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Maintainer Reply

Community
dannote
maintainer-reply

Draft concise, natural, context-aware public replies to GitHub issues, pull requests, reviews, and contributor follow-ups. Use when replying to a contributor, preparing an issue or PR comment, thanking or apologizing publicly, explaining a project decision, or composing a response that must fit an existing conversation.

Overview

Publisherdannote
Repositorydot-pi
Skill namemaintainer-reply
Stars
57
Forks
4
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 dannote on GitHub. Read the source before you install it.

Installation

Install the Maintainer 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/dannote/dot-pi/tree/master/skills/maintainer-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/dannote/dot-pi.git /tmp/dot-pi
mkdir -p .claude/skills
cp -r /tmp/dot-pi/skills/maintainer-reply .claude/skills/maintainer-reply
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maintainer 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 Maintainer 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 Maintainer 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 before posting. Read the relevant issue or PR conversation, repository guidance, and the user's stated goal before writing. A technically correct reply can still be wrong if it ignores what the recipient already knows or reveals private context.

Identify the reply

Before drafting, establish:

  • recipient and relationship;
  • issue, PR, review, or thread;
  • purpose: answer, clarify, thank, apologize, request information, request changes, close, or explain a decision;
  • what has already been said;
  • what the recipient already knows;
  • the state and time slice the reply must reflect, especially when testing or reconstructing an earlier point in a conversation;
  • whether this is a draft or an approved post.

Keep these separate:

text
private reasoning → review findings, frustration, speculation, roadmap, internal comparisons
public reply      → relevant facts, concrete request, brief rationale, appropriate next step

Do not leak private reasoning merely because it informed the decision.

Style

Write in the maintainer's natural voice:

  • concise and humane;
  • specific to this conversation;
  • proportionate to the issue and contributor;
  • direct without sounding automated or corporate.

Avoid generic filler, obvious restatements, excessive headings, long technical explanations, forced gratitude, unexplained jargon, and LLM-shaped phrases such as “I’ve provided detailed feedback below” when the context already makes that clear. Avoid intensifiers and stock support language such as “sincerely,” “we appreciate your effort,” and “thanks for your understanding” unless they fit the maintainer's established voice and this specific exchange. Do not mention bots, internal agents, or project politics unless the user explicitly wants that included.

Appreciate useful contributions sincerely. Do not treat a good-faith contributor like a low-effort or automated submission without clear evidence. If apologizing, name the concrete mistake and the correction; do not over-explain.

Workflow

  1. Read the relevant conversation and inspect linked context when the reply depends on it. If drafting for an earlier moment, use only facts known at that point and do not copy or rely on later replies.
  2. Summarize privately in one sentence what the reply needs to accomplish.
  3. Draft the shortest complete reply that accomplishes that purpose.
  4. Check every sentence: already known, private, generic, unsupported, or unnecessary sentences should be removed. Never promise a future action or say it is happening “now” unless the user approved it and the current workflow will perform it.
  5. Show the exact draft and intended destination unless the user already supplied exact final text and explicitly asked to post it.
  6. Wait for explicit approval when the text or action is not already explicit. A request to draft or review is not permission to post.
  7. Before posting, re-check the conversation for newer replies. Post exactly the approved text and verify the result.

If the user says “go ahead,” use the immediately preceding draft and action as the scope. Do not infer permission for unrelated comments, edits, labels, closing, or merging.

Review and PR comments

For technical review feedback, use pr-review for the review itself. Prefer concise inline comments for line-specific findings and avoid repeating comments already present in the thread. The summary should state only cross-cutting context or the final decision.

For a reply after review, distinguish:

  • first review;
  • response to author changes;
  • unresolved requested changes;
  • selected ideas reimplemented through vibe-merge;
  • approval or closure.

Use the current conversation to choose the correct one rather than sending a generic review message.

Frequently asked questions

What does the Maintainer Reply AI skill do?

Draft concise, natural, context-aware public replies to GitHub issues, pull requests, reviews, and contributor follow-ups. Use when replying to a contributor, preparing an issue or PR comment, thanking or apologizing publicly, explaining a project decision, or composing a response that must fit an existing conversation.

Why use Maintainer Reply on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dannote/dot-pi/tree/master/skills/maintainer-reply. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Maintainer 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 Maintainer Reply?

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

Is the Maintainer Reply AI skill free?

Yes. It is published on GitHub by dannote 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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