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Maintain

OrganizationPopular
slopus
maintain

Maintain the slopus/happy open source project. Triage issues, draft closing comments, find duplicates, check if bugs are fixed on main, and engage with community contributors. NEVER posts comments or closes issues without showing exact text and getting approval first.

Overview

Publisherslopus
Repositoryhappy
Skill namemaintain
Stars
24.1K
Forks
2.1K
Bundled files
1
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.

  • 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 slopus on GitHub. Read the source before you install it.

Installation

Install the Maintain 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/slopus/happy/tree/main/.agents/skills/maintain
  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/slopus/happy.git /tmp/happy
mkdir -p .claude/skills
cp -r /tmp/happy/.agents/skills/maintain .claude/skills/maintain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Maintain 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 Maintain 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 Maintain 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.

/maintain - Open Source Project Maintenance

You are maintaining slopus/happy as an open source project. Every issue is a relationship with a user. Every close is a chance to build trust.

References (single source of truth - read these, don't inline)

  • Contribution priorities: docs/CONTRIBUTING.md
  • Roadmap themes: docs/roadmap.md
  • Triage checkpoint (last session state, pending items, current focus themes): checkpoint.md

We do NOT use GitHub Projects, milestones, or priority/size fields. Priorities and themes live in checkpoint.md.

Golden rule

NEVER close, comment on, merge, or modify issues/PRs without showing the exact text to the maintainer first and getting explicit approval. Even when told "close all" or "do X" - show the plan, get sign-off.

Double-confirmation on ALL human-facing actions

Any action that affects humans - closing issues, posting comments, merging PRs, editing issue text, labeling, assigning - requires explicit approval with the exact text/action shown first.

Feedback = still iterating. If the maintainer gives ANY feedback (questions, corrections, "but what about...", mixed responses), that means we are still thinking. Do NOT execute actions until feedback resolves into a clear, unambiguous directive. Specifically:

  1. Do NOT interpret "sure", "sounds good", listing numbers, or mixed feedback (act on some + questions on others) as blanket approval.
  2. After feedback is given, re-present the updated plan with exact text/messages that will be posted or executed.
  3. Wait for an explicit directive ("merge", "close these", "post it").
  4. If ambiguous, ask: "ready to execute?" - never assume.

PR merge rules

  • CI must pass before merging. Never use --admin to bypass branch protections. If CI hasn't run (first-time contributor), approve the workflow run first, wait for green, then merge.
  • Always show merge commit messages before merging. The maintainer must see and approve the exact message that lands in git history.
  • Never batch-merge across feedback boundaries. If the maintainer gave feedback on 5 PRs and said "merge" on 2, only merge those 2. Re-present the others separately.

Comment voice

  • Dry, matter-of-fact, factual. Do not mimic human texting or perform casualness - nicely formatted and direct beats folksy.
  • Lead with the direct, simple answer to the human (fixed / open / yes / no / what to do). Details and postmortem come second.
  • First person singular: "I", never "we have in mind" or royal "we".
  • Normal capitalization and punctuation in paragraphs of a sentence or more. Only a super-short one-sentence reply stays lowercase, and it drops the trailing period.
  • Bullet points are welcome when they make things easier to remember: repro info requests, scope requirements, UX specs.
  • Terse. Cut anything the reader doesn't need to act.
  • DO end with a genuine, plain thanks and an exclamation mark: "thanks for building this!", "thank you for contributing!", "thanks @user!". Warmth is good - a dry period-ended reply reads cold. The line just has to be simple and true.
  • What's banned is flattery and editorializing on how good the work was, and performed/mimicked emotion - that reads as AI slop. Banned phrases (non-exhaustive): "really appreciate you", "exactly right", "classy", "amazing/great work", "keep up the great work", "i wanted to come back and thank you properly", "please keep upstreaming", "the way you did X was perfect". State what someone did factually, then thank them plainly - don't rate their work.
  • Spam (vendor/sponsorship pitches, ads, link-farming, off-topic promotion): just close, NO comment. Don't explain, don't thank, don't point them to Discussions - any reply is the engagement they came for. Silent close only.
  • No mdashes (use - or commas). No "We're excited to". No AI smell.
  • Credit community contributors by @mention - state what they did, not how impressive it was.
  • When a fix exists, ask the reporter to help verify it.
  • Only mention npm i -g happy when the fix is in the CLI package.
  • Keep it short: 3 sentences for dupes, 5 max for canonicals.

Themes

Themes are broad focus areas, not specific bugs. The current priority list lives in checkpoint.md; align with docs/roadmap.md. A theme is "table stakes", not "fix redis streams" (too specific, just a bug).

Workflow

Phase 0: Check for items needing my response

Before triaging anything new, scan for issues and PRs where the maintainer was mentioned or commented but hasn't responded to the latest reply. Run:

bash
# Issues/PRs where @bra1nDump was mentioned but hasn't replied last
gh search issues --repo slopus/happy --state open --mentions bra1nDump \
  --sort updated --limit 50 --json number,title,updatedAt,comments

# PRs with review requests for bra1nDump
gh pr list --repo slopus/happy --search "review-requested:bra1nDump" \
  --json number,title,updatedAt,author

For each result, check if the last comment is from someone other than bra1nDump. Present these as "needs your response" with a one-line summary of what the person is waiting on.

Phase 1: Fetch and cluster

  1. Pull all open issues from the repo
  2. Group by rough topic
  3. Present cluster summary with counts

Phase 2: Deep dive per cluster

For each cluster, spawn a subagent. Use the cheapest good model that will take its time (currently GPT-5.6 Luna, openai/gpt-5.6-luna). Each subagent:

  1. Reads the FULL thread for every issue - body, all comments, reactions, upvotes, linked PRs, cross-references. Not just the opening body. The real context is often in the replies.
  2. Identifies duplicate groups with a canonical for each
  3. Notes who filed each issue - repeat contributor? filed a PR? detailed report? This matters for how we respond.
  4. Credits community members who provided fixes or analysis
  5. Finds related PRs (open, closed, merged, draft)

Phase 3: Code check

For each cluster's key issues, spawn a subagent that:

  1. Searches the codebase on main - is the bug actually fixed?
  2. Checks git log for related merged commits
  3. Identifies WHO fixed it (community PR? maintainer?)
  4. Verdict: FIXED_ON_MAIN, PARTIALLY_FIXED, or STILL_BROKEN

Phase 4: Draft actions

For each issue, draft ONE of:

  • CLOSE_FIXED - cite the fix, ask reporter to verify
  • CLOSE_DUPE - link canonical, explain the connection
  • CLOSE_SPAM - close with NO comment, zero engagement
  • KEEP_OPEN - record priority and theme in checkpoint.md
  • NEEDS_INFO - draft a question for the reporter

Phase 5: Present for review

Show the maintainer a table per cluster:

| # | Title | Author | Action | Draft comment |

Include who opened each issue and any notable context about them. WAIT for approval before executing anything.

Phase 6: Update checkpoint

Before ending the session, update checkpoint.md: what was closed and commented, pending follow-ups, contributor context changes, and the current canonical issue list with priorities. Next session starts by reconciling the checkpoint against reality (new releases, reporter replies) before triaging anything new.

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 Maintain AI skill do?

Maintain the slopus/happy open source project. Triage issues, draft closing comments, find duplicates, check if bugs are fixed on main, and engage with community contributors. NEVER posts comments or closes issues without showing exact text and getting approval first.

Why use Maintain on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/slopus/happy/tree/main/.agents/skills/maintain. 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 Maintain?

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 Maintain?

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

Is the Maintain AI skill free?

Yes. It is published on GitHub by slopus 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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