根据学习 Claude 用法或团队引入需求,查看 Claude Academy 的课程、教程和应用实例,提供符合学习目标的资料。
- 寻找 Claude 入门资料
- 学习 Claude 的特定功能
- 为团队引入 Claude 选择教材
通过对话完成提案、规格文档和决策记录,从梳理背景、逐节起草到读者验证。让不了解写作过程的Claude阅读,找出解释不足和含糊之处。
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
通过与智能体对话,编写提案、技术规格文档和决策记录。流程分为背景收集、结构与内容推敲、读者验证三个阶段。先确认目的和目标读者,再为各节提出候选内容,由你选择后反复起草和修改,同时检查全文的重复与矛盾。
适合已有材料但尚未确定结构,或需要向相关人员说明提案理由和决策依据时使用。也能找出只有作者知道的前提,检查读者能否仅凭文档理解内容。
读者验证使用不了解写作对话的另一个Claude。支持子智能体的环境可直接验证;否则,由使用者在新对话中提供文档和问题。能否从共享文档和聊天中获取信息,取决于可用的集成功能。完成后,建议使用者自行通读,并核对事实、链接和技术信息。
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。
This skill provides a structured workflow for guiding users through collaborative document creation. Act as an active guide, walking users through three stages: Context Gathering, Refinement & Structure, and Reader Testing.
Trigger conditions:
Initial offer: Offer the user a structured workflow for co-authoring the document. Explain the three stages:
Explain that this approach helps ensure the doc works well when others read it (including when they paste it into Claude). Ask if they want to try this workflow or prefer to work freeform.
If user declines, work freeform. If user accepts, proceed to Stage 1.
Goal: Close the gap between what the user knows and what Claude knows, enabling smart guidance later.
Start by asking the user for meta-context about the document:
Inform them they can answer in shorthand or dump information however works best for them.
If user provides a template or mentions a doc type:
If user mentions editing an existing shared document:
Once initial questions are answered, encourage the user to dump all the context they have. Request information such as:
Advise them not to worry about organizing it - just get it all out. Offer multiple ways to provide context:
If integrations are available (e.g., Slack, Teams, Google Drive, SharePoint, or other MCP servers), mention that these can be used to pull in context directly.
If no integrations are detected and in Claude.ai or Claude app: Suggest they can enable connectors in their Claude settings to allow pulling context from messaging apps and document storage directly.
Inform them clarifying questions will be asked once they've done their initial dump.
During context gathering:
If user mentions team channels or shared documents:
If user mentions entities/projects that are unknown:
As user provides context, track what's being learned and what's still unclear
Asking clarifying questions:
When user signals they've done their initial dump (or after substantial context provided), ask clarifying questions to ensure understanding:
Generate 5-10 numbered questions based on gaps in the context.
Inform them they can use shorthand to answer (e.g., "1: yes, 2: see #channel, 3: no because backwards compat"), link to more docs, point to channels to read, or just keep info-dumping. Whatever's most efficient for them.
Exit condition: Sufficient context has been gathered when questions show understanding - when edge cases and trade-offs can be asked about without needing basics explained.
Transition: Ask if there's any more context they want to provide at this stage, or if it's time to move on to drafting the document.
If user wants to add more, let them. When ready, proceed to Stage 2.
Goal: Build the document section by section through brainstorming, curation, and iterative refinement.
Instructions to user: Explain that the document will be built section by section. For each section:
Start with whichever section has the most unknowns (usually the core decision/proposal), then work through the rest.
Section ordering:
If the document structure is clear: Ask which section they'd like to start with.
Suggest starting with whichever section has the most unknowns. For decision docs, that's usually the core proposal. For specs, it's typically the technical approach. Summary sections are best left for last.
If user doesn't know what sections they need: Based on the type of document and template, suggest 3-5 sections appropriate for the doc type.
Ask if this structure works, or if they want to adjust it.
Once structure is agreed:
Create the initial document structure with placeholder text for all sections.
If access to artifacts is available:
Use create_file to create an artifact. This gives both Claude and the user a scaffold to work from.
Inform them that the initial structure with placeholders for all sections will be created.
Create artifact with all section headers and brief placeholder text like "[To be written]" or "[Content here]".
Provide the scaffold link and indicate it's time to fill in each section.
If no access to artifacts:
Create a markdown file in the working directory. Name it appropriately (e.g., decision-doc.md, technical-spec.md).
Inform them that the initial structure with placeholders for all sections will be created.
Create file with all section headers and placeholder text.
Confirm the filename has been created and indicate it's time to fill in each section.
For each section:
Announce work will begin on the [SECTION NAME] section. Ask 5-10 clarifying questions about what should be included:
Generate 5-10 specific questions based on context and section purpose.
Inform them they can answer in shorthand or just indicate what's important to cover.
For the [SECTION NAME] section, brainstorm [5-20] things that might be included, depending on the section's complexity. Look for:
Generate 5-20 numbered options based on section complexity. At the end, offer to brainstorm more if they want additional options.
Ask which points should be kept, removed, or combined. Request brief justifications to help learn priorities for the next sections.
Provide examples:
If user gives freeform feedback (e.g., "looks good" or "I like most of it but...") instead of numbered selections, extract their preferences and proceed. Parse what they want kept/removed/changed and apply it.
Based on what they've selected, ask if there's anything important missing for the [SECTION NAME] section.
Use str_replace to replace the placeholder text for this section with the actual drafted content.
Announce the [SECTION NAME] section will be drafted now based on what they've selected.
If using artifacts: After drafting, provide a link to the artifact.
Ask them to read through it and indicate what to change. Note that being specific helps learning for the next sections.
If using a file (no artifacts): After drafting, confirm completion.
Inform them the [SECTION NAME] section has been drafted in [filename]. Ask them to read through it and indicate what to change. Note that being specific helps learning for the next sections.
Key instruction for user (include when drafting the first section): Provide a note: Instead of editing the doc directly, ask them to indicate what to change. This helps learning of their style for future sections. For example: "Remove the X bullet - already covered by Y" or "Make the third paragraph more concise".
As user provides feedback:
str_replace to make edits (never reprint the whole doc)Continue iterating until user is satisfied with the section.
After 3 consecutive iterations with no substantial changes, ask if anything can be removed without losing important information.
When section is done, confirm [SECTION NAME] is complete. Ask if ready to move to the next section.
Repeat for all sections.
As approaching completion (80%+ of sections done), announce intention to re-read the entire document and check for:
Read entire document and provide feedback.
When all sections are drafted and refined: Announce all sections are drafted. Indicate intention to review the complete document one more time.
Review for overall coherence, flow, completeness.
Provide any final suggestions.
Ask if ready to move to Reader Testing, or if they want to refine anything else.
Goal: Test the document with a fresh Claude (no context bleed) to verify it works for readers.
Instructions to user: Explain that testing will now occur to see if the document actually works for readers. This catches blind spots - things that make sense to the authors but might confuse others.
If access to sub-agents is available (e.g., in Claude Code):
Perform the testing directly without user involvement.
Announce intention to predict what questions readers might ask when trying to discover this document.
Generate 5-10 questions that readers would realistically ask.
Announce that these questions will be tested with a fresh Claude instance (no context from this conversation).
For each question, invoke a sub-agent with just the document content and the question.
Summarize what Reader Claude got right/wrong for each question.
Announce additional checks will be performed.
Invoke sub-agent to check for ambiguity, false assumptions, contradictions.
Summarize any issues found.
If issues found: Report that Reader Claude struggled with specific issues.
List the specific issues.
Indicate intention to fix these gaps.
Loop back to refinement for problematic sections.
If no access to sub-agents (e.g., claude.ai web interface):
The user will need to do the testing manually.
Ask what questions people might ask when trying to discover this document. What would they type into Claude.ai?
Generate 5-10 questions that readers would realistically ask.
Provide testing instructions:
For each question, instruct Reader Claude to provide:
Check if Reader Claude gives correct answers or misinterprets anything.
Also ask Reader Claude:
Ask what Reader Claude got wrong or struggled with. Indicate intention to fix those gaps.
Loop back to refinement for any problematic sections.
When Reader Claude consistently answers questions correctly and doesn't surface new gaps or ambiguities, the doc is ready.
When Reader Testing passes: Announce the doc has passed Reader Claude testing. Before completion:
Ask if they want one more review, or if the work is done.
If user wants final review, provide it. Otherwise: Announce document completion. Provide a few final tips:
Tone:
Handling Deviations:
Context Management:
Artifact Management:
create_file for drafting full sectionsstr_replace for all editsQuality over Speed:
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
根据学习 Claude 用法或团队引入需求,查看 Claude Academy 的课程、教程和应用实例,提供符合学习目标的资料。
使用p5.js,通过粒子运动和数学规则创作原创生成艺术。作品配有交互界面,可调整数值、比较变化,使用种子复现相同条件下的画面,并保存为PNG。
将Anthropic的品牌配色和字体应用于制作内容,统一标题与正文的字体,根据背景调整文字颜色,并为图形设置一致的强调色。
从配色、形状、留白和构图等设计方向入手,制作原创海报与静态艺术作品,以少量文字表达主题,输出 PNG 或 PDF,并附上设计理念文档。
依据按编程语言分类的资料,协助开发和维护Claude应用。涵盖官方SDK调用、工具集成、模型迁移、费用检查、提示词审查和评估流程。
在AI的建议、计划、估算或分析后附上两三个简短问题,帮助用户在行动前检查数字、逻辑与缺失信息。问题紧扣回答中的具体内容,可直接向代理追问。