Audit or rewrite AGENTS.md so it holds only lasting principles. Run it only when the user asks for it by name; never invoke it on your own.
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Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the codebase or domain is unfamiliar, the user will "know it when they see it", a reference implementation must be understood before porting, mid-build deviations from the plan need capturing, or a finished change needs buy-in or verified understanding before merge. Pairs with [write-spec](../write-spec/SKILL.md) — walk the quadrants to burn off fog before slicing, and feed the finished map into the spec.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
The map is not the territory. The prompt, the plan, and the context window are the map; the codebase, the domain, and the user's actual intent are the territory. The gap between them is the unknowns — and an unknown found before code is written costs minutes, while the same unknown found three PRs later costs the three PRs.
This skill is a guided conversation: the quadrant walk. Together with the user you fill in a four-quadrant map of the task, one quadrant per stage, and the user walks away holding the completed map. The map is the deliverable; implementation is a different task that starts only after the map is handed over.
Two moves apply at every stage:
Five stages, walked in order, one at a time. When you enter a stage, read its reference file and follow it. Name the current quadrant as you go — the user should always know where they stand on the map — and finish the stage in front of you before opening the next.
When the user moves on to build, review, or merge what the walk mapped, read after the walk — the map lives on past planning.
Count questions the user has answered across the walk, not turns or answers found in the territory. After about five or six, if their preferences form a clear pattern and questions remain, pause the interview for this checkpoint. If the pattern is still unclear, continue targeted questions and offer once it becomes clear; do not manufacture questions just to reach the count.
Done when the user has chosen whether to delegate and, if opted in, the confirmed preferences and delegation scope are recorded alongside the map's decision ledger.
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Audit or rewrite AGENTS.md so it holds only lasting principles. Run it only when the user asks for it by name; never invoke it on your own.
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Audit the choices an implementing agent made, not its diff — a pure decision audit that traces the session's history into a choices ledger, changes no code, and never blocks an unsupervised run. Working code still embeds architecture the user never chose; surface it because future work inherits it. Use when the user wants to review the decisions the AI made on their behalf, before merging or committing AI-implemented work, when integrating a delegated subagent's pass, or when a fix "works" but might be a point fix.
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Audit and prioritize performance work through bounded-work and forward-progress checks. Use when asked to find performance issues, investigate freezes/500s/OOMs, review retries or polling for no-progress loops, rank a performance backlog, or implement the next simple performance fixes.
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Audit whether tests earn their maintenance cost and which suite owns each contract. Use when pruning redundant tests, investigating implementation coupling or test-only production hooks, reviewing the value of proposed coverage, or auditing an entire subsystem. Use write-tests to implement the resulting test changes.
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Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
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Shoot software UI like a film. A sequence of hero shots, each a steep, oblique, extreme close-up of the interface floating in black, with a razor-thin focal plane, a slow motivated camera drift, and UI that acts on its own, hard-cut on the action, ending on one line of copy and a still logo. Use when asked for a launch video, product film, feature trailer, teaser, promo clip, hero shot, or any "cinematic" footage of an app or UI. This skill covers camera, framing, light, and edit. It is stack-agnostic.
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