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Ux Writing

Community
scarletkc
ux-writing

Judgment rules for user-facing text and docs: CLI and diagnostic output, error and help text, README and docs structure, code comments, titles, and generated reports, decks, or exports. Use when writing or changing any user-visible string, when adding or restructuring docs or deciding which page owns a fact, when a page is about to record a version, a deployment state, or a value the code already owns, when a comment, title, or artifact could carry the reasoning or an abandoned option behind the change, when a behavior change needs its copy sites swept, or when reviewing a diff that touches copy or docs.

Overview

Publisherscarletkc
Repositoryagents
Skill nameux-writing
Stars
225
Forks
12
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Ux 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/ux-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/ux-writing .claude/skills/ux-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ux 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 Ux 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 Ux 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.

UX Writing & Docs

User-visible text is product behavior and carries the same quality bar as code. Every rule below is distilled from a real defect caught in review, and keeps its counter-example because the reasoning is the point. When in doubt, re-read the output as the user who just hit the problem.

Status output & diagnostics

  • Report effective values, not stored ones. A status display answers "what will happen when I run this", so resolve values exactly the way the runtime does, including environment variables and layered config. Counter-example: a config viewer printed "API key set: no" while an env var held the key the next run would actually use.
  • Diagnostics must stay truthful under failure. When one config layer fails to load, fall back to the most complete state that still loads, never to blank defaults. A diagnostic that misreports is worse than one that aborts. Counter-example: a broken project-level config made a doctor command check blank defaults and report a missing API key that was in fact configured; the fake failure buried the real one.
  • Show deltas, not dumps. A health/diagnostic command lists what deviates and who set it; the exhaustive listing belongs to the dedicated inspect command. Don't make one command duplicate another's job. Counter-example: a doctor check printed fifteen "field: origin" lines, most of them saying "global". One line naming the two real overrides replaced the block.
  • Annotate at the granularity of the claim. If one sub-part of a composite value has a different source or state, say it on the sub-part; don't relabel the whole. Counter-example: an env-injected API key relabeled an entire endpoint block "(environment)" although its URL and model came from a file. The fix was "key from env", with the block label unchanged.
  • Re-read neighboring labels after adding metadata. New suffixes collide with existing value labels. Counter-example: "Embedding dimensions: default (default)", fixed by renaming the value "auto".
  • Machine-readable output is a contract. Porcelain/TSV/JSON output never gains decoration, notices, or annotations; informational text goes to stderr or the human-format path. Absence is part of the contract, so write the negative test ("(project)" not in stdout).
  • Never truncate the payload. Paths, IDs, and URLs in diagnostics must survive narrow terminals un-ellipsized (disable auto-wrap/crop for those lines); a truncated path cannot be copied into the next command.

Error messages

Every error answers three questions: what happened, where, and what to do now. The strongest pattern: name the offending file or input, list the rejected fields, list the allowed fields, and say where the rejected setting belongs instead. Fail loudly rather than degrade silently; when catching an exception purely to suppress a traceback, keep the message intact.

Documentation

  • Each document has one responsibility, and it decides what belongs. A page is a durable contract, a proposal, an investigation, a TODO, a dated work order, or a runbook — one of them, not several. Naming that first is what makes a canonical home decidable: a fact lives on the page whose job it is, and every other surface reaches it through a single specific link instead of a partial retelling on each page that happens to touch it. When two pages both claim to be the detailed spec, the broader responsibility keeps the shared rules and the narrower keeps only what its own surface adds. Counter-example: an implementation plan stayed the de-facto spec after shipping, so the rules lived half there and half in the architecture doc; folding the stable rules into the contract and leaving the sequence in git history left one page to trust.
  • Rationale is a genre of its own. A how-to answers what to run, a reference answers what exists, and why-it-was-built-this-way belongs to a design record, an ADR, or the pull request that decided it. Answering the design question inside a usage page pushes the steps the reader came for below the fold, and the argument is also the part that rots first: the implementation moves on and only the guide still defends the old choice. An explanation produced because someone asked once belongs in that answer, not in a permanent page. Counter-example: a setup guide spent its second paragraph on why this queue was chosen over two others; the queue was replaced a release later and the paragraph outlived it.
  • One canonical home per fact. Details that change together (field lists, precedence chains, supported values) live in exactly one document; every other mention links to it. Legitimate copies: artifacts distributed standalone (a bundled skill file that ships without the repo), and genuinely surface-specific nuance. Counter-example: a seven-field allowlist pasted into five docs.
  • Restating and linking is a bug, not thoroughness. If a section duplicates the canonical content and then ends with "see X for the full contract", it already is the full contract. Delete the restatement; keep the link and whatever is specific to this surface.
  • Prefer the smallest sufficient edit. When revising existing text, preserve unaffected wording, structure, and rationale. Remove genuine duplication, but do not rewrite neighboring prose or compress away useful distinctions without a reason. Counter-example: changing one mandatory workflow into an optional one rewrote several surrounding sections, then over-corrected by removing useful context; a few local edits were enough.
  • Insertion respects adjacency. Before adding a section, check what the surrounding paragraphs attach to. Counter-example: a new section landed between a flags table and its output-format footnote, orphaning the footnote in the wrong chapter.
  • Adjectives need evidence. "Recommended", "faster", "better" come from your own benchmarks, not optimism. Counter-example: a feature was about to ship commented "# recommended" while the project's own eval showed it losing to the default on strong models. It shipped as "optional".
  • Every README section has one job. Positioning sections ("Why X?") don't accumulate feature bullets; quick-starts don't explain architecture. A README stays lean and links into the docs; detail accumulating there usually means it left its canonical home.
  • Order a page by what the reader needs first, and split when it stops being one task. Open with scope and the authoritative entry points, then the common rules and the main path, and only then exceptions, recovery, and change checks. An overview layer summarizes stable semantics and links down; it does not carry field tables, full payloads, or current numbers to buy self-containment. When a page starts demanding that the reader understand several unrelated tasks, or whole chapters serve only two maintainers, that is the signal to split it — and the split leaves behind one line of purpose plus the link, never a second copy of the fact. Counter-example: a getting-started page opened with the full option reference, so the three commands a first-time reader needed sat two screens below it.
  • Reminders name the most-forgotten item only. A guideline that enumerates every artifact reads as noise and gets skipped whole. "Update whichever docs the change affects; the bundled skill is the easiest to forget" beats a list of six file types.

Facts that go stale

Docs are edited on a human cadence, while some facts change on every commit, deploy, or restart. Writing one of those into a long-lived page is not a maintenance burden, it is a defect on a delay: the page turns wrong on its own, and nothing fails when it does. Record where the current answer is read, not the answer. This is "report effective values, not stored ones" applied to prose.

  • Never snapshot a value that moves faster than the doc. Long-lived pages (README, architecture notes, runbooks, domain docs) carry the stable material: intent, invariants, boundaries, procedures, failure handling. Version and protocol numbers, image tags, build IDs, deployed commit hashes, object and migration counts, expiry dates, and "currently live / not yet shipped" claims all change without anyone re-reading the page that repeats them. Those belong in a changelog, in git history, or on the release ticket, where carrying a date is the point. The rule forbids the hand-maintained second copy, not the table: when a page genuinely has to show current values, generate it from the authoritative source at build time so it cannot drift silently. Counter-example: a runbook opened with "production currently runs 2.3.1"; four releases later an on-call engineer trusted the line and worked through the wrong version's changelog.
  • A pointer names a symbol, not a repository. The canonical home for a fact is often code rather than a doc, and then the doc's job is to say where to read it instead of copying the value or the whole field table. Make the pointer land: a specific file plus a searchable symbol, function, data key, or heading. "See the source", a repo-root link, or a directory leaves the reader to re-derive what the sentence promised. If no single symbol owns the fact, that is a code problem surfacing as a doc problem; fix the boundary instead of papering over it with a copied table. A directory is a fair target in two cases only: the fact emerges from an ordered set with no single-file truth (migrations replayed in sequence), or the directory is a catalog some loader enumerates (locales, plugins, maps). Both still owe a searchable selection key — the naming convention, the loader function, the object name. Counter-example: "protocol versions are defined in the networking layer" sent every reader grepping six files, and became a link to PROTOCOL_VERSION in net/constants.py.
  • A doc cannot observe the runtime. The repository answers how a commit is meant to behave; only the running system knows which commit is live, what is healthy, and which artifact is being served. Docs record the command or console that answers those questions, never the answer, and never promote merged code to "deployed". A successful deploy report is evidence on that release's ticket; copying it into a doc converts a one-time result into a standing hand-sync obligation. Counter-example: a "current environment" table listing service versions was updated by hand after every deploy, until the deploy where it wasn't, and nothing in CI could notice.

The final state, not the path to it

A deliverable is read by someone who was not in the room while it was made. Anything that only holds against the conversation behind it — an option that was considered and dropped, a scope that was corrected, an instruction the requester gave ten minutes ago — reads as noise at best, and at worst as a claim about the product. Session context expires faster than the artifact carrying it, so this is "facts that go stale" applied to the conversation rather than to time. The test for any line: does it hold for a reader who has never seen that conversation? "Without the bulk-download panel" does not, because nobody expected one. "Not json.dumps here, the payload has to keep key order for the signature check" does.

  • State what the code does, not what it nearly did. Titles, summaries, and comments describe the shipped behavior; intermediate attempts, abandoned options, and negative scope belong to the discussion that produced them, which git history and the review thread already keep. Counter-example: a requested cut left the pull request titled "Add export button (without the bulk-download panel)", so every reader had to understand a panel that never existed before reading the one that did.
  • A comment carries the non-obvious reason only. What earns the lines is a constraint the next reader cannot recover from the code: an ordering requirement, an upstream bug, a platform quirk. A "why not X" line qualifies when X is what that reader would reach for anyway, not when X is merely what this conversation happened to try and discard. Restating what the code plainly says, or defending it against an alternative nobody would propose, spends attention now and becomes a lie when the code around it moves. Counter-example: a helper kept eight lines on why it held no cache, written the moment a reviewer asked for the cache to go; two rewrites later the paragraph was the only trace of either.
  • Evidence serves the reader's decision, not the author's doubt. A claim the reader has to act on — this one is faster, this default is safe, use this over that — owes its basis, and "adjectives need evidence" above says where the basis comes from. A statement of what the code does owes nothing, because nobody is being asked to believe anything: a cited standard, a benchmark number, or an appeal to consensus attached to it is answering a challenge that was never made. The test is whether removing the line changes what the reader can decide. Cited figures also age faster than the sentence carrying them, and nobody comes back to re-measure. Counter-example: a config page defended its default with "benchmarks show a 40% improvement", measured two majors earlier against a code path that no longer existed; support was still quoting the number.
  • A deliverable does not narrate its own production. Generated reports, decks, exports, and screens are product content: they carry findings, values, and instructions, never the implementation notes, method rationale, or next steps of whoever produced them. "This page demonstrates", "we could also", and "implemented as" are the producer's voice leaking into the product, and the exceptions are narrow — copy that genuinely is help text or an empty state, and documents explicitly asked to record their own methodology. Reasoning has its own homes: the reply to the requester, the commit message, the pull request body, a planning file. Counter-example: a generated status deck opened on a slide titled "Approach and next steps for this report", ahead of the numbers it had been asked for.

Sync sweep for behavior changes

A behavior change is unfinished until its copy sites agree. Grep for the old wording across, in rough order of forgettability:

  1. --help option strings, the most-missed site: a flag's help kept saying "show current configuration" after the command learned origin labels,
  2. centralized message/string modules and command docstrings,
  3. README and docs pages,
  4. bundled skill files, plugin metadata, MCP tool descriptions (these are UX for agents, and the same rules apply),
  5. roadmap or status notes describing the old behavior.

Then re-resolve the links involved: a renamed heading breaks every anchor aimed at it, a moved file breaks the relative paths pointing at it, and neither announces itself in a normal test run.

Testing copy

  • Assert flattened text or behavior, not console formatting: consoles wrap (~80 columns under test runners), so multi-word substrings split across lines. Flatten with " ".join(output.split()) before substring assertions.
  • Color env leakage: FORCE_COLOR / COLORTERM in the invoking shell make rich consoles emit ANSI into captured output; clear them for test runs.
  • For machine formats, assert what must be absent, not only what must be present.

Frequently asked questions

What does the Ux Writing AI skill do?

Judgment rules for user-facing text and docs: CLI and diagnostic output, error and help text, README and docs structure, code comments, titles, and generated reports, decks, or exports. Use when writing or changing any user-visible string, when adding or restructuring docs or deciding which page owns a fact, when a page is about to record a version, a deployment state, or a value the code already owns, when a comment, title, or artifact could carry the reasoning or an abandoned option behind the change, when a behavior change needs its copy sites swept, or when reviewing a diff that touches...

Why use Ux Writing on TypingMind?

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

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

Which AI models can use Ux 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 Ux Writing?

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

Is the Ux 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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