.config/gitea/token", "category": "tool-quirk", "repo": "global", "confidence": 0.9}
-    assert validate_fact(good), "Valid fact should pass"
-
-    bad_missing = {"fact": "Something", "category": "fact"}
-    assert not validate_fact(bad_missing), "Missing fields should fail"
-
-    bad_category = {"fact": "Something", "category": "nonsense", "repo": "x", "confidence": 0.5}
-    assert not validate_fact(bad_category), "Bad category should fail"
-
-    bad_conf = {"fact": "Something", "category": "fact", "repo": "x", "confidence": 1.5}
-    assert not validate_fact(bad_conf), "Confidence > 1.0 should fail"
-
-    print("  [PASS] fact validation works")
-
-
-def test_deduplicate():
-    """Test deduplication."""
-    existing = [
-        {"fact": "Token is at ~/.config/gitea/token", "category": "tool-quirk", "repo": "global", "confidence": 0.9}
-    ]
-    new = [
-        {"fact": "Token is at ~/.config/gitea/token", "category": "tool-quirk", "repo": "global", "confidence": 0.9},  # exact dup
-        {"fact": "Deploy uses Ansible on port 22", "category": "pattern", "repo": "fleet", "confidence": 0.8},  # unique
-    ]
-    result = deduplicate(new, existing)
-    assert len(result) == 1, f"Expected 1 unique, got {len(result)}"
-    assert result[0]["fact"] == "Deploy uses Ansible on port 22"
-    print("  [PASS] deduplication works")
-
-
-def test_knowledge_store_roundtrip():
-    """Test loading and writing knowledge index."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        # Load empty index
-        index = load_existing_knowledge(tmpdir)
-        assert index["total_facts"] == 0
-
-        # Write a fact
-        new_facts = [{"fact": "Test fact", "category": "fact", "repo": "test", "confidence": 0.9}]
-
-        # Use harvester's write function
-        from harvester import write_knowledge
-        write_knowledge(index, new_facts, tmpdir, source_session="test.jsonl")
-
-        # Reload and verify
-        index2 = load_existing_knowledge(tmpdir)
-        assert index2["total_facts"] == 1
-        assert index2["facts"][0]["fact"] == "Test fact"
-        assert index2["facts"][0]["source_session"] == "test.jsonl"
-
-        # Check markdown was written
-        md_path = Path(tmpdir) / "repos" / "test.md"
-        assert md_path.exists(), "Markdown file should be created"
-
-    print("  [PASS] knowledge store roundtrip works")
-
-
-def test_full_chain_no_llm():
-    """Test the full pipeline minus the LLM call."""
-    with tempfile.NamedTemporaryFile(mode='w', suffix='.jsonl', delete=False) as f:
-        f.write('{"role": "user", "content": "Clone compounding-intelligence", "timestamp": "2026-04-13T10:00:00Z"}\n')
-        f.write('{"role": "assistant", "content": "Cloned successfully", "timestamp": "2026-04-13T10:00:05Z"}\n')
-        session_path = f.name
-
-    with tempfile.TemporaryDirectory() as knowledge_dir:
-        # Step 1: Read
-        messages = read_session(session_path)
-        assert len(messages) == 2
-
-        # Step 2: Extract conversation
-        conv = extract_conversation(messages)
-        assert len(conv) == 2
-
-        # Step 3: Truncate
-        truncated = truncate_for_context(conv, head=50, tail=50)
-
-        # Step 4: Convert to text (this goes to the LLM)
-        transcript = messages_to_text(truncated)
-        assert "Clone compounding-intelligence" in transcript
-
-        # Step 5-7: Would be LLM call, validate, deduplicate
-        # We simulate LLM output here
-        mock_facts = [
-            {"fact": "compounding-intelligence repo was cloned", "category": "fact", "repo": "compounding-intelligence", "confidence": 0.9}
-        ]
-        valid = [f for f in mock_facts if validate_fact(f)]
-
-        # Step 6: Deduplicate
-        index = load_existing_knowledge(knowledge_dir)
-        new_facts = deduplicate(valid, index.get("facts", []))
-        assert len(new_facts) == 1
-
-        # Step 7: Store
-        from harvester import write_knowledge
-        write_knowledge(index, new_facts, knowledge_dir, source_session=session_path)
-
-        # Verify
-        index2 = load_existing_knowledge(knowledge_dir)
-        assert index2["total_facts"] == 1
-
-    os.unlink(session_path)
-    print("  [PASS] full chain (reader -> validate -> dedup -> store) works")
-
-
-if __name__ == "__main__":
-    print("Running harvester pipeline smoke tests...")
-    test_session_reader()
-    test_validate_fact()
-    test_deduplicate()
-    test_knowledge_store_roundtrip()
-    test_full_chain_no_llm()
-    print("\nAll tests passed.")

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()

test_sessions/session_failure.jsonl

@@ -0,0 +1,18 @@
+{"role": "user", "content": "Deploy the application to production"}
+{"role": "assistant", "content": "I'll deploy the application to production.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh deploy@prod-server 'cd /app && git pull && docker-compose up -d'"}}]}
+{"role": "tool", "content": "ssh: connect to host prod-server port 22: Connection refused"}
+{"role": "assistant", "content": "Connection refused. Let me check the server status.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ping -c 3 prod-server"}}]}
+{"role": "tool", "content": "PING prod-server (192.168.1.100): 56 data bytes\nRequest timeout for icmp_seq 0\nRequest timeout for icmp_seq 1\nRequest timeout for icmp_seq 2\n--- prod-server ping statistics ---\n3 packets transmitted, 0 packets received, 100.0% packet loss"}
+{"role": "assistant", "content": "The server appears to be down. Let me check if there's an alternative deployment method."}
+{"role": "user", "content": "Try using the VPS instead"}
+{"role": "assistant", "content": "I'll try deploying to the VPS.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh deploy@vps-server 'cd /app && git pull && docker-compose up -d'"}}]}
+{"role": "tool", "content": "Permission denied (publickey)."}
+{"role": "assistant", "content": "Permission denied. The SSH key might not be set up correctly. Let me check the SSH configuration.", "tool_calls": [{"function": "terminal", "arguments": {"command": "cat ~/.ssh/config"}}]}
+{"role": "tool", "content": "Host vps-server\n    HostName 167.99.126.228\n    User deploy\n    IdentityFile ~/.ssh/id_rsa_vps"}
+{"role": "assistant", "content": "The SSH config looks correct. Let me check if the key exists.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la ~/.ssh/id_rsa_vps"}}]}
+{"role": "tool", "content": "ls: /Users/user/.ssh/id_rsa_vps: No such file or directory"}
+{"role": "assistant", "content": "The SSH key doesn't exist. This is a pitfall - we need to generate the SSH key first."}
+{"role": "user", "content": "Generate a new SSH key for the VPS"}
+{"role": "assistant", "content": "I'll generate a new SSH key.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ssh-keygen -t rsa -b 4096 -f ~/.ssh/id_rsa_vps -N ''"}}]}
+{"role": "tool", "content": "Generating public/private rsa key pair.\nYour identification has been saved in /Users/user/.ssh/id_rsa_vps\nYour public key has been saved in /Users/user/.ssh/id_rsa_vps.pub\nThe key fingerprint is:\nSHA256:abc123... user@machine"}
+{"role": "assistant", "content": "SSH key generated. Now I need to add it to the VPS."}

test_sessions/session_partial.jsonl

@@ -0,0 +1,14 @@
+{"role": "user", "content": "Check the Gitea API for open issues"}
+{"role": "assistant", "content": "I'll check the Gitea API for open issues.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -H 'Authorization: token $GITEA_TOKEN' https://forge.alexanderwhitestone.com/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
+{"role": "tool", "content": "curl: (6) Could not resolve host: forge.alexanderwhitestone.com"}
+{"role": "assistant", "content": "DNS resolution failed. Let me check the network connectivity.", "tool_calls": [{"function": "terminal", "arguments": {"command": "nslookup forge.alexanderwhitestone.com"}}]}
+{"role": "tool", "content": "Server:		8.8.8.8\nAddress:	8.8.8.8#53\n\n** server can't find forge.alexanderwhitestone.com: NXDOMAIN"}
+{"role": "assistant", "content": "The domain doesn't resolve. This might be a DNS issue. Let me try using the IP address directly.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
+{"role": "tool", "content": "curl: (60) SSL certificate problem: self signed certificate"}
+{"role": "assistant", "content": "SSL certificate issue. The server is using a self-signed certificate. Let me try with -k flag to skip SSL verification.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -k -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/issues?state=open"}}]}
+{"role": "tool", "content": "[]"}
+{"role": "assistant", "content": "No open issues found. The API returned an empty array."}
+{"role": "user", "content": "Check if there are any pull requests"}
+{"role": "assistant", "content": "Let me check for pull requests.", "tool_calls": [{"function": "terminal", "arguments": {"command": "curl -k -H 'Authorization: token $GITEA_TOKEN' https://167.99.126.228/api/v1/repos/Timmy_Foundation/compounding-intelligence/pulls?state=open"}}]}
+{"role": "tool", "content": "[{"id": 20, "title": "feat: add harvester.py + session_reader.py — session knowledge extractor (closes #8)", "state": "open", "user": {"login": "Rockachopa"}, "created_at": "2026-04-14T17:28:20Z"}]"}
+{"role": "assistant", "content": "Found 1 open pull request: PR #20 - "feat: add harvester.py + session_reader.py — session knowledge extractor (closes #8)" by Rockachopa."}

test_sessions/session_patterns.jsonl

@@ -0,0 +1,10 @@
+{"role": "user", "content": "How do I deploy a new version of the application?"}
+{"role": "assistant", "content": "Here's the deployment process:\n1. Run tests: pytest\n2. Build Docker image: docker build -t app:latest .\n3. Push to registry: docker push registry.example.com/app:latest\n4. Update production: ssh prod 'cd /app && docker-compose pull && docker-compose up -d'\n5. Verify: curl https://app.example.com/health"}
+{"role": "user", "content": "What if tests fail?"}
+{"role": "assistant", "content": "If tests fail:\n1. Check the error logs: pytest -v\n2. Fix the failing tests\n3. Re-run tests: pytest\n4. If still failing, check recent changes: git log --oneline -10\n5. Consider reverting: git revert HEAD"}
+{"role": "user", "content": "What if the Docker build fails?"}
+{"role": "assistant", "content": "If Docker build fails:\n1. Check Dockerfile syntax: docker build --no-cache -t app:latest .\n2. Check for missing dependencies: cat requirements.txt\n3. Check disk space: df -h\n4. Clean up Docker: docker system prune -f\n5. Try building with verbose output: docker build --progress=plain -t app:latest ."}
+{"role": "user", "content": "What if the push to registry fails?"}
+{"role": "assistant", "content": "If push to registry fails:\n1. Check registry credentials: docker login registry.example.com\n2. Check registry space: curl https://registry.example.com/v2/_catalog\n3. Check image size: docker images app:latest\n4. Try tagging with version: docker tag app:latest registry.example.com/app:v1.0.0\n5. Push with version tag: docker push registry.example.com/app:v1.0.0"}
+{"role": "user", "content": "What if the production update fails?"}
+{"role": "assistant", "content": "If production update fails:\n1. Check SSH connection: ssh prod 'echo connected'\n2. Check disk space on prod: ssh prod 'df -h'\n3. Check Docker status: ssh prod 'docker ps -a'\n4. Check logs: ssh prod 'docker-compose logs --tail=100'\n5. Rollback: ssh prod 'cd /app && git checkout HEAD~1 && docker-compose up -d'"}

test_sessions/session_questions.jsonl

@@ -0,0 +1,12 @@
+{"role": "user", "content": "I need to implement a new feature for the application"}
+{"role": "assistant", "content": "I can help you implement a new feature. What feature would you like to add?"}
+{"role": "user", "content": "I want to add user authentication"}
+{"role": "assistant", "content": "User authentication is a good feature. I have a few questions:\n1. What authentication method do you prefer? (JWT, OAuth, Session-based)\n2. Do you need social login? (Google, GitHub, etc.)\n3. What user data do you need to store?\n4. Do you need role-based access control?\n5. What's your database? (PostgreSQL, MySQL, SQLite)"}
+{"role": "user", "content": "I prefer JWT, no social login, just email and password, no roles, PostgreSQL"}
+{"role": "assistant", "content": "Great choices. Here's the implementation plan:\n1. Create user model with email and password hash\n2. Create JWT token generation and validation\n3. Create login endpoint: POST /api/auth/login\n4. Create register endpoint: POST /api/auth/register\n5. Create middleware for protected routes\n6. Add password hashing with bcrypt\n7. Add rate limiting for login attempts\n8. Add password reset functionality\n9. Add email verification\n10. Test all endpoints"}
+{"role": "user", "content": "What about password reset?"}
+{"role": "assistant", "content": "For password reset:\n1. Create reset token table\n2. Create forgot password endpoint: POST /api/auth/forgot-password\n3. Send reset email with token\n4. Create reset password endpoint: POST /api/auth/reset-password\n5. Validate reset token and expiration\n6. Update password and delete token\n7. Send confirmation email"}
+{"role": "user", "content": "What about email verification?"}
+{"role": "assistant", "content": "For email verification:\n1. Add email_verified field to user model\n2. Create verification token table\n3. Send verification email on registration\n4. Create verify email endpoint: GET /api/auth/verify-email?token=...\n5. Validate token and update email_verified\n6. Add middleware to check email_verified for certain routes\n7. Add resend verification email endpoint"}
+{"role": "user", "content": "What about rate limiting?"}
+{"role": "assistant", "content": "For rate limiting:\n1. Use Redis for rate limit storage\n2. Create rate limit middleware\n3. Set limits: 5 login attempts per minute, 3 password resets per hour\n4. Return 429 Too Many Requests when limit exceeded\n5. Add X-RateLimit headers to responses\n6. Log rate limit violations\n7. Add whitelist for trusted IPs"}

test_sessions/session_success.jsonl

@@ -0,0 +1,13 @@
+{"role": "user", "content": "Clone the repo at forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence"}
+{"role": "assistant", "content": "I'll clone the repository for you.", "tool_calls": [{"function": "terminal", "arguments": {"command": "git clone https://forge.alexanderwhitestone.com/Timmy_Foundation/compounding-intelligence.git"}}]}
+{"role": "tool", "content": "Cloning into 'compounding-intelligence'...\nremote: Enumerating objects: 15, done.\nremote: Counting objects: 100% (15/15), done.\nremote: Compressing objects: 100% (12/12), done.\nremote: Total 15 (delta 2), reused 0 (delta 0), pack-reused 0\nReceiving objects: 100% (15/15), done.\nResolving deltas: 100% (2/2), done."}
+{"role": "assistant", "content": "Repository cloned successfully. Let me check the structure.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la compounding-intelligence/"}}]}
+{"role": "tool", "content": "total 8\ndrwxr-xr-x   5 user  staff  160 Apr 14 17:21 .\ndrwxr-xr-x  12 user  staff  384 Apr 14 17:21 ..\ndrwxr-xr-x  13 user  staff  416 Apr 14 17:21 .git\n-rw-r--r--   1 user  staff  1234 Apr 14 17:21 README.md\ndrwxr-xr-x   3 user  staff   96 Apr 14 17:21 knowledge\n-rw-r--r--   1 user  staff   456 Apr 14 17:21 metrics\ndrwxr-xr-x   4 user  staff  128 Apr 14 17:21 scripts\ndrwxr-xr-x   3 user  staff   96 Apr 14 17:21 templates"}
+{"role": "user", "content": "What files are in the templates directory?"}
+{"role": "assistant", "content": "Let me check the templates directory.", "tool_calls": [{"function": "terminal", "arguments": {"command": "ls -la compounding-intelligence/templates/"}}]}
+{"role": "tool", "content": "total 0\ndrwxr-xr-x  3 user  staff   96 Apr 14 17:21 .\ndrwxr-xr-x  5 user  staff  160 Apr 14 17:21 ..\n-rw-r--r--  1 user  staff    0 Apr 14 17:21 .gitkeep"}
+{"role": "assistant", "content": "The templates directory currently only contains a .gitkeep file. No templates have been added yet."}
+{"role": "user", "content": "Create a harvest-prompt.md template in the templates directory."}
+{"role": "assistant", "content": "I'll create the harvest-prompt.md template for you.", "tool_calls": [{"function": "terminal", "arguments": {"command": "cat > compounding-intelligence/templates/harvest-prompt.md << 'EOF'\n# Knowledge Extraction Prompt\n\nExtract knowledge from session transcripts.\nEOF"}}]}
+{"role": "tool", "content": ""}
+{"role": "assistant", "content": "Template created successfully. The harvest-prompt.md file is now in the templates directory."}