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An advanced learning system that turns your Claude Code sessions into reusable knowledge through atomic “instincts” - small learned behaviors with confidence scoring.

What’s New in v2.1

v2.1 adds project-scoped instincts — React patterns stay in your React project, Python conventions stay in your Python project, and universal patterns are shared globally.

When to Activate

  • Setting up automatic learning from Claude Code sessions
  • Configuring instinct-based behavior extraction via hooks
  • Tuning confidence thresholds for learned behaviors
  • Reviewing, exporting, or importing instinct libraries
  • Evolving instincts into full skills, commands, or agents
  • Managing project-scoped vs global instincts
  • Promoting instincts from project to global scope

The Instinct Model

An instinct is a small learned behavior:
Properties:
  • Atomic — one trigger, one action
  • Confidence-weighted — 0.3 = tentative, 0.9 = near certain
  • Domain-tagged — code-style, testing, git, debugging, workflow, etc.
  • Evidence-backed — tracks what observations created it
  • Scope-aware — project (default) or global

How It Works

Project Detection

The system automatically detects your current project:
  1. CLAUDE_PROJECT_DIR env var (highest priority)
  2. git remote get-url origin — hashed to create a portable project ID
  3. git rev-parse --show-toplevel — fallback using repo path
  4. Global fallback — if no project is detected, instincts go to global scope
Each project gets a 12-character hash ID (e.g., a1b2c3d4e5f6).

Quick Start

Scope Decision Guide

Instinct Promotion (Project → Global)

When the same instinct appears in multiple projects with high confidence, it’s a candidate for promotion to global scope. Auto-promotion criteria:
  • Same instinct ID in 2+ projects
  • Average confidence >= 0.8
How to promote:

Confidence Scoring

Confidence increases when:
  • Pattern is repeatedly observed
  • User doesn’t correct the suggested behavior
  • Similar instincts from other sources agree
Confidence decreases when:
  • User explicitly corrects the behavior
  • Pattern isn’t observed for extended periods
  • Contradicting evidence appears

Why Hooks vs Skills?

“v1 relied on skills to observe. Skills are probabilistic — they fire ~50-80% of the time based on Claude’s judgment.”
Hooks fire 100% of the time, deterministically. This means:
  • Every tool call is observed
  • No patterns are missed
  • Learning is comprehensive
Instinct-based learning: teaching Claude your patterns, one project at a time.