Official agent skill

Doc Coauthoring

by aws-samples in aws-samples/sample-strands-agent-with-agentcore

Guide users through a structured workflow for co-authoring documentation.

OfficialMITAuto-check passedSales & Support

Install Doc Coauthoring

skills CLI
$ npx skills add aws-samples/sample-strands-agent-with-agentcore --skill doc-coauthoring -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aws-samples/sample-strands-agent-with-agentcore doc-coauthoring --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aws-samples/sample-strands-agent-with-agentcore.git skills-src && mkdir -p .claude/skills && cp -r skills-src/chatbot-app/agentcore/skills/doc-coauthoring .claude/skills/doc-coauthoring && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
doc-coauthoring
GitHub stars
194
Used in
39 other repos
Token cost
~3.2k tokens
SKILL.md length
1,851 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
MIT

At a glance

Guide users through a structured workflow for co-authoring documentation.

  • Works in 3 steps: Context Gathering → Refinement & Structure → Reader Testing
  • User wants to write documentation
  • SKILL.md covers When to Offer This Workflow, Stage 1: Context Gathering, Stage 2: Refinement & Structure and Stage 3: Reader Testing, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Doc Coauthoring is an agent skill from aws-samples/sample-strands-agent-with-agentcore, published by the product's own GitHub organization. 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.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Sales & Support, covering Proposals and quotes. The repository describes itself as: Reference architecture for agentic AI chatbots with Strands Agents and Amazon Bedrock AgentCore. The licence is MIT.

When your agent uses it

  • User wants to write documentation
  • Technical specs
  • Similar structured content
  • User mentions writing docs

Example prompts

  • “/doc-coauthoring”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Context Gathering
  2. Refinement & Structure
  3. Reader Testing

What it can do on your machine

Read from SKILL.md and the folder at commit 6dd9d13. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Doc Coauthoring loads about 3.2k tokens when it runs. Until then it costs about 111 tokens; SKILL.md has 1,851 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aws-samples/sample-strands-agent-with-agentcore at commit 6dd9d13, republished under its MIT licence (© aws-samples). 1,851 words, ~3,209 tokens.

Download SKILL.mdSave it as .claude/skills/doc-coauthoring/SKILL.md (or your agent's skills folder).
name
doc-coauthoring
description
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.
type
instruction

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 you ask clarifying questions
  2. Refinement & Structure: Iteratively build each section through brainstorming and editing
  3. Reader Testing: Test the doc with a fresh session (no context) to catch blind spots before others read it

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 you know, 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 available tools to fetch it
  • If they provide a file, read it

If user mentions editing an existing shared document:

  • Use available tools to read the current state
  • Check for images without alt-text
  • If images exist without alt-text, explain that AI readers won't be able to interpret them. Ask if they want alt-text generated.
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 tools are available for fetching external content (web search, URL fetcher, etc.), mention that these can be used to pull in context directly. Otherwise, ask the user to paste the relevant content.

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 relevant tools are available: Inform them the content will be read now, then fetch it
    • If no tools available: Ask them to paste the relevant content directly
  • If user mentions entities/projects that are unknown:

    • Ask if available tools should be used to search for more context
    • 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, 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 the user wants a Word document, use the word-documents skill to create a .docx file with section headers and placeholder text. Otherwise, draft the structure directly in the conversation.

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.

Show full SKILL.md (766 more words)Show less
Step 5: Drafting

Draft the section based on the curated points.

If working with a document file, update the relevant section. If working in conversation, present the drafted section clearly.

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:

  • Make targeted edits (never reprint the whole doc unless requested)
  • After each edit, confirm completion
  • 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 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 perspective (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.

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: Fresh-Perspective Review

Approach the document as if reading it for the first time. For each predicted question:

  • Try to answer it using only the document content
  • Note where the document assumes context the reader may not have
  • Identify ambiguous phrasing or missing explanations

Summarize what works well and what falls short for each question.

Step 3: Additional Checks

Review the document for:

  • Ambiguity or unclear phrasing
  • Assumptions about reader knowledge that aren't stated
  • Internal contradictions or inconsistencies
  • Jargon used without definition

Summarize any issues found.

Step 4: Report and Fix

If issues found: Report the specific problems discovered during reader testing.

List the specific issues.

Indicate intention to fix these gaps.

Loop back to refinement for problematic sections.

Exit Condition

When the fresh-perspective review consistently produces correct answers and doesn't surface new gaps or ambiguities, the doc is ready.

Final Review

When Reader Testing passes: Announce the doc has passed reader 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:

  • 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

Document Management:

  • Draft content directly in conversation or via the word-documents skill
  • Make targeted edits rather than reprinting entire sections
  • Never use document creation 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

© aws-samples, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in chatbot-app/agentcore/skills/doc-coauthoring of aws-samples/sample-strands-agent-with-agentcore.

Open the folder on GitHubat commit 6dd9d13

Used in at least 33 other repositories

We found 64 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 39 other GitHub owners. This page covers the copy in aws-samples/sample-strands-agent-with-agentcore, which our catalogue first saw on October 7, 2026.

…and 14 more copies not listed here.

Compare with similar skills

Doc Coauthoring next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Doc Coauthoring compared with similar skills
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Doc Coauthoring this skillaws-samples/sample-strands-agent-with-agentcore19439 repos~3.2kAutomated safety check: PassMIT
Ccsn Find Simplificationskenryu42/cc-safety-net1.6k—~2.9kAutomated safety check: PassMIT
AI Meetingccplugins/awesome-claude-code-plugins967—~2.4kAutomated safety check: NotesApache-2.0
Exploration Modefjrevoredo/mini-diarium308—~3.4kAutomated safety check: PassMIT
Audit Onboarding Proposalhoangnb24/repository-harness1.2k—~4kAutomated safety check: PassMIT
No Negative EchoLB623/no-negative-echo893—~965Automated safety check: PassMIT

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Questions about Doc Coauthoring

What does Doc Coauthoring do?

Guide users through a structured workflow for co-authoring documentation. Doc Coauthoring is an agent skill from aws-samples/sample-strands-agent-with-agentcore, published by the product's own GitHub organization. Guide users through a structured workflow for co-authoring documentation.

When should I use Doc Coauthoring?

Doc Coauthoring fits situations like: user wants to write documentation; technical specs; similar structured content; user mentions writing docs.

How do I install Doc Coauthoring in Claude Code?

Run `npx skills add aws-samples/sample-strands-agent-with-agentcore --skill doc-coauthoring -a claude-code`. Or copy the skill folder (chatbot-app/agentcore/skills/doc-coauthoring in aws-samples/sample-strands-agent-with-agentcore) into .claude/skills/doc-coauthoring in your project. Claude Code loads it when a task matches its description.

How do I install Doc Coauthoring in Codex?

Run `npx skills add aws-samples/sample-strands-agent-with-agentcore --skill doc-coauthoring -a codex`. Or copy the skill folder (chatbot-app/agentcore/skills/doc-coauthoring in aws-samples/sample-strands-agent-with-agentcore) into .agents/skills/doc-coauthoring in your project. Codex loads it when a task matches its description.

Can I use Doc Coauthoring in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws-samples/sample-strands-agent-with-agentcore --skill doc-coauthoring -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/doc-coauthoring, .gemini/skills/doc-coauthoring, .github/skills/doc-coauthoring and .opencode/skills/doc-coauthoring in your project.

What does Doc Coauthoring need to run?

SKILL.md names no scripts, command-line tools or credentials: Doc Coauthoring is instructions for the agent only.

Does Doc Coauthoring access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Doc Coauthoring safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Doc Coauthoring use?

Doc Coauthoring is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Doc Coauthoring use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Doc Coauthoring?

Skills that share tags, products or a category with Doc Coauthoring: Ccsn Find Simplifications (kenryu42/cc-safety-net, 1.6k stars), AI Meeting (ccplugins/awesome-claude-code-plugins, 967 stars), Exploration Mode (fjrevoredo/mini-diarium, 308 stars) and Audit Onboarding Proposal (hoangnb24/repository-harness, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Doc Coauthoring?

aws-samples (a GitHub organization, an official publisher) maintains it in aws-samples/sample-strands-agent-with-agentcore, which has 194 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 6, 2026.

Source: aws-samples/sample-strands-agent-with-agentcore on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.