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Hand work to subagents and get back results you can actually act on. A subagent that fails will often produce a plausible answer anyway - URLs that 404, numbers nobody checked, all reading as confidently as a correct report. This covers what delegates well and what quietly does not, the five things a brief needs before the output is usable, why asking for the dead ends tells you how carefully the work was done, and what to re-verify by hand before acting.
This Skill is built on practical expertise in long-running AI workflow control, constraint tracking, handoff reliability, completion evidence, and instruction-change recovery. It helps agents keep the user's first, middle, and latest requirements aligned across multi-step work so older assumptions do not override newer directions and completion is backed by observable evidence.
一个问题，Claude、Gemini、ChatGPT 同时给你三个独立答案——然后展开结构化辩论，最终给出一个真正经得起推敲的建议。不是一家之言，而是三种视角的正面交锋。适合那些一个人说了不算的时刻：选技术架构、给策略挑毛病、听点大实话而不是场面话，或者拖了很久还没想通的决定。
一个会挑毛病的导演。你给它一场戏，它先判断这场戏要观众做什么，再决定镜头怎么动 —— 包括告诉你「不该」怎么动。 大多数 AI 视频的问题不在提示词写得不够漂亮，而在镜头本身没想清楚：为什么要动、动给谁看、动完观众该有什么变化。这三个问题不清楚，参数写得再全也是装饰。这个技能先把这三件事问清楚，再落到数值上。
Review AI agents, automation workflows, and project architectures for design gaps, risks, missing requirements, and improvement opportunities.