Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Redesign intake: goal statement (plain English) replaces checklist. Judge evaluates holistically against goal, not a spec sheet. User reviews a paragraph, not 10 bullet points.
What This Skill Does
Checkmate converts a task description into explicit pass/fail criteria, spawns a worker agent to attempt the task, runs a judge agent against the criteria, and loops with accumulated feedback until every criterion passes. An interactive mode lets you review criteria and approve each checkpoint; batch mode runs fully autonomously.
Unlike a single-shot agent run, it locks criteria upfront and uses a separate judge role each iteration, so the definition of done cannot drift and partial results cannot slip through.
When to use it
- Running a code generation task until all tests pass
- Producing a research report that must cover specific required topics
- Drafting a document that must meet a defined quality checklist
- Iterating on a data transformation script until output matches a spec
- Automating QA loops that would otherwise require manual review cycles
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: checkmate: Write a Python function that validates email addresses and passes all edge cases in the provided test suite
- 1Resolve session UUID and create a timestamped workspace directory
- 2Run intake loop to draft pass/fail criteria from the task description and present them for user approval
- 3On approval, spawn a worker agent session that attempts the task and writes output to iter-01/output.md
- 4Spawn a judge agent session that evaluates the output against locked criteria and writes a verdict
- 5On FAIL, extract gaps, present checkpoint to user, then loop with accumulated feedback until judge returns PASS
final-output.md containing the validated function, with all criteria marked passed in the final verdict