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Product Writing

Community
scarletkc
product-writing

Write, edit, or review UI copy, CLI messages, help text, code comments, and technical documentation covering product behavior, errors, status, setup, compatibility, or technical results.

Overview

Publisherscarletkc
Repositoryagents
Skill nameproduct-writing
Stars
225
Forks
12
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Product Writing 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.
    https://github.com/scarletkc/agents/tree/main/skills/product-writing
  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/scarletkc/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/product-writing .claude/skills/product-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Writing 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 Product Writing 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 Product Writing 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.

Product Writing

Help the reader understand what is happening and what they can do next.

Work from the task

Use only the sections relevant to the requested copy. Preserve the user's meaning, terminology, audience, and requested format. Follow existing product conventions where they help readers recognize controls, commands, and states. A wording task does not itself authorize changes to product behavior or a reorganization of the documentation.

Ground behavioral claims in supplied facts, the relevant implementation or specification, or an observed result. When a fact cannot be verified, identify that uncertainty where it matters and complete the parts that are supported. Do not invent behavior or a cause to make the text sound complete.

Interface copy, errors, and help

  • Name the action or state accurately. Distinguish saving a setting from testing a connection, accepting a request from completing a job, and partial success from full success. For example, a queued export should not announce that a file is ready to download.
  • Make recovery useful. Identify what failed and the relevant input or operation. Include a next step when it is known and actionable. An unknown network failure does not establish that credentials are wrong. Put detail in the message, an expanded view, or a specific help link as the interface allows; an error need not fill a fixed template.
  • Preserve meaningful distinctions. Keep prerequisites, limits, and consequences that affect the reader's decision. Use the product's names for controls and commands. Do not shorten away which item an action affects or imply that an irreversible action is temporary.

Status and diagnostic output

  • Describe the state the label promises. Effective settings account for runtime overrides; stored settings should be identified as such. If only the API key comes from the environment, label that field rather than the entire endpoint as environment-provided.
  • Keep failure visible. Distinguish unknown or unavailable values from empty, missing, or default values. If partial results are supported, identify what could not be checked. Follow the project's failure behavior instead of introducing a fallback merely to produce a message.
  • Preserve output contracts. Keep prose and decoration out of JSON, TSV, and other machine formats. Use existing diagnostic channels for explanations. Keep paths, IDs, and commands complete where users need to copy them, subject to the product's redaction rules.
  • Give diagnostic views distinct jobs. When a dedicated inspection command already provides all values and origins, a health summary can focus on deviations, failures, and their sources, with a pointer to full details. Without that separate view, preserve the values needed to investigate the problem. Fifteen normal field: origin rows can bury the two overrides that matter, but removing the only available configuration view loses information. Re-read adjacent labels to catch duplication such as default (default).

Documentation and technical explanations

  • Give each page one responsibility. A how-to completes an operation, a reference defines a contract, a design record explains choices, and a report presents findings and their basis. Put a section on the page that owns its reader question. When a page mixes independent tasks, separate them and leave a useful pointer at the boundary. An overview's responsibility is orientation: summarize the available paths and link to their details instead of becoming a second reference manual.
  • Keep README sections focused. The introduction identifies the product, its audience, and its purpose. Positioning explains why someone would choose it. Quick-start instructions give the shortest complete path to a useful result, including prerequisites, commands, and essential caveats. Full option catalogs, architecture explanations, and decision histories belong in their respective documents, linked from the relevant section. Do not turn a quick start into an architecture tour or a positioning section into a feature dump. Keep a compatibility warning beside the step it affects; moving background detail must not hide a condition needed to follow the instructions safely.
  • Keep reasons near the decisions they support. A setup step may need a short explanation of why a prerequisite matters. A long history of rejected designs usually belongs in a design record linked from the guide, unless that history is the page's purpose. Reports need enough method, source context, assumptions, and limitations for readers to assess the findings.
  • Separate summaries from competing specifications. Keep one maintained source for a detailed contract. A summary explains what the reader needs now; a second complete field table, default list, or precedence rule creates another specification to maintain. For example, a README can show a minimal configuration and link to the full schema instead of copying all seven fields into another table. Detailed repetition may be necessary for independently distributed artifacts; check how those copies stay synchronized.
  • Plan how changeable facts stay correct. Supported versions, pinned install commands, and compatibility limits may be necessary. Verify them against the maintained source, then check what will keep them aligned on the next change: generation, an existing release check, or an explicit maintenance responsibility. Avoid adding a second hand-maintained copy of a build ID, migration count, or current deployment version just to make a page self-contained. Prefer a pointer or generated value when the reader needs the current answer. Preserve version constraints that are part of the instructions; do not remove them simply because versions change.
  • Distinguish records from live state. A dated report can record the version and status actually observed, with the evidence and limits of that observation. A standing operational guide should direct readers to the command or dashboard that answers what is running now. A merged change alone does not establish deployment, and a successful deployment alone does not establish every health or verification claim.
  • Make the next reference useful. Link to the section, command, API entry, or symbol that answers the reader's question. For implementation details, PROTOCOL_VERSION is a useful pointer; a repository root leaves the reader to search again. For normal setup, prefer user-facing instructions. Keep caveats next to the steps they qualify and preserve working anchors.

Comments and standalone artifacts

Read titles, comments, and deliverables as someone who has not seen the working conversation. Remove abandoned options and temporary scope qualifications that make sense only in that conversation. An export button title does not need "without the bulk-download panel" if readers were never offered such a panel. Keep exclusions when they explain a real contract, compatibility limit, or tradeoff the reader needs; preserve history in documents requested to record it.

Comments are most useful for reasons the code does not make apparent: ordering constraints, upstream defects, compatibility workarounds, and invariants a later edit could break. Explain an alternative when it is one a maintainer would reasonably reach for. For example, a comment explaining why a lock must be released before invoking a callback can prevent a deadlock; "release the lock" merely repeats the operation. Do not replace that reason with a shorter restatement of the code. Keep public API documentation and useful algorithm overviews as well.

Claims and evidence

Match the support to the claim. Performance comparisons need applicable measurements or a cited result with its scope. Compatibility claims need the relevant implementation or maintained specification. An editorial recommendation can explain a concrete benefit, such as naming the failed field so the reader can locate it; recommending clearer wording does not require a benchmark.

Keep observations, hypotheses, preferences, and recommendations distinct. Preserve user-provided opinions as opinions. Qualify unsupported claims or explain the gap rather than invent proof or erase useful uncertainty.

Review and verification

In a review, identify the wording, its effect on the reader, and a concrete correction when the evidence supports one. If the copy already works, say so; optional preferences should not become required rewrites. For an editing task, make the requested edits and explain consequential choices only as needed.

Check meaning, terminology, formatting, and affected links. When behavior changes, search for dependent help text, errors, documentation, and bundled skill descriptions that need the same update. Keep catalog and localization entries in sync where applicable; avoid turning a small edit into a general audit.

Use the project's relevant checks. When output behavior changes, cover success and failure: parse machine formats and check channels; for human messages, assert the relevant information without relying on incidental wrapping or color.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Product Writing AI skill do?

Write, edit, or review UI copy, CLI messages, help text, code comments, and technical documentation covering product behavior, errors, status, setup, compatibility, or technical results.

Why use Product Writing on TypingMind?

Because you install it once and use it with any model. Product Writing 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 Product Writing in TypingMind?

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

Which AI models can use Product Writing?

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 Product Writing?

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

Is the Product Writing AI skill free?

Yes. It is published on GitHub by scarletkc under the Apache-2.0 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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