Refactor cockpit to use DockerTmuxController pattern
Based on claude-code-tools TmuxCLIController, this refactor: - Added DockerTmuxController class for robust tmux session management - Implements send_keys() with configurable delay_enter - Implements capture_pane() for output retrieval - Implements wait_for_prompt() for pattern-based completion detection - Implements wait_for_idle() for content-hash-based idle detection - Implements wait_for_shell_prompt() for shell prompt detection Also includes workflow improvements: - Pre-task git snapshot before agent execution - Post-task commit protocol in agent guidelines Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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admin
367
lib/kg_pattern_detector.py
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367
lib/kg_pattern_detector.py
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#!/usr/bin/env python3
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"""
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Knowledge Graph Pattern Detector
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Identifies incomplete research sessions in both the KG database and file system.
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Detects 4 pattern types:
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1. Unresolved questions - content ends with user decision point
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2. Minimal duration - sessions <5min with no findings
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3. Claude indicators without conclusions - Assistant responses missing synthesis
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4. Markdown files - incomplete research in file system
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"""
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import re
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import time
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import json
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from sqlite3 import connect as sqlite_connect
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from pathlib import Path
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from typing import List, Dict, Tuple
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class KGPatternDetector:
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"""Detect incomplete research patterns in KG databases and file system."""
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# Question patterns indicating unresolved state
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UNRESOLVED_QUESTION_PATTERNS = [
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r'what\s+(?:do\s+you|should\s+we|would\s+you)',
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r'which\s+(?:approach|method|option)',
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r'should\s+we',
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r'please\s+choose',
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r'your\s+(?:thoughts|preference|opinion)',
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r'(?:any|what)\s+thoughts',
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r'(?:how|what)\s+would\s+you',
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r'would\s+you\s+(?:recommend|prefer)',
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r'what\'s\s+your',
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r'do\s+you\s+(?:think|agree)',
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]
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# Claude writing indicators
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CLAUDE_INDICATORS = [
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r'^assistant:',
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r'^i\'ll\s+(?:analyze|review|help|create|implement)',
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r'^let\s+me\s+(?:analyze|review|help)',
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r'^here\'s',
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r'^based\s+on',
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r'^to\s+summarize',
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r'^in\s+summary',
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r'^the\s+key\s+(?:findings|points)',
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]
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# Conclusion/synthesis indicators
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CONCLUSION_PATTERNS = [
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r'conclusion:',
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r'findings?:',
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r'recommendation:',
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r'summary:',
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r'synthesis:',
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r'takeaway:',
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r'next\s+steps?:',
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r'action\s+items?:',
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]
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def __init__(self):
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"""Initialize the pattern detector."""
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self.findings: List[Dict] = []
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def find_incomplete_research_kg(self, db_path: str, time_scope_days: int = 30) -> List[Dict]:
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"""
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Find incomplete research sessions in KG database.
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Args:
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db_path: Path to research.db SQLite database
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time_scope_days: Only examine sessions modified in last N days
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Returns:
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List of finding dicts with source, id, name, pattern, severity, example
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"""
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findings = []
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cutoff_time = time.time() - (time_scope_days * 86400)
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if not Path(db_path).exists():
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return findings
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try:
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with sqlite_connect(db_path) as conn:
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cursor = conn.cursor()
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# Query research entities from last 30 days
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cursor.execute("""
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SELECT id, name, content, created_at, updated_at
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FROM entities
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WHERE type = 'session' AND domain = 'research'
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AND updated_at > ?
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ORDER BY updated_at DESC
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""", (cutoff_time,))
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for row in cursor.fetchall():
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entity_id, name, content, created, updated = row
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# Skip empty content
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if not content or not isinstance(content, str):
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continue
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duration_secs = int(updated - created)
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# Pattern 1: Unresolved questions
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if self._has_unresolved_question(content):
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findings.append({
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'source': 'kg_database',
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'id': entity_id,
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'name': name,
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'pattern': 'unresolved_question',
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'duration_secs': duration_secs,
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'severity': 'high',
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'example': self._extract_ending(content, 300),
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'timestamp': updated,
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'db_path': db_path
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})
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# Pattern 2: Minimal duration with no findings
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if duration_secs < 300 and not self._has_findings(content):
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findings.append({
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'source': 'kg_database',
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'id': entity_id,
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'name': name,
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'pattern': 'incomplete_duration',
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'duration_secs': duration_secs,
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'severity': 'medium',
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'example': content[:300],
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'timestamp': updated,
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'db_path': db_path
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})
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# Pattern 3: Claude indicators without conclusions
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if self._has_claude_indicators(content) and not self._has_conclusions(content):
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findings.append({
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'source': 'kg_database',
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'id': entity_id,
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'name': name,
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'pattern': 'claude_no_conclusion',
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'duration_secs': duration_secs,
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'severity': 'high',
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'example': self._extract_ending(content, 300),
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'timestamp': updated,
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'db_path': db_path
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})
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except Exception as e:
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print(f"Error querying KG database {db_path}: {e}")
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return findings
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def find_incomplete_research_files(self, time_scope_days: int = 30) -> List[Dict]:
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"""
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Find incomplete research in markdown and JSON files.
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Args:
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time_scope_days: Only examine files modified in last N days
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Returns:
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List of finding dicts with source, path, pattern, severity, example
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"""
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findings = []
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cutoff_time = time.time() - (time_scope_days * 86400)
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# Search in relevant directories
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search_dirs = [
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Path('/home/admin'),
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Path('/home/admin/conductor'),
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Path('/opt/server-agents/state'),
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]
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# File patterns that might contain research
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file_patterns = [
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'**/*research*.md',
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'**/*findings*.md',
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'**/*analysis*.md',
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'**/*research*.json',
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'**/*incomplete*.md',
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'**/*session*.json',
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]
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for search_dir in search_dirs:
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if not search_dir.exists():
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continue
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for file_pattern in file_patterns:
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try:
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for file_path in search_dir.glob(file_pattern):
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# Skip if too old
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mtime = file_path.stat().st_mtime
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if mtime < cutoff_time:
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continue
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try:
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content = file_path.read_text(errors='ignore')
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# Skip very small files (likely noise)
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if len(content) < 100:
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continue
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# Pattern 1: Unresolved questions
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if self._has_unresolved_question(content):
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findings.append({
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'source': 'file',
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'path': str(file_path),
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'pattern': 'unresolved_question',
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'severity': 'high',
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'example': self._extract_ending(content, 300),
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'timestamp': mtime
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})
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# Pattern 3: Claude indicators without conclusions
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if self._has_claude_indicators(content) and not self._has_conclusions(content):
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findings.append({
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'source': 'file',
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'path': str(file_path),
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'pattern': 'claude_no_conclusion',
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'severity': 'high',
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'example': self._extract_ending(content, 300),
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'timestamp': mtime
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})
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except Exception as e:
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# Silently skip files that can't be read
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pass
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except Exception:
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pass
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return findings
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def find_all_incomplete_research(self,
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kg_db_paths: List[str] = None,
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time_scope_days: int = 30) -> Dict:
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"""
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Comprehensive incomplete research discovery across KG and files.
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Args:
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kg_db_paths: List of KG database paths to check. Defaults to standard locations.
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time_scope_days: Time scope for search in days
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Returns:
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Dict with:
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- 'findings': List of all findings
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- 'summary': Dict with counts by pattern and severity
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- 'timestamp': When scan was performed
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"""
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if kg_db_paths is None:
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kg_db_paths = [
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'/etc/luz-knowledge/research.db',
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'/etc/zen-swarm/memory/research.db',
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]
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all_findings = []
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# Scan KG databases
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for db_path in kg_db_paths:
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kg_findings = self.find_incomplete_research_kg(db_path, time_scope_days)
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all_findings.extend(kg_findings)
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# Scan file system
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file_findings = self.find_incomplete_research_files(time_scope_days)
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all_findings.extend(file_findings)
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# Generate summary statistics
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summary = self._generate_summary(all_findings)
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return {
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'findings': all_findings,
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'summary': summary,
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'timestamp': time.time(),
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'time_scope_days': time_scope_days
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}
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def _has_unresolved_question(self, content: str) -> bool:
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"""Check if content contains unresolved user decision points."""
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if not content:
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return False
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# Check if ends with question-like pattern
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for pattern in self.UNRESOLVED_QUESTION_PATTERNS:
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if re.search(pattern, content, re.IGNORECASE | re.MULTILINE):
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# Make sure there's no resolution after the question
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if not re.search(r'(conclusion|resolution|decision made|will do|here\'s|approved)',
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content[-200:], re.IGNORECASE):
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return True
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return False
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def _has_findings(self, content: str) -> bool:
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"""Check if content contains synthesis/findings/analysis section."""
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if not content:
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return False
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return re.search(
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r'(finding|synthesis|analysis|conclusion|recommendation)',
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content,
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re.IGNORECASE
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) is not None
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def _has_claude_indicators(self, content: str) -> bool:
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"""Check if content contains Claude-style writing indicators."""
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if not content:
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return False
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for pattern in self.CLAUDE_INDICATORS:
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if re.search(pattern, content, re.MULTILINE):
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return True
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return False
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def _has_conclusions(self, content: str) -> bool:
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"""Check if content contains conclusion/synthesis indicators."""
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if not content:
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return False
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return re.search(
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r'|'.join(self.CONCLUSION_PATTERNS),
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content,
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re.IGNORECASE
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) is not None
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def _extract_ending(self, content: str, length: int) -> str:
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"""Extract last N characters of content (the most relevant part)."""
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if not content:
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return ""
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return content[-length:] if len(content) > length else content
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def _generate_summary(self, findings: List[Dict]) -> Dict:
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"""Generate statistics about findings."""
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summary = {
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'total': len(findings),
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'by_pattern': {},
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'by_severity': {},
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'by_source': {}
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}
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for finding in findings:
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pattern = finding.get('pattern', 'unknown')
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severity = finding.get('severity', 'unknown')
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source = finding.get('source', 'unknown')
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summary['by_pattern'][pattern] = summary['by_pattern'].get(pattern, 0) + 1
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summary['by_severity'][severity] = summary['by_severity'].get(severity, 0) + 1
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summary['by_source'][source] = summary['by_source'].get(source, 0) + 1
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return summary
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if __name__ == '__main__':
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# Quick test
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detector = KGPatternDetector()
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result = detector.find_all_incomplete_research(time_scope_days=30)
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print(f"Found {result['summary']['total']} incomplete research sessions")
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print(f"Summary by pattern: {result['summary']['by_pattern']}")
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print(f"Summary by severity: {result['summary']['by_severity']}")
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# Show first few findings
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for finding in result['findings'][:5]:
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print(f"\n[{finding['severity'].upper()}] {finding['pattern']}")
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print(f" Source: {finding['source']}")
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if 'name' in finding:
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print(f" Name: {finding['name']}")
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if 'path' in finding:
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print(f" Path: {finding['path']}")
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print(f" Example: {finding['example'][:100]}...")
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