doc-coauthoring
Skill2 starsChanged 13 days agoMITView on GitHub
Co-author a structured document with the user — use when they want to write or rework a spec, proposal, design doc, decision doc, RFC, PRD, or similar.
179 other projects use a version of this.
Adopted by
Most recently taken up by
- TehseenTech/manus-skillstheir own version, first seen Nov 8, 2026
- rvdbreemen/OTGW-firmwaretheir own version, first seen Nov 8, 2026
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- fumoctl/nix-agent-officetheir own version, first seen Nov 8, 2026
- ppswdev/pp_skillstheir own version, first seen Nov 8, 2026
- shkodnik1917/claude-brainlabtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use context7-efficient, academy-guide and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
hfzarslanSkillcontext7-efficient
Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.
Also in 5 projects that use this
AnthropicSkillacademy-guide
Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
Also in 6 projects that use this
AbdullahMalik17Skillconfiguring-dapr-pubsub
Configures Dapr pub/sub components for event-driven microservices with Kafka or Redis. Use when wiring agent-to-agent communication, setting up event subscriptions, or integrating Dapr sidecars. Covers component configuration, subscription patterns, publishing events, and Kubernetes deployment. NOT when using direct Kafka clients or non-Dapr messaging patterns.
Also in 3 projects that use this
AbdullahMalik17Skilldeploying-cloud-k8s
Deploys applications to cloud Kubernetes (AKS/GKE/DOKS) with CI/CD pipelines. Use when deploying to production, setting up GitHub Actions, troubleshooting deployments. Covers build-time vs runtime vars, architecture matching, and battle-tested debugging.
Also in 3 projects that use this
Galaxy-DawnSkillresults-report
This skill should be used when the user asks to "write an experiment report", "summarize experimental results", "do experiment retrospection", "write a results report", "写实验总结报告", "写实验复盘", or mentions turning completed experiment artifacts into a structured, decision-oriented research report. It assumes strict analysis should come from `results-analysis` first.
Also in 3 projects that use this
AnthropicSkillweb-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
Also in 72 projects that use this
- +5 more from AbdullahMalik17
Other versions179 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 179
- anthropics/skills★ 189ksame name
- Galaxy-Dawn/claude-scholar★ 5.9ksame name
- moonrepo/moon★ 4.2ksame name
- ChenglinPoly/infiAgent★ 1.2ksame name
- polyuiislab/infiAgent★ 1.2ksame name
- getsentry/skills★ 1.1ksame name
- Ohh-889/skyroc-admin★ 795same name
- JetBrains/skills★ 386same name
- zephyrwang6/myskill★ 339same name
- xuzeyu91/WebCode★ 278same name
169 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- AGENTS.mdAGENTS.md
- browser-workflowsSkill
- evolveSkill
- frontend-designSkill
- office-documentsSkill
- skill-creatorSkill
The file
We split this skill into the rules it gives an agent
8 rules. 0 are common, 6 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 170 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 19
Ask the meta questions in one agent_ask_user call or one message: document type, audience, the impact it should have on a reader, template …
- Very specific: line 33
Skeleton first: Create the file now with write_file — every agreed heading plus a [To be written] placeholder, nothing more.
- Distinctive: line 36
ask 5–10 questions about what belongs in it.
- Distinctive: line 39
ask what's still missing, then draft the section into the file, replacing its placeholder.
- Distinctive: line 42
Ask the user to tell you what to change rather than editing the file themselves — their corrections teach you their style for the next …
- Distinctive: line 43
After three rounds on a section with no substantial change, ask what can be cut without losing information.
- Distinctive: line 51
Write 5–10 questions the audience would realistically bring to this document.
- Distinctive: line 53
Run one more sub-agent over the whole document for ambiguity, unstated assumptions, and internal contradictions.
“Common” is measured: kept by at least 6 in 10 of the 170 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Doc Co-Authoring
You are the user's editor: they own the content and the decisions; you pull context out of them, shape it into sections, and prove the document works for a reader who wasn't in the room.
The workflow runs in three stages, in order, and each stage's Done when gates the next. Offer it first; if the user prefers to write freeform, help freeform. A short or simple document still runs all three stages — the ceremony scales down, the order doesn't.
The working draft is a Markdown file in the workspace (e.g. design-doc.md), written with write_file. When the user wants a Word or PDF deliverable at the end, load the office-documents skill and produce it from the finished Markdown.
Stage 1 — Context
Ask the meta questions in one agent_ask_user call or one message: document type, audience, the impact it should have on a reader, template or required format, constraints. Invite an unorganized dump of everything else — background, alternatives rejected and why, stakeholders, deadlines, architecture.
Pull context from where it already lives:
- files the user attached or names →
read_file; - connected sources (chat, drive, trackers) →
capabilities_searchfor a matching tool, then use it after telling the user what you'll read.
Then ask 5–10 numbered clarifying questions aimed at the gaps. The user may answer in shorthand ("1: yes, 3: see the RFC").
Done when your questions are about trade-offs and edge cases rather than basics, and the user says they have nothing more to add.
Stage 2 — Build, section by section
- Structure. Propose 3–5 sections suited to the doc type (or follow the template) and confirm with the user.
- Skeleton first. Create the file now with
write_file— every agreed heading plus a[To be written]placeholder, nothing more. The file never jumps ahead of the conversation: real prose lands only for a section the user has already curated, one section at a time. - Pick the section with the most unknowns first — usually the core proposal or technical approach; the summary comes last.
For each section, in order:
- ask 5–10 questions about what belongs in it;
- brainstorm 5–20 numbered candidate points, including context the user mentioned and may have forgotten;
- let the user curate ("keep 1,4,7; drop 3; merge 11+12") — parse freeform feedback the same way;
- ask what's still missing, then draft the section into the file, replacing its placeholder;
- refine from the user's feedback until they approve that section.
Ask the user to tell you what to change rather than editing the file themselves — their corrections teach you their style for the next sections. When they do edit directly, re-read the file and carry their changes forward.
- After three rounds on a section with no substantial change, ask what can be cut without losing information.
Done when the file exists with every agreed heading, every section is written and approved, and a full re-read finds no contradictions, repetition, or filler sentences — report what you changed in that pass.
Stage 3 — Reader test
A document works when a reader with zero context can answer the questions its audience will ask.
- Write 5–10 questions the audience would realistically bring to this document.
- For each question, call
run_subagentwith a task containing only the document path and the question, andcapabilities: ["read_file"]. Ask it to answer, flag anything ambiguous, and list the knowledge the document assumed. Issue the calls in one turn so they run in parallel. - Run one more sub-agent over the whole document for ambiguity, unstated assumptions, and internal contradictions.
- Report to the user what the readers got right and wrong. Fix the sections behind every wrong answer (back to Stage 2 for that section), then re-test those questions.
Done when every predicted question has been put to a fresh sub-agent (one run_subagent call per question), the contradiction pass has run, every answer came back correct, and the last round surfaced no new gaps. Reading the document yourself is not a substitute — the point is that a reader with zero context does it.
Finish
Tell the user the document passed the reader test and where the file is. Recommend they read it once end-to-end themselves and verify facts, links, and numbers — they sign their name to it. Offer the Word/PDF export if they need one.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
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Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

