🐾 claw-stack
Architecture Module OpenClaw Ecosystem

Agent Swarm

δΈ€δΊΊ = δΈ€ζ”―εΌ€ε‘ε›’ι˜Ÿ — One person, a full AI development team

Overview

AI Dev Workforce is a framework for running an autonomous AI development team. It connects 4 modules β€” daily issue scanning, task tracking, AI code review, and intelligent failure recovery β€” into a self-sustaining loop. You set the direction; the agents handle the rest.

Modules

Key Features

Four purpose-built modules that snap together into a fully autonomous development pipeline.

Morning Scan

Module 1

Runs daily at 09:00 EST via cron. Scans GitHub Issues for new actionable items, filters already-handled tasks, and automatically dispatches them to the coding agent β€” all before you've had your first coffee.

GitHub Issues Cron Auto-dispatch

Task Registry

Module 2

Every spawned agent writes to active-tasks.json on creation and updates it on completion. One command gives you real-time status across every parallel task, PR URL, and outcome note.

JSON Registry Cross-session PR Tracking

AI PR Review

Module 3

Every PR triggers a self-hosted GitHub Actions runner on the local server. It pipes the diff through Gemini CLI for a full code review covering bugs, performance, security, and quality β€” then posts the verdict as a PR comment and fires an iMessage notification.

Gemini AI Self-hosted Runner iMessage

Ralph Loop V2

Module 4

Intelligent failure recovery that never blindly retries. When an agent fails, it classifies the failure into one of 4 types β€” missing context, direction drift, environment issue, or task too large β€” then rewrites the prompt accordingly before spawning again.

4 Failure Types Prompt Rewrite Smart Retry

Architecture

How It Works

A closed-loop orchestration pipeline. The main agent dispatches work; everything else runs autonomously β€” right up to the iMessage notification in your pocket.

01

You β†’ Main Agent

Set a direction or let Morning Scan surface tasks automatically from GitHub Issues.

02

Main Agent β†’ Coding Agent

The orchestrator spawns a dedicated coding agent (tmux + Claude Code) and registers the task in active-tasks.json.

03

Coding Agent β†’ GitHub PR

The coding agent implements the feature or fix and opens a pull request β€” no human intervention.

04

PR β†’ Gemini AI Review

GitHub Actions triggers the self-hosted runner on the local server. Gemini reviews the diff and posts a verdict comment.

05

Review β†’ iMessage Notification

Once the review completes, a notification fires to your phone. APPROVE, REQUEST_CHANGES, or COMMENT β€” you're always in the loop.

06

Failure β†’ Ralph Loop V2

If any step fails, the failure is classified (A/B/C/D), the prompt is rewritten, and the agent is re-spawned intelligently.

4
Orchestration Modules
4
Failure Recovery Types
0
External Dependencies
∞
Retry Intelligence

Ralph Loop V2 β€” Failure Types

A

Missing Context

File not found, incomplete output, "can't see the full picture"

B

Direction Drift

Agent did X but you wanted Y, ignored constraints

C

Environment Issue

Command errors, missing deps, permission failures

D

Task Too Large

Timeout, chaotic output, stuck midway β€” needs splitting

Coverage

What Gets Automated

Every step of the development loop β€” from triage to shipping β€” handled by agents so you can stay in the architect's seat.

Issue Triage

Morning Scan scans GitHub Issues daily and auto-routes actionable items to the coding agent.

Code Generation

A dedicated coding agent (tmux + Claude Code) implements every task autonomously end-to-end.

PR Review

Gemini AI reviews every pull request via a self-hosted GitHub Actions runner on the local server.

Failure Recovery

Ralph Loop V2 classifies failures into 4 types and rewrites the prompt before retrying.

Task Tracking

active-tasks.json registers every spawned agent β€” status, PR URL, and notes in one place.

Notifications

iMessage alerts fire when PR review completes, so nothing slips through without your awareness.