将大型调研或生成任务分配给多个AI模型,完成计划评审、并行执行、成果验证与整合,并记录各负责角色的进度和重试情况。
- 并行调研多个竞争对手
- 分担大型内容生成任务
- 让顾问评审计划或草稿
将语音输入或长篇随想整理为目标、决定和待解问题,同时保留中途改变的方向与暂缓话题。单独列出智能体的推测,在开始工作前确认理解。
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's inferences and guesses quarantined away from the user's own phrasing, so the user verifies what the model believes, not just what it doubts. Use when the user says "let me think out loud" or wants to ramble a bit before building anything, when a message opens with a speech-to-text preamble like "switching to voice, sorry for typos", when input is a long weakly punctuated stream with restarts and mid-message reversals ("actually no, wait, scrap that idea entirely"), or when the user asks to be interviewed about a fuzzy half-formed idea. Includes an optional capture mode for rambles spread across several messages and an optional targeted interview.
thinking-out-loud 将语音输入或想到哪里说到哪里的长篇说明,整理成开始工作前可核对的简短摘要。摘要分别列出目标、已定事项与限制、待解问题、中途改变的方向,以及暂缓讨论的话题。智能体补充的解释和推测会单独列出,便于检查是否混入了自己没有说过的前提。在用户认可摘要之前,不会开始实现或编写解决方案。
适合在构想尚未成形时边说边想,或确认智能体如何理解一段长篇说明。想法分散在多条消息中时,也可以等用户发出结束信号后再统一整理。根据用户意愿,还能针对未解决的问题和推测逐一提问,最多五问。
不适用于逐字转写、会议记录或简短明确的请求。保存摘要需要用户确认,可选择追加到 CLAUDE.md、保存为带日期的文件,或仅保留在对话中。许可证为 Apache-2.0。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月20日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
A ten minute voice ramble transfers more context than any prompt a person would type, and models reconstruct rambles well. The failure is downstream and invisible: the model fills every gap in the ramble confidently. "The usual model" silently becomes a specific model. "The standard size" becomes a specific viewport. A position the user reversed mid-ramble survives as fact. None of this registers as uncertainty from the inside, so none of it ever becomes a clarifying question. The model then acts on a misreading it fully believes, and the user discovers it an hour of generated work later.
This skill is the fix: before acting on any ramble, produce an echo, a short structured audit of everything absorbed, with the model's own additions quarantined from the user's words. The user corrects three lines instead of debugging a built artifact.
Asking clarifying questions is good, and the interview below does it. But questions alone cannot secure a ramble, for two structural reasons:
One structured reply. Dense, scannable, and short: the user should find and fix an error in seconds. Full template with a worked example in references/echo-format.md.
Compression rules, non-negotiable:
Close by inviting corrections and offering the interview.
Follow-up questions have their place: after the audit, not instead of it. Only if the user accepts the offer, or asked to be interviewed up front.
Not needed for dictation tools, where the whole ramble arrives as one message. Use it when the user invokes the skill before rambling and then adds thoughts across several messages, possibly over a long stretch.
After the user approves the echo, offer exactly three options:
docs/rambles/YYYY-MM-DD-<topic>.mdThe approved brief then governs the rest of the session: honor its decisions and constraints without re-asking.
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将大型调研或生成任务分配给多个AI模型,完成计划评审、并行执行、成果验证与整合,并记录各负责角色的进度和重试情况。
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检查Python和JavaScript依赖文件中的版本约束缺失、重复条目、固定版本冲突及过时兼容包,用易懂的语言说明影响并提出便于核对的修改建议。
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