docs: GENOME.md — full codebase analysis #676

GENOME.md

@@ -0,0 +1,239 @@
+# GENOME.md — compounding-intelligence
+
+*Auto-generated codebase genome. See timmy-home#676.*
+
+---
+
+## Project Overview
+
+**What:** A system that turns 1B+ daily agent tokens into durable, compounding fleet intelligence.
+
+**Why:** Every agent session starts at zero. The same mistakes get made repeatedly — the same HTTP 405 is rediscovered as a branch protection issue, the same token path is searched for from scratch. Intelligence evaporates when the session ends.
+
+**How:** Three pipelines form a compounding loop:
+
+```
+SESSION ENDS → HARVESTER → KNOWLEDGE STORE → BOOTSTRAPPER → NEW SESSION STARTS SMARTER
+                              ↓
+                         MEASURER → Prove it's working
+```
+
+**Status:** Early stage. Template and test scaffolding exist. Core pipeline scripts (harvester.py, bootstrapper.py, measurer.py, session_reader.py) are planned but not yet implemented. The knowledge extraction prompt is complete and validated.
+
+---
+
+## Architecture
+
+```mermaid
+graph TD
+    A[Session Transcript<br/>.jsonl] --> B[Harvester]
+    B --> C{Extract Knowledge}
+    C --> D[knowledge/index.json]
+    C --> E[knowledge/global/*.md]
+    C --> F[knowledge/repos/{repo}.md]
+    C --> G[knowledge/agents/{agent}.md]
+    D --> H[Bootstrapper]
+    H --> I[Bootstrap Context<br/>2k token injection]
+    I --> J[New Session<br/>starts smarter]
+    J --> A
+    D --> K[Measurer]
+    K --> L[metrics/dashboard.md]
+    K --> M[Velocity / Hit Rate<br/>Error Reduction]
+```
+
+### Pipeline 1: Harvester
+
+**Status:** Prompt designed. Script not implemented.
+
+Reads finished session transcripts (JSONL). Uses `templates/harvest-prompt.md` to extract durable knowledge into five categories:
+
+| Category | Description | Example |
+|----------|-------------|---------|
+| `fact` | Concrete, verifiable information | "Repository X has 5 files" |
+| `pitfall` | Errors encountered, wrong assumptions | "Token is at ~/.config/gitea/token, not env var" |
+| `pattern` | Successful action sequences | "Deploy: test → build → push → webhook" |
+| `tool-quirk` | Environment-specific behaviors | "URL format requires trailing slash" |
+| `question` | Identified but unanswered | "Need optimal batch size for harvesting" |
+
+Output schema per knowledge item:
+```json
+{
+  "fact": "One sentence description",
+  "category": "fact|pitfall|pattern|tool-quirk|question",
+  "repo": "repo-name or 'global'",
+  "confidence": 0.0-1.0
+}
+```
+
+### Pipeline 2: Bootstrapper
+
+**Status:** Not implemented.
+
+Queries knowledge store before session start. Assembles a compact 2k-token context from relevant facts. Injects into session startup so the agent begins with full situational awareness.
+
+### Pipeline 3: Measurer
+
+**Status:** Not implemented.
+
+Tracks compounding metrics: knowledge velocity (facts/day), error reduction (%), hit rate (knowledge used / knowledge available), task completion improvement.
+
+---
+
+## Directory Structure
+
+```
+compounding-intelligence/
+├── README.md                           # Project overview and architecture
+├── GENOME.md                           # This file (codebase genome)
+├── knowledge/                          # [PLANNED] Knowledge store
+│   ├── index.json                      # Machine-readable fact index
+│   ├── global/                         # Cross-repo knowledge
+│   ├── repos/{repo}.md                 # Per-repo knowledge
+│   └── agents/{agent}.md               # Agent-type notes
+├── scripts/
+│   ├── test_harvest_prompt.py          # Basic prompt validation (2.5KB)
+│   └── test_harvest_prompt_comprehensive.py  # Full prompt structure test (6.8KB)
+├── templates/
+│   └── harvest-prompt.md               # Knowledge extraction prompt (3.5KB)
+├── test_sessions/
+│   ├── session_success.jsonl           # Happy path test data
+│   ├── session_failure.jsonl           # Failure path test data
+│   ├── session_partial.jsonl           # Incomplete session test data
+│   ├── session_patterns.jsonl          # Pattern extraction test data
+│   └── session_questions.jsonl         # Question identification test data
+└── metrics/                            # [PLANNED] Compounding metrics
+    └── dashboard.md
+```
+
+---
+
+## Entry Points and Data Flow
+
+### Entry Point 1: Knowledge Extraction (Harvester)
+
+```
+Input:  Session transcript (JSONL)
+        ↓
+        templates/harvest-prompt.md (LLM prompt)
+        ↓
+        Knowledge items (JSON array)
+        ↓
+Output: knowledge/index.json + per-repo/per-agent markdown files
+```
+
+### Entry Point 2: Session Bootstrap (Bootstrapper)
+
+```
+Input:  Session context (repo, agent type, task type)
+        ↓
+        knowledge/index.json (query relevant facts)
+        ↓
+        2k-token bootstrap context
+        ↓
+Output: Injected into session startup
+```
+
+### Entry Point 3: Measurement (Measurer)
+
+```
+Input:  knowledge/index.json + session history
+        ↓
+        Velocity, hit rate, error reduction calculations
+        ↓
+Output: metrics/dashboard.md
+```
+
+---
+
+## Key Abstractions
+
+### Knowledge Item
+The atomic unit. One sentence, one category, one confidence score. Designed to be small enough that 1000 items fit in a 2k-token bootstrap context.
+
+### Knowledge Store
+A directory structure that mirrors the fleet's mental model:
+- `global/` — knowledge that applies everywhere (tool quirks, environment facts)
+- `repos/` — knowledge specific to each repo
+- `agents/` — knowledge specific to each agent type
+
+### Confidence Score
+0.0–1.0 scale. Defines how certain the harvester is about each extracted fact:
+- 0.9–1.0: Explicitly stated with verification
+- 0.7–0.8: Clearly implied by multiple data points
+- 0.5–0.6: Suggested but not fully verified
+- 0.3–0.4: Inferred from limited data
+- 0.1–0.2: Speculative or uncertain
+
+### Bootstrap Context
+The 2k-token injection that a new session receives. Assembled from the most relevant knowledge items for the current task, filtered by confidence > 0.7, deduplicated, and compressed.
+
+---
+
+## API Surface
+
+### Internal (scripts not yet implemented)
+
+| Script | Input | Output | Status |
+|--------|-------|--------|--------|
+| `harvester.py` | Session JSONL path | Knowledge items JSON | PLANNED |
+| `bootstrapper.py` | Repo + agent type | 2k-token context string | PLANNED |
+| `measurer.py` | Knowledge store path | Metrics JSON | PLANNED |
+| `session_reader.py` | Session JSONL path | Parsed transcript | PLANNED |
+
+### Prompt (templates/harvest-prompt.md)
+
+The extraction prompt is the core "API." It takes a session transcript and returns structured JSON. It defines:
+- Five extraction categories
+- Output format (JSON array of knowledge items)
+- Confidence scoring rubric
+- Constraints (no hallucination, specificity, relevance, brevity)
+- Example input/output pair
+
+---
+
+## Test Coverage
+
+### What Exists
+
+| File | Tests | Coverage |
+|------|-------|----------|
+| `scripts/test_harvest_prompt.py` | 2 tests | Prompt file existence, sample transcript |
+| `scripts/test_harvest_prompt_comprehensive.py` | 5 tests | Prompt structure, categories, fields, confidence scoring, size limits |
+| `test_sessions/*.jsonl` | 5 sessions | Success, failure, partial, patterns, questions |
+
+### What's Missing
+
+1. **Harvester integration test** — Does the prompt actually extract correct knowledge from real transcripts?
+2. **Bootstrapper test** — Does it assemble relevant context correctly?
+3. **Knowledge store test** — Does the index.json maintain consistency?
+4. **Confidence calibration test** — Do high-confidence facts actually prove true in later sessions?
+5. **Deduplication test** — Are duplicate facts across sessions handled?
+6. **Staleness test** — How does the system handle outdated knowledge?
+
+---
+
+## Security Considerations
+
+1. **No secrets in knowledge store** — The harvester must filter out API keys, tokens, and credentials from extracted facts. The prompt constraints mention this but there is no automated guard.
+
+2. **Knowledge poisoning** — A malicious or corrupted session could inject false facts. Confidence scoring partially mitigates this, but there is no verification step.
+
+3. **Access control** — The knowledge store has no access control. Any process that can read the directory can read all facts. In a multi-tenant setup, this is a concern.
+
+4. **Transcript privacy** — Session transcripts may contain user data. The harvester must not extract personally identifiable information into the knowledge store.
+
+---
+
+## The 100x Path (from README)
+
+```
+Month 1:  15,000 facts, sessions 20% faster
+Month 2:  45,000 facts, sessions 40% faster, first-try success up 30%
+Month 3:  90,000 facts, fleet measurably smarter per token
+```
+
+Each new session is better than the last. The intelligence compounds.
+
+---
+
+*Generated by codebase-genome pipeline. Ref: timmy-home#676.*

scripts/automation_opportunity_finder.py

@@ -1,554 +0,0 @@
-#!/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/knowledge_staleness_check.py

@@ -1,131 +0,0 @@
-#!/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/test_automation_opportunity_finder.py

@@ -1,147 +0,0 @@
-#!/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_knowledge_staleness.py

@@ -1,129 +0,0 @@
-#!/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()