A paradigm for coordinating multiple AI sub-agents to solve long-horizon software engineering tasks through structured delegation and isolated git worktrees.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install caid-multi-agent
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install caid-multi-agent using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is CAID Multi-Agent Coordination?
CAID Multi-Agent Coordination is a high-performance framework designed for Openclaw Skills that implements the Centralized Asynchronous Isolated Delegation paradigm. It enables a lead manager agent to decompose complex software tasks into a dependency-aware graph, delegating specific units of work to multiple engineer agents. Unlike simple parallel agents, this skill enforces physical git worktree isolation to prevent state corruption and ensures that integration is gated by executable tests, significantly increasing the accuracy of autonomous code generation.
CAID Multi-Agent Coordination Use Cases
- Executing long-horizon software engineering tasks requiring complex multi-file modifications.
- Implementing features with clear dependency structures like API-to-frontend integrations.
- Managing large-scale repository migrations or library implementations where work is highly parallelizable.
- Scenarios where high accuracy is prioritized over immediate speed through sequential, test-verified merging.
How CAID Multi-Agent Coordination Works
- Manager Pre-Setup: The lead agent prepares the environment, creates function stubs, and commits an initial state to the main branch.
- Dependency Graph Creation: Tasks are analyzed to identify atomic units of work and mapped into a dependency-aware execution graph.
- Physical Workspace Isolation: The system creates individual git worktrees for each engineer agent to ensure zero interference during development.
- Structured Delegation: Tasks are assigned using strict JSON formats to maintain alignment and avoid conversational drift.
- Asynchronous Execution and Self-Verification: Engineer agents work concurrently and must pass local tests before submitting work.
- Test-Gated Integration: The manager merges validated branches into the main branch, resolving conflicts as necessary to maintain a single source of truth.
CAID Multi-Agent Coordination Setup
To initialize the environment for these Openclaw Skills, use the following git worktree commands:
# Create physically isolated workspaces
git worktree add ../workspace-engineer-1 <branch-name-1>
git worktree add ../workspace-engineer-2 <branch-name-2>
Configure the OpenClaw sessions_spawn tool to manage parallel sub-agents:
{
"runtime": "subagent",
"task": "<task specification with context>",
"agentId": "engineer-1",
"mode": "run",
"runTimeoutSeconds": 300
}
CAID Multi-Agent Coordination Data Schema & Taxonomy
The skill organizes coordination data through several structured formats:
| Component | Description |
|---|---|
| Dependency Graph | A directed acyclic graph (DAG) mapping task order (Ready, Completed, Blocked). |
| Task Assignment JSON | Includes task_id, target_files, verification_command, and restricted_files. |
| Execution History | A compressed state via LLMSummarizingCondenser to manage context window limits. |
| Main Branch | The absolute source of truth containing all integrated and verified commits. |
CAID Multi-Agent Coordination Advanced Features
- Physical Git Isolation: Mandates filesystem-level separation for agents to prevent performance degradation found in soft isolation.
- LLMSummarizingCondenser: Periodically compresses conversation history into structured artifacts to prevent context window explosion.
- Dependency-Aware Scheduling: Prioritizes upstream tasks and enabling tests to maximize evaluation signals early in the process.
- Automatic Conflict Resolution: Assigns merge conflict responsibility to the engineer agent producing the conflicting commit for localized fixing.
SKILL.md
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