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Github Voice

Community
tobihagemann
github-voice

Shared writing style rules for GitHub-facing output (PR comments, PR descriptions, PR titles, issues, design proposals). Differentiates insider vs outsider voice based on author association. Not typically invoked directly — loaded by other skills before composing GitHub text.

Overview

Publishertobihagemann
Repositoryturbo
Skill namegithub-voice
Stars
417
Forks
32
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 tobihagemann on GitHub. Read the source before you install it.

Installation

Install the Github Voice 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/tobihagemann/turbo/tree/main/claude/skills/github-voice
  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/tobihagemann/turbo.git /tmp/turbo
mkdir -p .claude/skills
cp -r /tmp/turbo/claude/skills/github-voice .claude/skills/github-voice
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Voice 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 Github Voice 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 Github Voice 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.

GitHub Voice

Writing Style

  • No em dashes (—) or double hyphens (--) used as dashes. Use periods, commas, colons, or restructure the sentence.
  • Write in a natural, human tone. Avoid stiff or formal phrasing, unless the session is operating under an explicit style constraint. That constraint governs GitHub text too, and the destination does not relax it.
  • Don't over-explain. Say what needs saying, then stop. Answer a question with exactly what it asked, in the vocabulary it used.
  • Leave evidence of correctness out of a PR body: a Verification section and test-count lines when CI runs the suite, lines listing the tests or changelog entry the change includes, and figures from a survey of live data. A statistic offered as reassurance reads as an open question rather than a finished change. Keep a measurement the change exists to produce.
  • In issues and design proposals, present the principle, the options, and their costs at a high level. Expert readers infer the call-site lists and per-file mechanics, and that detail buries the decision.
  • When explaining how the code works, describe its current behavior. Drop phrasings that narrate the edit history ("X was changed to Y", "no longer does X").
  • Keep one term per concept, preferring the name the code uses. Once a thing has a name, repeat that name instead of rotating synonyms, and refer to a code element by its identifier rather than a shorthand the text never introduces.
  • When the user has stated their reasoning in the conversation, mine it and write from that reasoning. A cleaner argument constructed afterwards reads as someone else's, however sound it is.
  • When the user supplies wording for the artifact, that wording is the draft. Keep their phrasing rather than sharpening it into something more precise, more formal, or more technically careful. Where the phrasing names an effect and the mechanism differs, state the mechanism separately. Flagging the deviation to the user does not license it.
  • Sound like the author, not like an AI assistant.
  • When the user corrects the style of a PR body, issue, or comment, carry that correction into every later GitHub artifact in the session.
  • Never attribute session-internal work to its tooling. Speak as the author, not as a pass-through for unseen automations (AI reviewers, linters, subagents, etc.). The recipient doesn't know about these tools.
  • Composing prose in the user's voice is not the same as posting it. For comments published in the user's name (closing rationales, review replies, issue comments), hand over the draft or get the exact wording approved first. Approval of the underlying action (close, merge, resolve) doesn't cover the prose.
  • Soften opinions when asking questions. Strong verdicts push the reviewer toward a specific answer instead of inviting their input. Flag concerns neutrally and let the reviewer reach their own conclusion. Strong opinions are appropriate when the author wants to take a position; they're out of place when framed as a question.
  • Cut hedges that add no information ("perhaps", "possibly", "I think"). Keep a hedge that carries information: a claim that wasn't verified, a cause that wasn't confirmed, behavior that wasn't tested, a position deliberately left open.
  • GitHub strips the list marker from every task-list item, so an ordered task list (1. [ ]) renders exactly like - [ ] with no visible numbers. Use - [ ] for checklists and let item order carry the sequence. When the reader needs the numbers, write them into the item text.

Voice by Author Association

Before composing GitHub output, detect the author's relationship to the repo. For an existing PR or issue, check author_association on that object:

bash
gh api repos/<owner>/<repo>/issues/<number> --jq '.author_association'

When no artifact exists yet, check your own access on the repo the artifact will be filed against, which is the PR base or the issue target. In a fork workflow that is the upstream repo, not the fork the local remote points at. true means insider:

bash
gh api repos/<owner>/<repo> --jq '.permissions.push'

Insider (OWNER, MEMBER, COLLABORATOR)

Write as a teammate. No third-person references to the team you're on, no deferential offers. State things directly.

Skip context the teammate already has. Don't restate project conventions, recite established workflows, or explain why a commonly-understood rule applies. A reply like "Fixed in ." or "Reverted in ." is often all that's needed. Add rationale only when the action genuinely diverges from what the reviewer would expect.

Outsider (CONTRIBUTOR, FIRST_TIME_CONTRIBUTOR, FIRST_TIMER, NONE)

Write as an outside contributor. Referring to "the project" or "the maintainers" is natural. Deferring to maintainer preferences is appropriate.

If the relationship cannot be determined, default to outsider voice.

Frequently asked questions

What does the Github Voice AI skill do?

Shared writing style rules for GitHub-facing output (PR comments, PR descriptions, PR titles, issues, design proposals). Differentiates insider vs outsider voice based on author association. Not typically invoked directly — loaded by other skills before composing GitHub text.

Why use Github Voice on TypingMind?

Because you install it once and use it with any model. Github Voice 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 Github Voice in TypingMind?

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

Which AI models can use Github Voice?

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 Github Voice?

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

Is the Github Voice AI skill free?

Yes. It is published on GitHub by tobihagemann 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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