Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A development methodology that executes implementation plans by dispatching fresh subagents per task, each followed by two sequential review stages: spec compliance and code quality. Each task gets an isolated agent context to prevent cross-task contamination. Designed as an enhancement to the ralph-loop workflow.
Using one reviewer for both spec compliance and code quality tends to conflate the two concerns, causing both to be checked superficially — splitting them into sequential gates catches more issues.
When to use it
- Breaking a multi-task feature plan into isolated agent executions
- Catching spec drift before merging a completed task
- Separating "did we build the right thing" from "did we build it well" reviews
- Running TDD cycles through subagents with commit-per-task discipline
- Handling ambiguous requirements by letting subagents ask clarifying questions mid-task
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Execute task 3 from the implementation plan: add user authentication middleware
- 1Extract task 3 spec and relevant context (files, architecture constraints) from the plan
- 2Dispatch implementation subagent with task spec, context, TDD constraint, and self-review requirement
- 3Implementation subagent writes tests first, implements, commits, and returns summary with self-review findings
- 4Dispatch spec-review subagent with original requirements and git diff to verify all requirements are met and no extras added
- 5Dispatch code-quality subagent to check naming, duplication, error handling, and edge cases — loop back to implementer if issues found
Task 3 marked complete with two review gates passed, commit made, ready for next task