Lobster Continuous Learning V2

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

Continuous Learning v2.1 - Instinct

-Based Architecture

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

v2.1 adds project-scoped instincts — React patterns stay in your React project, Python conventions stay in your Python project, and universal patterns (like "always validate input") 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

What's New in v2.1

Feature	v2.0	v2.1
Storage	Global (~/.claude/homunculus/)	Project-scoped (projects//)
Scope	All instincts apply everywhere	Project-scoped + global
Detection	None	git remote URL / repo path
Promotion	N/A	Project → global when seen in 2+ projects
Commands	4 (status/evolve/export/import)	6 (+promote/projects)
Cross-project	Contamination risk	Isolated by default

What's New in v2 (vs v1)

Feature	v1	v2
Observation	Stop hook (session end)	PreToolUse/PostToolUse (100% reliable)
Analysis	Main context	Background agent (Haiku)
Granularity	Full skills	Atomic "instincts"
Confidence	None	0.3-0.9 weighted
Evolution	Direct to skill	Instincts -> cluster -> skill/command/agent
Sharing	None	Export/import instincts

The Instinct Model

An instinct is a small learned behavior:

yaml

---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
scope: project
project_id: "a1b2c3d4e5f6"
project_name: "my-react-app"
---

# Prefer Functional Style

## Action
Use functional patterns over classes when appropriate.

## Evidence
- Observed 5 instances of functional pattern preference
- User corrected class-based approach to functional on 2025-01-15

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

text

Session Activity (in a git repo)
      |
      | Hooks capture prompts + tool use (100% reliable)
      | + detect project context (git remote / repo path)
      v
+---------------------------------------------+
|  projects/<project-hash>/observations.jsonl  |
|   (prompts, tool calls, outcomes, project)   |
+---------------------------------------------+
      |
      | Observer agent reads (background, Haiku)
      v
+---------------------------------------------+
|          PATTERN DETECTION                   |
|   * User corrections -> instinct             |
|   * Error resolutions -> instinct            |
|   * Repeated workflows -> instinct           |
|   * Scope decision: project or global?       |
+---------------------------------------------+
      |
      | Creates/updates
      v
+---------------------------------------------+
|  projects/<project-hash>/instincts/personal/ |
|   * prefer-functional.yaml (0.7) [project]   |
|   * use-react-hooks.yaml (0.9) [project]     |
+---------------------------------------------+
|  instincts/personal/  (GLOBAL)               |
|   * always-validate-input.yaml (0.85) [global]|
|   * grep-before-edit.yaml (0.6) [global]     |
+---------------------------------------------+
      |
      | /evolve clusters + /promote
      v
+---------------------------------------------+
|  projects/<hash>/evolved/ (project-scoped)   |
|  evolved/ (global)                           |
|   * commands/new-feature.md                  |
|   * skills/testing-workflow.md               |
|   * agents/refactor-specialist.md            |
+---------------------------------------------+

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 (same repo on different machines gets the same ID)
3. git rev-parse --show-toplevel -- fallback using repo path (machine-specific)
4. Global fallback -- if no project is detected, instincts go to global scope

Each project gets a 12-character hash ID (e.g., a1b2c3d4e5f6). A registry file at ~/.claude/homunculus/projects.json maps IDs to human-readable names.

Quick Start

1. Enable Observation Hooks

Add to your ~/.claude/settings.json.

If installed as a plugin (recommended):

json

{
  "hooks": {
    "PreToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh"
      }]
    }]
  }
}

If installed manually to ~/.claude/skills:

json

{
  "hooks": {
    "PreToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh"
      }]
    }]
  }
}

2. Initialize Directory Structure

The system creates directories automatically on first use, but you can also create them manually:

bash

# Global directories
mkdir -p ~/.claude/homunculus/{instincts/{personal,inherited},evolved/{agents,skills,commands},projects}

# Project directories are auto-created when the hook first runs in a git repo

3. Use the Instinct Commands

bash

/instinct-status     # Show learned instincts (project + global)
/evolve              # Cluster related instincts into skills/commands
/instinct-export     # Export instincts to file
/instinct-import     # Import instincts from others
/promote             # Promote project instincts to global scope
/projects            # List all known projects and their instinct counts

Commands

Command	Description
/instinct-status	Show all instincts (project-scoped + global) with confidence
/evolve	Cluster related instincts into skills/commands, suggest promotions
/instinct-export	Export instincts (filterable by scope/domain)
/instinct-import <file>	Import instincts with scope control
/promote [id]	Promote project instincts to global scope
/projects	List all known projects and their instinct counts

Configuration

Edit config.json to control the background observer:

json

{
  "version": "2.1",
  "observer": {
    "enabled": false,
    "run_interval_minutes": 5,
    "min_observations_to_analyze": 20
  }
}

Key	Default	Description
observer.enabled	false	Enable the background observer agent
observer.run_interval_minutes	5	How often the observer analyzes observations
observer.min_observations_to_analyze	20	Minimum observations before analysis runs

Other behavior (observation capture, instinct thresholds, project scoping, promotion criteria) is configured via code defaults in instinct-cli.py and observe.sh.

File Structure

text

~/.claude/homunculus/
+-- identity.json           # Your profile, technical level
+-- projects.json           # Registry: project hash -> name/path/remote
+-- observations.jsonl      # Global observations (fallback)
+-- instincts/
|   +-- personal/           # Global auto-learned instincts
|   +-- inherited/          # Global imported instincts
+-- evolved/
|   +-- agents/             # Global generated agents
|   +-- skills/             # Global generated skills
|   +-- commands/           # Global generated commands
+-- projects/
    +-- a1b2c3d4e5f6/       # Project hash (from git remote URL)
    |   +-- project.json    # Per-project metadata mirror (id/name/root/remote)
    |   +-- observations.jsonl
    |   +-- observations.archive/
    |   +-- instincts/
    |   |   +-- personal/   # Project-specific auto-learned
    |   |   +-- inherited/  # Project-specific imported
    |   +-- evolved/
    |       +-- skills/
    |       +-- commands/
    |       +-- agents/
    +-- f6e5d4c3b2a1/       # Another project
        +-- ...

Scope Decision Guide

Pattern Type	Scope	Examples
Language/framework conventions	project	"Use React hooks", "Follow Django REST patterns"
File structure preferences	project	"Tests in __tests__/", "Components in src/components/"
Code style	project	"Use functional style", "Prefer dataclasses"
Error handling strategies	project	"Use Result type for errors"
Security practices	global	"Validate user input", "Sanitize SQL"
General best practices	global	"Write tests first", "Always handle errors"
Tool workflow preferences	global	"Grep before Edit", "Read before Write"
Git practices	global	"Conventional commits", "Small focused commits"

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:

bash

# Promote a specific instinct
python3 instinct-cli.py promote prefer-explicit-errors

# Auto-promote all qualifying instincts
python3 instinct-cli.py promote

# Preview without changes
python3 instinct-cli.py promote --dry-run

The /evolve command also suggests promotion candidates.

Confidence Scoring

Confidence evolves over time:

Score	Meaning	Behavior
0.3	Tentative	Suggested but not enforced
0.5	Moderate	Applied when relevant
0.7	Strong	Auto-approved for application
0.9	Near-certain	Core behavior

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 for Observation?

"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

Backward Compatibility

v2.1 is fully compatible with v2.0 and v1:

• Existing global instincts in ~/.claude/homunculus/instincts/ still work as global instincts
• Existing ~/.claude/skills/learned/ skills from v1 still work
• Stop hook still runs (but now also feeds into v2)
• Gradual migration: run both in parallel

Privacy

• Observations stay local on your machine
• Project-scoped instincts are isolated per project
• Only instincts (patterns) can be exported — not raw observations
• No actual code or conversation content is shared
• You control what gets exported and promoted

Related

• ECC-Tools GitHub App - Generate instincts from repo history
• Homunculus - Community project that inspired the v2 instinct-based architecture (atomic observations, confidence scoring, instinct evolution pipeline)
• The Longform Guide - Continuous learning section

---

Instinct-based learning: teaching Claude your patterns, one project at a time.