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cabernectcabernect/caberos/skills/doc-coauthoring/SKILL.md

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.

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Most recently taken up by

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.
  • hfzarslanSkill

    context7-efficient

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    Also in 5 projects that use this

  • AnthropicSkill

    academy-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

  • AbdullahMalik17Skill

    configuring-dapr-pubsub

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    Also in 3 projects that use this

  • AbdullahMalik17Skill

    deploying-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-DawnSkill

    results-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

  • AnthropicSkill

    web-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

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
  1. Very specific:

    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 …

    line 19
  2. Very specific:

    Skeleton first: Create the file now with write_file — every agreed heading plus a [To be written] placeholder, nothing more.

    line 33
  3. Distinctive:

    ask 5–10 questions about what belongs in it.

    line 36
  4. Distinctive:

    ask what's still missing, then draft the section into the file, replacing its placeholder.

    line 39
  5. Distinctive:

    Ask the user to tell you what to change rather than editing the file themselves — their corrections teach you their style for the next …

    line 42
  6. Distinctive:

    After three rounds on a section with no substantial change, ask what can be cut without losing information.

    line 43
  7. Distinctive:

    Write 5–10 questions the audience would realistically bring to this document.

    line 51
  8. Distinctive:

    Run one more sub-agent over the whole document for ambiguity, unstated assumptions, and internal contradictions.

    line 53

“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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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_search for 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

  1. Structure. Propose 3–5 sections suited to the doc type (or follow the template) and confirm with the user.
  2. 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.
  3. Pick the section with the most unknowns first — usually the core proposal or technical approach; the summary comes last.
  4. 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.

  5. 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.

  1. Write 5–10 questions the audience would realistically bring to this document.
  2. For each question, call run_subagent with a task containing only the document path and the question, and capabilities: ["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.
  3. Run one more sub-agent over the whole document for ambiguity, unstated assumptions, and internal contradictions.
  4. 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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