Iterate your ideas with PM and DEV subagents

    1

    Implement a rigorous PM-Developer review loop with separated authority and automated quality gates.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    I need to implement a new JWT auth flow. Use a PM and a dev agent to ensure the spec is followed and all security tests pass before finishing.

    Your agent does

    Protocol initialized in .pmdev/jwt-auth-flow/. PM has generated SPEC.md with 5 acceptance criteria. Quality gate detected: npm test, npm run lint. Starting Round 01. Developer is implementing the auth provider while PM awaits the gate log for verification.

    What you get

    Split authority between spec-writing and code-writing to eliminate bias.Automate multi-round iterations until all acceptance criteria are met.Detect and stop loop thrashing when agents disagree on implementation.Verify every code change against a hardware-enforced quality gate log.

    About this skill

    The problem

    Single-agent coding tasks often suffer from "self-review blindness" where the agent decides a task is done simply because it stopped typing. Developers frequently find themselves in a "ping-pong" loop with agents that claim success while ignoring acceptance criteria or moving the goalposts by editing the spec.

    What it does

    • Enforces a strict separation of authority by spawning two subagents: a PM to write the spec and a Developer to write the code.
    • Implements a file-based protocol (SPEC.md, BUILD.md, REVIEW.md) that persists across agent crashes or restarts.
    • Automates quality gates by detecting and running project-specific tests, linters, and build commands.
    • Prevents infinite loops through built-in thrash detection and hard round budgets.
    • Ensures the PM agent has no write access to source code, preventing it from reviewing its own fixes.

    Why this beats prompting it yourself

    Writing "please review your work" in a single prompt rarely works because the agent shares the same context and bias as its mistakes. This skill provides a structured framework with specialized roles, dedicated artifacts, and bash-backed verification that forces objective accountability. It replaces vague conversational feedback with a citation-based rubric that links every code change to a specific acceptance criterion.

    Use cases

    • Executing complex feature requests where self-correction typically fails.
    • Managing long-running refactors that require strict adherence to a multi-point checklist.
    • Automating internal PR reviews before a human ever touches the code.
    • Ensuring legacy code changes pass a rigorous, multi-stage quality gate without manual oversight.

    Known limitations

    This skill requires a repository with executable tests or build signals to populate the quality gate. It consumes 3x to 5x more tokens than a single-agent execution, making it unsuitable for trivial fixes.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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