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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondoc-coauthoringExecute the skills CLI command in your project's root directory to begin installation:
Fetches doc-coauthoring from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate doc-coauthoring. Access via /doc-coauthoring in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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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:
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
davila7/claude-code-templates
Solid pick for teams standardizing on skills: doc-coauthoring is focused, and the summary matches what you get after install.
doc-coauthoring is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: doc-coauthoring is the kind of skill you can hand to a new teammate without a long onboarding doc.
doc-coauthoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
doc-coauthoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for doc-coauthoring matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in doc-coauthoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added doc-coauthoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in doc-coauthoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
doc-coauthoring has been reliable in day-to-day use. Documentation quality is above average for community skills.
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