claw-conductor

Review·Scanned 2/18/2026

Claw Conductor is an always-on development orchestrator that maps Discord channels to project workspaces under /root/projects, decomposes requests, runs AI models, and commits/pushes code. It invokes local commands (openclaw, git, gh), uses env vars like GITHUB_USER/DISCORD_CHANNEL_NAME, and performs network calls to github.com and other external services.

from clawhub.ai·v2.1.0·252.4 KB·0 installs
Scanned from 0.1.7 at 9c5c521 · Transparency log ↗
$ vett add clawhub.ai/johnsonfarmsus/claw-conductorReview findings below

Claw Conductor v2.1

Build Complete Projects with a Single Request

Transform complex development requests into working software through intelligent multi-model orchestration. Claw Conductor decomposes your request, routes subtasks to optimal AI models, executes in parallel, and delivers a complete project.

🚀 What is This?

Claw Conductor is a full autonomous development orchestrator that transforms a single Discord message into a complete, working software project.

The Problem

Building software requires multiple AI models for different tasks:

  • Database design → Best with Mistral Devstral (256K context)
  • Unit tests → Best with Llama 3.3 70B (test generation expert)
  • Frontend UI → Best with Mistral Devstral (near-Claude quality)
  • API endpoints → Best with Llama or Mistral
  • Research → Best with Perplexity (internet access)

Manually coordinating these models is tedious and error-prone.

The Solution

Single Discord request:

@OpenClaw use claw-conductor to build a towing dispatch system with:
- Customer portal for requesting service
- Driver dashboard for accepting jobs
- Admin panel for managing users
- Real-time location tracking

Claw Conductor automatically:

  1. Decomposes into 8 subtasks with dependencies
  2. 🎯 Routes each to the optimal model (Mistral/Llama/Perplexity)
  3. Executes up to 5 tasks in parallel
  4. 📦 Consolidates results, runs tests, commits to git
  5. 🐙 Pushes complete project to GitHub

Result: Working dispatch system in ~45 minutes, zero manual intervention.


✨ What's New in v2.1

🤖 Always-On Triage: Automatically classifies requests as simple questions vs development tasks 💬 Discord Integration: Auto-maps Discord channels to project workspaces in /root/projectsDual-Mode Response: Fast answers for questions, full orchestration for development 🎯 Smart Routing: Simple mode uses fast models, development mode uses full pipeline 🔧 User Overrides: Force classification with !simple or !dev flags 📋 Path Announcements: Visual indicators (📋 Simple / 🔧 Development) show routing decisions 🌍 Project-Aware: All responses know their workspace, channel, and context 🎨 Multi-Project: Handle multiple concurrent requests across different Discord channels


📋 Complete Workflow

Discord Message in #calculator-app channel
    ↓
1. Context Detection & Triage
   ├─ Discord Integration: Maps #calculator-app → /root/projects/calculator-app
   ├─ Request Classification: "Build a calculator app" → DEVELOPMENT
   ├─ Path Announcement: 🔧 Development mode - full orchestration
   └─ Route to: Full decomposition pipeline
    ↓
2. AI-Powered Task Decomposition (Development Mode)
   ├─ Uses best-rated model (auto-selected or configured)
   ├─ Task 1: Calculator logic (backend-development, complexity: 2)
   ├─ Task 2: UI components (frontend-development, complexity: 2)
   └─ Task 3: Unit tests (unit-test-generation, complexity: 2)
    ↓
3. Intelligent Routing
   ├─ Task 1 → Llama 3.3 70B (score: 87/100)
   ├─ Task 2 → Mistral Devstral (score: 95/100)
   └─ Task 3 → Llama 3.3 70B (score: 95/100)
    ↓
4. Project Initialization
   ├─ Workspace: /root/projects/calculator-app/ (from Discord mapping)
   ├─ Initializes git repository
   ├─ Creates GitHub repo: {user}/calculator-app
   └─ Registers OpenClaw agent
    ↓
5. Parallel Execution (max 5 concurrent)
   ├─ Worker 1: Task 1 (Llama) → Running
   ├─ Worker 2: Task 2 (Devstral) → Running
   └─ Worker 3: Task 3 (Llama) → Running
    ↓
6. Result Consolidation
   ├─ Merges all code changes
   ├─ Runs npm test (passes)
   ├─ Commits: "feat: calculator-app - 3 tasks completed"
   └─ Pushes to github.com/{user}/calculator-app
    ↓
✅ Complete project delivered in ~8 minutes

🎯 Key Features

Always-On Triage & Discord Integration

The Problem: Not every Discord message needs full orchestration. Questions like "What's the status?" shouldn't trigger decomposition.

The Solution: Claw Conductor acts as an always-on layer that intelligently routes requests:

Triage Classification

  • Simple Mode (📋): Questions, status checks, explanations → Fast model response
  • Development Mode (🔧): Build, fix, implement requests → Full orchestration pipeline

Discord Channel Mapping

Discord #scientific-calculator → /root/projects/scientific-calculator
Discord #dispatch-suite → /root/projects/dispatch-suite
Discord #satire-news → /root/projects/satire-news

Each Discord channel in "Active Projects" category automatically maps to its project workspace.

User Overrides

@OpenClaw !simple Build a calculator  → Forces simple mode (just answers)
@OpenClaw !dev What is this project?  → Forces development mode (probably errors)

Example Flows

Simple Request:

User: "What files are in this project?"
  → 📋 Simple response mode
  → Fast answer from Mistral Devstral (project-aware)
  → Response in ~2 seconds

Development Request:

User: "Add user authentication with email verification"
  → 🔧 Development mode - full orchestration
  → Decompose into 5 tasks
  → Execute in parallel
  → Complete project in ~15 minutes

AI-Powered Task Decomposition

  • Intelligent Analysis: Uses AI models to analyze complex requests and break them into structured tasks
  • Auto-Selection: Automatically picks the best model from your registry (or manual override)
  • Fallback Strategy: Falls back to second-best model on failure
  • Structured Output: Generates tasks with categories, complexity ratings, dependencies, and file targets

Intelligent Multi-Model Routing

  • Capability-Based Scoring: 1-5 star ratings per model per task category
  • Complexity Analysis: Hard ceilings prevent overwhelming models
  • Cost Optimization: Prefers free models when capabilities equal
  • Experience Tracking: Learns which models perform best over time

Parallel Task Execution

  • Worker Pool: Up to 5 tasks running simultaneously
  • Dependency-Aware: Database tasks complete before auth tasks
  • File Conflict Detection: Tasks touching same files run sequentially
  • Multi-Project Support: Handle multiple concurrent project requests

Complete Project Delivery

  • Auto-Setup: Creates workspace, git repo, GitHub repo
  • Test Execution: Runs pytest, npm test, etc.
  • Conventional Commits: Clean git history with co-authorship
  • GitHub Integration: Automatic push on completion

🛠️ Installation

Quick Start

# On your OpenClaw server
cd ~/.openclaw/skills
git clone https://github.com/johnsonfarmsus/claw-conductor.git
cd claw-conductor
chmod +x scripts/*.sh scripts/*.py
./scripts/setup.sh

The wizard will:

  1. Detect your configured OpenClaw models
  2. Ask about cost structure (free vs paid)
  3. Create personalized agent-registry.json
  4. Set routing preferences

Manual Setup

# Copy example configuration
cp config/agent-registry.example.json config/agent-registry.json

# Add your models
python3 scripts/update-capability.py \
  --agent mistral-devstral-2512 \
  --category frontend-development \
  --rating 5 \
  --max-complexity 5

📚 Usage

Simple Request

@OpenClaw use claw-conductor to build a calculator with:
- Basic operations (add, subtract, multiply, divide)
- Clean UI
- Unit tests

Result:

  • 3 tasks decomposed
  • 2 models used (Mistral + Llama)
  • Completed in ~8 minutes
  • GitHub repo created and pushed

Complex Request

@OpenClaw use claw-conductor to build a towing dispatch system with:
- Customer portal for requesting service
- Driver dashboard for accepting jobs
- Admin panel for managing users
- Real-time location tracking
- Payment integration

Result:

  • 8 tasks decomposed (database, auth, UIs, API, tracking, tests, docs)
  • 3 models used (Mistral, Llama, Perplexity)
  • Completed in ~45 minutes
  • Full working application on GitHub

Testing the Router

# Test decomposition and routing (dry run)
cd scripts
python3 orchestrator.py

# Test with custom request
python3 decomposer.py "Build a blog system with comments"

# Test routing for specific task
python3 router.py --test

🎓 How It Works

1. Task Decomposition

Analyzes your request and breaks into categorized subtasks:

Request: "Build user registration with email verification"

Subtasks:
├─ Database schema (database-operations, complexity: 3)
├─ Registration API (api-development, complexity: 3)
├─ Email verification (backend-development, complexity: 4)
├─ Registration UI (frontend-development, complexity: 2)
└─ Unit tests (unit-test-generation, complexity: 3)

Dependencies: API depends on database, UI depends on API, tests depend on all

2. Intelligent Routing

Each task scored 0-100 against all models:

Example: API Development (complexity: 3)

Mistral Devstral 2512:
  Rating: 4★ (40 pts)
  Complexity fit: Can handle 3 (40 pts)
  Experience: 0 tasks (0 pts)
  Cost: Free tier (10 pts)
  Total: 90/100

Llama 3.3 70B:
  Rating: 4★ (40 pts)
  Complexity fit: Can handle 4 (40 pts)
  Experience: 2 tasks (2 pts)
  Cost: Free (10 pts)
  Total: 92/100 ✅ Winner

3. Parallel Execution

Worker Pool (max 5 concurrent):

Worker 1: Database schema (Mistral) → Running
Worker 2: Registration UI (Devstral) → Running
Worker 3: Tests (Llama) → Queued (waits for API)
Worker 4: -
Worker 5: -

After database completes:
Worker 1: Registration API (Llama) → Running

After API completes:
Worker 1: Tests (Llama) → Running

4. Result Consolidation

# After all tasks complete:
1. Check git status for conflicts → None
2. Run tests: npm test → ✅ Passed
3. Commit: "feat: user-registration - 5 tasks completed"
4. Push to github.com/{user}/user-registration
5. Report to Discord: "✅ Project completed!"

🔧 Configuration

Agent Registry

config/agent-registry.json:

{
  "user_config": {
    "cost_tracking_enabled": true,
    "prefer_free_when_equal": true,
    "max_parallel_tasks": 5,
    "fallback": {
      "enabled": true,
      "retry_delay_seconds": 2
    }
  },
  "agents": {
    "mistral-devstral-2512": {
      "model_id": "mistral/devstral-2512",
      "enabled": true,
      "user_cost": {
        "type": "free-tier"
      },
      "capabilities": {
        "frontend-development": {
          "rating": 5,
          "max_complexity": 5
        }
      }
    }
  }
}

Task Categories (23 Standard)

  • code-generation-new-features
  • bug-detection-fixes
  • multi-file-refactoring
  • unit-test-generation
  • api-development
  • frontend-development
  • backend-development
  • database-operations
  • security-fixes
  • documentation-generation
  • performance-optimization
  • algorithm-implementation
  • And more...

See config/task-categories.json


📊 Supported Models

Default Profiles

ModelProviderCostBest For
Mistral Devstral 2512MistralFree tierMulti-file refactoring, frontend, 256K context
Llama 3.3 70BOpenRouterFreeUnit tests, algorithms, boilerplate
Perplexity SonarPerplexity$1/M tokensResearch, documentation, exploration
Claude Sonnet 4.5Anthropic$3-15/MComplex architecture (future)
GPT-4 TurboOpenAI$10-30/MGeneral purpose (future)

See config/defaults/ for all profiles.


🎯 Examples

Example 1: Calculator

Request: Build a calculator with operations and tests

Decomposition:

  • Task 1: Calculator logic (Llama)
  • Task 2: UI components (Devstral)
  • Task 3: Unit tests (Llama)

Result: 3 tasks, 8 minutes, pushed to GitHub

Example 2: Blog API

Request: REST API for blog with auth and docs

Decomposition:

  • Task 1: Database schema (Mistral)
  • Task 2: Auth system (Mistral)
  • Task 3: CRUD endpoints (Llama)
  • Task 4: Swagger docs (Perplexity)
  • Task 5: Integration tests (Llama)

Result: 5 tasks, 20 minutes, API-first design

Example 3: Dispatch System

Request: Full towing dispatch with portals and tracking

Decomposition:

  • Task 1: Database (Mistral)
  • Task 2: Auth (Mistral)
  • Task 3-5: UIs for customer/driver/admin (Devstral)
  • Task 6: REST API (Llama)
  • Task 7: Real-time tracking (Mistral)
  • Task 8: Tests (Llama)

Result: 8 tasks, 45 minutes, full application


🔍 Advanced Features

Conservative Fallback

If a task fails:

  1. Try primary model (attempt 1)
  2. Try primary model (attempt 2)
  3. Try first runner-up (attempt 3)
  4. Try first runner-up (attempt 4)
  5. Give up, report to Discord

File Conflict Detection

Task 1: Modify src/api/users.js → Running
Task 2: Modify src/api/users.js → Queued (waits)
Task 3: Modify src/ui/home.js → Running (independent)

Multi-Project Concurrency

Project A: Dispatch System (3 tasks running)
Project B: Calculator (2 tasks running)
───────────────────────────────────────────
Total: 5 tasks (at global max_parallel_tasks)

📖 Documentation

  • SKILL.md - Complete usage guide for OpenClaw
  • docs/orchestration-design.md - Architecture details
  • examples/ - Working examples with walkthroughs
  • config/ - Configuration reference

🤝 Contributing

We welcome contributions!

  1. Share benchmark data - Help improve capability ratings
  2. Add model profiles - Support more AI models
  3. Improve decomposition - Better task breakdown logic
  4. Report issues - Help us fix bugs

See CONTRIBUTING.md for guidelines.


📄 License

GNU AGPL v3 - See LICENSE

This copyleft license requires anyone running a modified version on a server to make source code available to users.


🙏 Acknowledgments


🔗 Links


Made with 🎼 for autonomous development

Stop manually coordinating AI models. Let the Conductor orchestrate.