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Writing User Docs

OrganizationPopular
callstackincubator
writing-user-docs

House style for user-facing documentation — voice, scope, structure, and what to leave out. Use this skill whenever writing, editing, or reviewing anything a user reads to learn how to use a tool — help center articles, getting-started guides, tutorials, feature docs, README usage sections, in-app help, release notes, or FAQ entries. Trigger it even when the request is phrased plainly — "document this feature", "write docs for X", "explain this to users", "write a README for this library", "turn these notes into a guide" — and even when no style guidelines are mentioned. Also use it to review existing docs for tone, bloat, or leaked implementation detail. Do not use it for internal engineering docs, architecture write-ups, RFCs, or code comments.

Overview

Publishercallstackincubator
Repositoryagent-skills
Skill namewriting-user-docs
Stars
1.6K
Forks
116
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by callstackincubator on GitHub. Read the source before you install it.

Installation

Install the Writing User Docs AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/callstackincubator/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/writing-user-docs .claude/skills/writing-user-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Writing User Docs in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Writing User Docs on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Writing User Docs is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

User-facing documentation

Write for the person using the tool, not the person who built it.

"User" means whoever uses the thing. For an app, that's an end user. For a library, it's the developer who installs it. Either way they are not implementing it, and the docs should reflect that.

Voice

Everyday English — the way you'd explain it to a colleague sitting next to you. Formality doesn't add authority, it just adds distance and words.

  • Second person, active, present tense. "You tap Save," not "the Save button should be tapped" or "the user will then be able to save."
  • Cut "just," "simply," "easy," "obviously." They add nothing when things work, and read as mockery when they don't.
  • No marketing adjectives. "Powerful," "seamless," "robust," "intuitive" — that's the author admiring the product. The reader is mid-task and doesn't care.
  • One term per concept, matching the UI exactly. If the button says Workspace, the docs never say "project" or "team space." Varying your vocabulary is good prose and bad documentation — every synonym reads as a new concept.

Scope: what goes in

Include only what the reader needs to use the tool. The default is to leave things out.

Implementation details stay out. Not the database, the queue, the framework, the algorithm's name, the internal service, the refactor it took to ship. The test isn't "is this technical?" — it's "will the reader be surprised, or make a wrong decision, without it?"

Observable behavior passes that test even when it sounds technical, and belongs in the docs: rate limits, offline behavior, what syncs and when, where files are stored, what happens on a conflict, what survives a reinstall. The machinery producing that behavior doesn't.

Concise means no filler, not short. Every sentence earns its place. A thorough troubleshooting section is still concise if none of it is padding — and a two-line page is bloated if one line is a welcome message. Cut preambles, restatements of the heading, and "in this article you will learn."

Structure

  • Organize by user goal, not by feature or screen. Headings are things people want to do — "Share a report with someone outside your team" — not nouns like "Sharing module."
  • Lead with the outcome, then the steps. People need to know they're on the right page before investing in step 1.
  • Prerequisites and limits go up front. Required plan, permission, platform, file size cap. Discovering the blocker at step 6 is the worst possible moment.
  • One page answers one question, and stands alone. Most readers arrive from search or a deep link, never from page 1. Assume no one read the previous page; link instead of assuming.
  • Pick one path. Where three ways exist, document the recommended one. Alternatives get a short note at the end, or nothing.

Cover the unhappy path

This is the most-skipped section and the one people actually search for.

  • Document the failure cases: what the error says, what causes it, what to do next.
  • Say what the feature doesn't do when users predictably assume it does. Cheaper than a support ticket.
  • Warn before destructive actions, never after. The warning goes above the step it applies to.

Keep it from rotting

  • Real examples with real-looking data. No foo, no test123, no lorem ipsum. Examples should be copy-pasteable and produce the result shown.
  • No time-relative language. "New," "recently," "currently," "coming soon" — all rot silently. So do roadmap promises.
  • Walk every procedure literally. If someone following it word-for-word can't finish, the doc is wrong — not the reader.

Before publishing

Read the draft once looking only for these:

  1. Anything the reader doesn't need to finish the task → cut it.
  2. Anything about how it works internally → cut it, unless behavior depends on it.
  3. Any sentence you wouldn't say out loud → rewrite it.
  4. What happens when this goes wrong → is that answered?

Frequently asked questions

What does the Writing User Docs AI skill do?

House style for user-facing documentation — voice, scope, structure, and what to leave out. Use this skill whenever writing, editing, or reviewing anything a user reads to learn how to use a tool — help center articles, getting-started guides, tutorials, feature docs, README usage sections, in-app help, release notes, or FAQ entries. Trigger it even when the request is phrased plainly — "document this feature", "write docs for X", "explain this to users", "write a README for this library", "turn these notes into a guide" — and even when no style guidelines are mentioned. Also use it to revi...

Why use Writing User Docs on TypingMind?

Because you install it once and use it with any model. Writing User Docs is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Writing User Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/callstackincubator/agent-skills/tree/main/skills/writing-user-docs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Writing User Docs?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Writing User Docs?

As many as you like. As long as a model supports skills, you can use Writing User Docs with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Writing User Docs AI skill free?

Yes. It is published on GitHub by callstackincubator under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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