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Agent Skill · 研究检索

anthropics-doc-coauthoring(Anthropic DOC coauthoring 搜索)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

anthropics-doc-coauthoring 是研究检索类 Agent Skill,可用宿主为 Local Agent,通过unknown获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 70,825。

总安装 206周安装 17下载 140核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:anthropics-doc-coauthoring(Anthropic DOC coauthoring 搜索)
来源仓库:https://smithery.ai
仓库路径:anthropics-doc-coauthoring
安装命令:
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。当前暂无明确安装命令,请以来源页面说明为准。

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

引导协作式文档编写流程,分阶段完成上下文收集、结构优化与读者测试。适用于 PRD、技术方案与决策记录等长文档的共建场景。主动询问用户意图与受众角色,动态调整写作风格与深度层级。所有修改建议以增量方式呈现,保留原文档历史版本可追溯。最终交付物需明确标注假设条件与待决议项,避免过度承诺。

来源仓库内路径:anthropics-doc-coauthoring

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Local Agent;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    没有现成安装命令,按unknown的说明从来源页面获取,仓库路径是 anthropics-doc-coauthoring。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

anthropics-doc-coauthoring 怎么安装?

公开资料没有给出可以直接复制的命令,需要按来源页面说明手动安装,仓库内路径为 anthropics-doc-coauthoring。

anthropics-doc-coauthoring 支持哪些 AI 工具?

公开资料标注支持 Local Agent。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

anthropics-doc-coauthoring 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

anthropics-doc-coauthoring 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

anthropics-doc-coauthoring 用的人多吗?

公开数据是累计安装 206 次,近一周安装 17 次,下载 140 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

anthropics-doc-coauthoring 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 Local Agent 一种宿主,触发方式和使用习惯不用来回切换。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Local Agent,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
anthropics-doc-coauthoring

Anthropic DOC coauthoring 搜索 · 本页

206—调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Doc Co-Authoring Workflow

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.

When to Offer This Workflow

Trigger conditions:

  • User mentions writing documentation: "write a doc", "draft a proposal", "create a spec", "write up"
  • User mentions specific doc types: "PRD", "design doc", "decision doc", "RFC"
  • User seems to be starting a substantial writing task

Initial offer: Offer the user a structured workflow for co-authoring the document. Explain the three stages:

  1. Context Gathering: User provides all relevant context while Claude asks clarifying questions
  2. Refinement & Structure: Iteratively build each section through brainstorming and editing
  3. Reader Testing: Test the doc with a fresh Claude (no context) to catch blind spots before others read it

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.

Stage 1: Context Gathering

Goal: Close the gap between what the user knows and what Claude knows, enabling smart guidance later.

Initial Questions

Start by asking the user for meta-context about the document:

  1. What type of document is this? (e.g., technical spec, decision doc, proposal)
  2. Who's the primary audience?
  3. What's the desired impact when someone reads this?
  4. Is there a template or specific format to follow?
  5. Any other constraints or context to know?

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:

  • Ask if they have a template document to share
  • If they provide a link to a shared document, use the appropriate integration to fetch it
  • If they provide a file, read it

If user mentions editing an existing shared document:

  • Use the appropriate integration to read the current state
  • Check for images without alt-text
  • If images exist without alt-text, explain that when others use Claude to understand the doc, Claude won't be able to see them. Ask if they want alt-text generated. If so, request they paste each image into chat for descriptive alt-text generation.

Info Dumping

Once initial questions are answered, encourage the user to dump all the context they have. Request information such as:

  • Background on the project/problem
  • Related team discussions or shared documents
  • Why alternative solutions aren't being used
  • Organizational context (team dynamics, past incidents, politics)
  • Timeline pressures or constraints
  • Technical architecture or dependencies
  • Stakeholder concerns

Advise them not to worry about organizing it - just get it all out. Offer multiple ways to provide context:

  • Info dump stream-of-consciousness
  • Point to team channels or threads to read
  • Link to shared documents

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 integrations available: Inform them the content will be read now, then use the appropriate integration - If integrations not available: Explain lack of access. Suggest they enable connectors in Claude settings, or paste the relevant content directly.

  • If user mentions entities/projects that are unknown:

- Ask if connected tools should be searched to learn more - Wait for user confirmation before searching

  • 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.

Stage 2: Refinement & Structure

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:

  1. Clarifying questions will be asked about what to include
  2. 5-20 options will be brainstormed
  3. User will indicate what to keep/remove/combine
  4. The section will be drafted
  5. It will be refined through surgical edits

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:

Step 1: Clarifying Questions

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.

Step 2: Brainstorming

For the [SECTION NAME] section, brainstorm [5-20] things that might be included, depending on the section's complexity. Look for:

  • Context shared that might have been forgotten
  • Angles or considerations not yet mentioned

Generate 5-20 numbered options based on section complexity. At the end, offer to brainstorm more if they want additional options.

Step 3: Curation

Ask which points should be kept, removed, or combined. Request brief justifications to help learn priorities for the next sections.

Provide examples:

  • "Keep 1,4,7,9"
  • "Remove 3 (duplicates 1)"
  • "Remove 6 (audience already knows this)"
  • "Combine 11 and 12"

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.

Step 4: Gap Check

Based on what they've selected, ask if there's anything important missing for the [SECTION NAME] section.

Step 5: Drafting

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".

Step 6: Iterative Refinement

As user provides feedback:

  • Use str_replace to make edits (never reprint the whole doc)
  • If using artifacts: Provide link to artifact after each edit
  • If using files: Just confirm edits are complete
  • If user edits doc directly and asks to read it: mentally note the changes they made and keep them in mind for future sections (this shows their preferences)

Continue iterating until user is satisfied with the section.

Quality Checking

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.

Near Completion

As approaching completion (80%+ of sections done), announce intention to re-read the entire document and check for:

  • Flow and consistency across sections
  • Redundancy or contradictions
  • Anything that feels like "slop" or generic filler
  • Whether every sentence carries weight

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.

Stage 3: Reader Testing

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.

Testing Approach

If access to sub-agents is available (e.g., in Claude Code):

Perform the testing directly without user involvement.

Step 1: Predict Reader Questions

Announce intention to predict what questions readers might ask when trying to discover this document.

Generate 5-10 questions that readers would realistically ask.

Step 2: Test with Sub-Agent

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.

Step 3: Run Additional Checks

Announce additional checks will be performed.

Invoke sub-agent to check for ambiguity, false assumptions, contradictions.

Summarize any issues found.

Step 4: Report and Fix

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.

Step 1: Predict Reader Questions

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.

Step 2: Setup Testing

Provide testing instructions:

  1. Open a fresh Claude conversation: https://claude.ai
  2. Paste or share the document content (if using a shared doc platform with connectors enabled, provide the link)
  3. Ask Reader Claude the generated questions

For each question, instruct Reader Claude to provide:

  • The answer
  • Whether anything was ambiguous or unclear
  • What knowledge/context the doc assumes is already known

Check if Reader Claude gives correct answers or misinterprets anything.

Step 3: Additional Checks

Also ask Reader Claude:

  • "What in this doc might be ambiguous or unclear to readers?"
  • "What knowledge or context does this doc assume readers already have?"
  • "Are there any internal contradictions or inconsistencies?"

Step 4: Iterate Based on Results

Ask what Reader Claude got wrong or struggled with. Indicate intention to fix those gaps.

Loop back to refinement for any problematic sections.


Exit Condition (Both Approaches)

When Reader Claude consistently answers questions correctly and doesn't surface new gaps or ambiguities, the doc is ready.

Final Review

When Reader Testing passes: Announce the doc has passed Reader Claude testing. Before completion:

  1. Recommend they do a final read-through themselves - they own this document and are responsible for its quality
  2. Suggest double-checking any facts, links, or technical details
  3. Ask them to verify it achieves the impact they wanted

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:

  • Consider linking this conversation in an appendix so readers can see how the doc was developed
  • Use appendices to provide depth without bloating the main doc
  • Update the doc as feedback is received from real readers

Tips for Effective Guidance

Tone:

  • Be direct and procedural
  • Explain rationale briefly when it affects user behavior
  • Don't try to "sell" the approach - just execute it

Handling Deviations:

  • If user wants to skip a stage: Ask if they want to skip this and write freeform
  • If user seems frustrated: Acknowledge this is taking longer than expected. Suggest ways to move faster
  • Always give user agency to adjust the process

Context Management:

  • Throughout, if context is missing on something mentioned, proactively ask
  • Don't let gaps accumulate - address them as they come up

Artifact Management:

  • Use create_file for drafting full sections
  • Use str_replace for all edits
  • Provide artifact link after every change
  • Never use artifacts for brainstorming lists - that's just conversation

Quality over Speed:

  • Don't rush through stages
  • Each iteration should make meaningful improvements
  • The goal is a document that actually works for readers

平台分布

Local Agent

79.32%
按下载量换算111

安全审计

Socket

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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