Doc Co-Authoring Workflow workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/doc-coauthoring from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses EXTERNAL_SOURCE.json plus ORIGIN.md as the provenance anchor for review.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Stage 1: Context Gathering, Stage 2: Refinement & Structure, Stage 3: Reader Testing, Final Review, Tips for Effective Guidance, Limitations.
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | EXTERNAL_SOURCE.json | Confirms repository, branch, commit, and imported path before touching the copied workflow |
| Provenance review | ORIGIN.md | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | SKILL.md | Starts with the smallest copied file that materially changes execution |
| Supporting context | SKILL.md | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills | Helps the operator switch to a stronger native skill when the task drifts |
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
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.
Use @doc-coauthoring to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Review @doc-coauthoring against EXTERNAL_SOURCE.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Use @doc-coauthoring for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Review @doc-coauthoring using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/doc-coauthoring, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open EXTERNAL_SOURCE.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
@devops-deploy - Use when the work is better handled by that native specialization after this imported skill establishes context.@devops-troubleshooter - Use when the work is better handled by that native specialization after this imported skill establishes context.@differential-review - Use when the work is better handled by that native specialization after this imported skill establishes context.@discord-automation - Use when the work is better handled by that native specialization after this imported skill establishes context.Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
references | copied reference notes, guides, or background material from upstream | references/n/a |
examples | worked examples or reusable prompts copied from upstream | examples/n/a |
scripts | upstream helper scripts that change execution or validation | scripts/n/a |
agents | routing or delegation notes that are genuinely part of the imported package | agents/n/a |
assets | supporting assets or schemas copied from the source package | assets/n/a |
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: