parallel-agents

What This Skill Does

The parallel-agents skill is a high-performance orchestration tool for OpenClaw designed to maximize throughput by dispatching multiple subagents to work on unrelated tasks simultaneously. Instead of forcing a single agent to context-switch between disparate issues, this skill allows you to parallelize workload based on domain independence. By splitting tasks that do not share state or sequential dependencies, you can effectively cut development and debugging time by up to 70% in ideal scenarios. This skill serves as a task manager that assigns individual, self-contained units of work to separate execution environments, ensuring that one complex problem doesn't stall the resolution of others.

Installation

To integrate this skill into your environment, run the following command within your Clawbot terminal:

clawhub install openclaw/skills/skills/kjaylee/kj-parallel-agents

Ensure you have the latest version of ClawKit installed to support the sessions_spawn orchestration logic required by this skill.

Use Cases

• Multi-test failure resolution: When multiple tests in different modules fail due to distinct causes.
• Distributed system debugging: When various subsystems show errors that do not impact one another.
• Simultaneous maintenance: Conducting research, refactoring, and documentation updates across non-overlapping project areas.
• Batch asset generation: Creating multiple independent assets like level data, documentation stubs, and configuration files at once.

Example Prompts

1. "Parallel dispatch: Fix the memory leak in auth.service.ts using agent A and simultaneously optimize the SQL query in stats.repository.ts using agent B."
2. "I have three unrelated failing tests in the UI suite. Spawn subagents for each, ensuring they provide a root cause summary and a git-formatted patch for each file."
3. "Run parallel agents to generate documentation for the API endpoints and concurrently run the linting suite, keeping the results in separate summaries."

Tips & Limitations

To get the best results, always ensure that tasks are truly domain-isolated. If two agents attempt to edit the same file, the risk of merge conflicts and state corruption is high. Always perform a final integration check after the subagents complete their work to ensure global consistency. Avoid using this for exploratory debugging where the scope of the problem is unknown; parallelizing guesswork often leads to conflicting changes and wasted compute resources. Always provide explicit output formats (e.g., 'Summary + Diff') to ensure the subagent results are easy to merge into your main branch.