Review external pull requests and issues in public GitHub repositories by checking whether they address real user problems. It helps assess proposed improvements, question hypothetical issues, and inform prioritization or replies without treating AI use alone as suspicious.
Skeptical review of outside contributions to public repos. Use when reviewing, ranking, or replying to public GitHub pull requests or issues from outside contributors, or when the user says "larp-detector".
Review the instructions the agent will receive before you install.
When reviewing this code, be careful. A lot of people on public repos just use an AI coding agent to contribute to a codebase, only for the sake of their name being on the contributors list. They also have no taste and no product feel. They're not trying to fix any real issue, any real problem, or any real bug. They literally just want to contribute.
Another common thing they do is use an AI coding agent to analyze the repo for issues, problems, and security vulnerabilities. Even if our code is good, LLMs will hallucinate hypothetical issues that would really never happen.
So be careful when reviewing this. Check that it is actually a good idea, and think about the intention of the creator. What is the intention? Did they actually face a problem and then tell their coding agent to fix it? Everybody uses AI coding agents, so that alone is not a red flag.
Really try to figure out the intention and the origin of this PR, issue, or whatever you are reviewing:
Be careful. A lot of the code you're going to review is complete nonsense. It's created by people who have no product taste, are not technical, and just tell an AI coding agent to contribute something for the sake of contributing something.
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Work through sixteen open questions in four rounds to clarify major product decisions, risky changes, or large refactors. The agent adapts later questions to earlier answers, then turns your decisions into a step-by-step implementation plan.
Document approved decisions as concise ADRs, or architecture decision records. It preserves the user's meaning and conditions, removes conversational filler, follows the project's numbering, and presents the saved record for review.
Keep a Mac awake for a chosen duration or while a specified process runs. The skill uses built-in macOS tools to manage one session, verify that sleep prevention is active, and control whether the display stays on.
Turn a feature or refactoring idea into a concise implementation prompt for another agent. It asks focused questions one at a time, records decisions, and includes development steps, validation, and working rules.
Inspect and manage BB threads, environments, projects, and plugins through its command line interface. The skill guides agents to identify the correct targets, apply changes, and verify the resulting state.
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