capy-cortex
Autonomous learning system - learns from mistakes, reflects on sessions, and gets smarter over time. The AI brain.
Best use case
capy-cortex is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Autonomous learning system - learns from mistakes, reflects on sessions, and gets smarter over time. The AI brain.
Teams using capy-cortex should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/capy-cortex/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How capy-cortex Compares
| Feature / Agent | capy-cortex | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Autonomous learning system - learns from mistakes, reflects on sessions, and gets smarter over time. The AI brain.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 ```bash # 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)
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