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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. Use when capturing lessons from a session, managing instincts, or promoting them into skills, commands, or agents.

continuous-learning-v2 とは?

continuous-learning-v2 is a Claude Code agent skill that 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. Use when capturing lessons from a session, managing instincts, or promoting them into skills, commands, or agents.

対応Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/continuous-learning-v2

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ドキュメント

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

Featurev2.0v2.1
StorageGlobal (~/.claude/homunculus/)Project-scoped (${XDG_DATA_HOME:-~/.local/share}/ecc-homunculus/projects/<hash>/)
ScopeAll instincts apply everywhereProject-scoped + global
DetectionNonegit remote URL / repo path
PromotionN/AProject → global when seen in 2+ projects
Commands4 (status/evolve/export/import)6 (+promote/projects)
Cross-projectContamination riskIsolated by default

What's New in v2 (vs v1)

Featurev1v2
ObservationStop hook (session end)PreToolUse/PostToolUse (100% reliable)
AnalysisMain contextBackground agent (Haiku)
GranularityFull skillsAtomic "instincts"
ConfidenceNone0.3-0.9 weighted
EvolutionDirect to skillInstincts -> cluster -> skill/command/agent
SharingNoneExport/import instincts

The Instinct Model

An instinct is a small learned behavior:

---
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

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) -- honored as an explicit override even when the directory is not a git repo (hashed by its absolute path)
  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 ${XDG_DATA_HOME:-~/.local/share}/ecc-homunculus/projects.json maps IDs to human-readable names.

Data Directory

Continuous-learning-v2 stores observer data outside ~/.claude so Claude Code's sensitive-path guard does not block background instinct writes:

  1. CLV2_HOMUNCULUS_DIR when set to an absolute path
  2. $XDG_DATA_HOME/ecc-homunculus
  3. $HOME/.local/share/ecc-homunculus

Existing users with data at ~/.claude/homunculus can migrate once:

bash skills/continuous-learning-v2/scripts/migrate-homunculus.sh

Quick Start

1. Enable Observation Hooks

If installed as a plugin (recommended):

No extra settings.json hook block is required. Claude Code v2.1+ auto-loads the plugin hooks/hooks.json, and observe.sh is already registered there.

If you previously copied observe.sh into ~/.claude/settings.json, remove that duplicate PreToolUse / PostToolUse block. Duplicating the plugin hook causes double execution and ${CLAUDE_PLUGIN_ROOT} resolution errors because that variable is only available inside plugin-managed hooks/hooks.json entries.

If installed manually to ~/.claude/skills, add this to your ~/.claude/settings.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:

# Global directories
mkdir -p "${XDG_DATA_HOME:-$HOME/.local/share}/ecc-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

/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

CommandDescription
/instinct-statusShow all instincts (project-scoped + global) with confidence
/evolveCluster related instincts into skills/commands, suggest promotions
/instinct-exportExport instincts (filterable by scope/domain)
/instinct-import <file>Import instincts with scope control
/promote [id]Promote project instincts to global scope
/projectsList all known projects and their instinct counts

Configuration

Edit config.json to control the background observer:

{
  "version": "2.1",
  "observer": {
    "enabled": false,
    "run_interval_minutes": 5,
    "min_observations_to_analyze": 20
  }
}
KeyDefaultDescription
observer.enabledfalseEnable the background observer agent
observer.run_interval_minutes5How often the observer analyzes observations
observer.min_observations_to_analyze20Minimum 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.

Observer platform support

The background observer requires WSL2, Linux, or macOS. On native Windows (Git Bash / MSYS2) it starts and reports success, but the process is killed when the spawning hook exits and its Job Object closes, so no analysis ever runs — setting observer.enabled: true there is effectively a no-op (see issue #2489).

observe.sh detects this on the following hook invocation and writes an explanatory warning to observer-start.log once the observer has failed to survive several times in a row.

Env varDefaultDescription
ECC_OBSERVER_NOSURVIVE_WARN_AFTER3Consecutive non-survivals before the warning is logged

File Structure

${XDG_DATA_HOME:-~/.local/share}/ecc-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 TypeScopeExamples
Language/framework conventionsproject"Use React hooks", "Follow Django REST patterns"
File structure preferencesproject"Tests in __tests__/", "Components in src/components/"
Code styleproject"Use functional style", "Prefer dataclasses"
Error handling strategiesproject"Use Result type for errors"
Security practicesglobal"Validate user input", "Sanitize SQL"
General best practicesglobal"Write tests first", "Always handle errors"
Tool workflow preferencesglobal"Grep before Edit", "Read before Write"
Git practicesglobal"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:

# 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:

ScoreMeaningBehavior
0.3TentativeSuggested but not enforced
0.5ModerateApplied when relevant
0.7StrongAuto-approved for application
0.9Near-certainCore 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 can be migrated from ~/.claude/homunculus/instincts/ with scripts/migrate-homunculus.sh
  • 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.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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