capy-cortex

name	capy-cortex
description	Autonomous learning system - learns from mistakes, reflects on sessions, and gets smarter over time. The AI brain.

Capy Cortex - Autonomous Learning System

You have a persistent learning brain powered by SQLite + FTS5 + sklearn TF-IDF. Knowledge is automatically loaded via hooks. This file describes manual operations.

Architecture

• Database: ~/.claude/skills/capy-cortex/cortex.db (SQLite + FTS5 + WAL)
• Hooks (automatic, never call manually):
  • SessionStart: Loads anti-patterns, preferences, principles
  • UserPromptSubmit: Retrieves task-relevant rules via FTS5
  • PreToolUse(Bash): Blocks known dangerous commands
  • PostToolUseFailure: Records errors as anti-patterns
  • Stop: Extracts corrections and preferences from conversation
• Scripts (for manual/scheduled use):
  • cortex.py: Core engine (retrieve, add rules, stats)
  • reflect.py: Deep session analysis
  • consolidate.py: Cluster rules into principles (sklearn)
  • bootstrap.py: Mine historical sessions

Manual Commands

# Check system health
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py stats

# Retrieve rules for a topic
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py retrieve "react typescript"

# Add a rule manually
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py add-rule "Always use TypeScript strict mode" "best_practice"

# Add an anti-pattern
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py add-ap "Never force push to main" "critical"

# Add a preference
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py add-pref "User prefers functional components over class components"

# Run consolidation (clusters rules into principles)
python3 ~/.claude/skills/capy-cortex/scripts/consolidate.py

# Retrain TF-IDF model
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py retrain

# Apply confidence decay
python3 ~/.claude/skills/capy-cortex/scripts/cortex.py decay

How It Learns

1. Automatic (via hooks): Errors are captured, corrections noted, preferences extracted
2. Reflection: Deep analysis of session transcripts extracts patterns
3. Consolidation: sklearn clustering groups similar rules into principles
4. Decay: Old, unreinforced rules fade; validated rules strengthen
5. Retrieval: Two-stage FTS5 + TF-IDF returns only relevant knowledge (O(1) context)