Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A coordination protocol for teams of AI agents working on shared projects. Assigns roles by model capability: a lead model handles architecture and decisions, an engineer model handles execution, and a maintainer model handles archiving. Three shared documents (TASK.md, CHANGELOG.md, CONTEXT.md) act as the sync layer, with QMD indexing enabling on-demand context retrieval instead of full history injection.
Tiered model roles cut token costs by reserving expensive models for high-level decisions while cheaper models handle routine execution and archiving.
When to use it
- Running an expensive model for architecture decisions while cheaper models write code
- Tracking task status across multiple agent sessions with a shared TASK.md
- Generating weekly progress reports by aggregating tagged CHANGELOG entries
- Flagging repeated agent workflows as candidate reusable skills
- Coordinating parallel agents on a shared codebase without context conflicts
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: implement a new API endpoint using multiple agents
- 1Lead model queries QMD index to retrieve relevant TASK.md fragments
- 2Lead model breaks the feature into subtasks and updates TASK.md
- 3Engineer model implements the endpoint and writes tests
- 4Engineer model appends a tagged CHANGELOG entry and marks tasks complete
- 5Maintainer model archives completed entries and flags any pattern occurring 3 or more times as a candidate skill
Endpoint implemented with synchronized TASK.md, a new CHANGELOG entry, and a candidate skill flagged if the workflow pattern recurred
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.