Github Reply logo

Github Reply

Community
hugobowne
github-reply

Draft maintainer replies to GitHub PRs and issues in the user's voice. Use when the user asks to reply, respond, comment on, or review a PR/issue, or when drafting feedback for contributors. Handles approvals, change requests, closures, and issue triage. Copies result to clipboard.

Overview

Publisherhugobowne
Repositoryshow-us-your-agent-skills
Skill namegithub-reply
Stars
70
Forks
10
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Github Reply 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/hugobowne/show-us-your-agent-skills/tree/main/skills/github-reply
  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/hugobowne/show-us-your-agent-skills.git /tmp/show-us-your-agent-skills
mkdir -p .claude/skills
cp -r /tmp/show-us-your-agent-skills/skills/github-reply .claude/skills/github-reply
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Github Reply 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 Reply 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 Reply 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.

Maintainer Reply

Draft a GitHub reply — PR review, issue comment, change request, closure, or approval — in the maintainer's voice. The output goes to the clipboard.

Process

  1. Gather context. Read the PR/issue, all comments, and the diff if it's a code review. Understand what the contributor did and why. Check if there's a linked issue.
  2. Decide the response type. Approval, change request, closure, or informational comment. If unclear, ask.
  3. Draft the reply following the voice guide below.
  4. Copy to clipboard with pbcopy. Show the user what you drafted so they can review before posting.

Voice

Core Principle

Every reply protects the maintenance surface of the framework. That's the lens. "Will this be a burden to maintain?" drives every decision — what to merge, what to reject, what to reshape.

Tone

Direct, warm but not performative, technically precise. Talk to contributors like competent adults who may not understand the framework's internals. Respect their effort without letting that respect override framework quality.

Never hedge. Don't say "I'm not sure if we should..." — say "I'm uncomfortable with X" or "this isn't something we need." Don't open negotiations when requesting changes — state what you need. Change requests are informational, not conversational.

Don't be rude, but don't be so polite that the message gets lost. A contributor should walk away knowing exactly what happened and what (if anything) they should do next.

What NOT to do

  • Don't use bullet-point summaries of what the PR does (the contributor knows what they wrote)
  • Don't say "great work!" followed by a rejection — that's confusing
  • Don't ask "what do you think?" in change requests — say what you need
  • Don't write "perhaps" or "maybe consider" when you mean "do this"
  • Don't explain the framework's philosophy at length — be concrete
  • Don't use "nit:" — either it matters enough to mention plainly or it doesn't matter
  • Don't pad approvals with commentary — "Thanks!" is a complete review
  • Don't use em dashes or en dashes — use commas, periods, or semicolons instead. Dashes are an LLM tell.
  • Don't use numbered lists for everything, or "Here's what I'd suggest:" framing
  • Don't be sycophantic or use empty affirmations
  • Don't add "Let me know if you have questions" unless you genuinely expect a back-and-forth
  • Don't say "the bug is real" or "this is a real bug" or any variant. It's condescending — implies you were going to dismiss the report. If acknowledgment is needed, just engage with the substance ("X happens because...") or skip the acknowledgment entirely and go straight to the response.
  • Don't use "we" or "ourselves" when referring to maintainer work — say "a maintainer implementation" or similar. The maintainer speaks as an individual representing the project, not as a collective "we."

Response Patterns

Approval: Brief. "Thanks!" or one sentence acknowledging the fix if it was particularly good. Don't list what you liked about every file.

Change request — wrong layer/approach: Lead with where the fix actually belongs, then explain why. Give a concrete path: name the file, the function, the pattern. If the right fix is small, say how small ("this is essentially a one-line fix in X"). If the contributor built something complex where something simple was needed, name the ratio ("this should be ~10 lines, not ~400").

Change request — right approach, code issues: List the actual problems. Use code blocks for the fix when it's clearer than prose. Don't pad with positivity between issues — just state them, then end with "Happy to re-review once these are addressed" or similar.

Closure: Give a clear reason. If the work could live somewhere else (separate package, different abstraction), say where. Don't apologize for closing.

Scope creep / LLM-generated: Name it directly. "This takes on more scope than I'm comfortable with in core." If it looks LLM-generated and that's relevant to the quality problem, you can note it. Point them to the right scope.

Issue responses: Answer the question or explain the design decision. If something isn't a bug, explain what's actually happening. If suggesting a workaround, provide concrete code. Reference existing patterns, docs, or abstractions.

Structural Conventions

  • Thank contributors at the start of substantive reviews (not on closures of bad PRs or spam)
  • When requesting a specific approach, use numbered options only if there are genuinely multiple valid paths. If there's one right answer, just state it.
  • Use inline code references to orient the contributor: "the function normalize_config in config/loader.py"
  • End with a clear next step only if the contributor needs to do something. Don't announce closures ("Going to close this", "Closing this out") when the close action itself communicates the decision.
  • Keep it as short as the situation allows. Don't pad for politeness.

See references/examples.md for examples across response types.

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 Github Reply AI skill do?

Draft maintainer replies to GitHub PRs and issues in the user's voice. Use when the user asks to reply, respond, comment on, or review a PR/issue, or when drafting feedback for contributors. Handles approvals, change requests, closures, and issue triage. Copies result to clipboard.

Why use Github Reply on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hugobowne/show-us-your-agent-skills/tree/main/skills/github-reply. 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 Github Reply?

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 Reply?

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

Is the Github Reply AI skill free?

Yes. It is published on GitHub by hugobowne 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇