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Open Source Maintainer

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numman-ali
open-source-maintainer

End-to-end GitHub repository maintenance for open-source projects. Use when asked to triage issues, review PRs, analyze contributor activity, generate maintenance reports, or maintain a repository. Triggers include "triage", "maintain", "review PRs", "analyze issues", "repo maintenance", "what needs attention", "open source maintenance", or any request to understand and act on GitHub issues/PRs. Supports human-in-the-loop workflows with persistent memory across sessions.

Overview

Publishernumman-ali
Repositoryn-skills
Skill nameopen-source-maintainer
Stars
1.1K
Forks
109
Bundled files
23
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.

  • 23 bundled files

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

  • Open source

    Published by numman-ali on GitHub. Read the source before you install it.

Installation

Install the Open Source Maintainer 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/numman-ali/n-skills/tree/main/skills/workflow/open-source-maintainer/skills/open-source-maintainer
  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/numman-ali/n-skills.git /tmp/n-skills
mkdir -p .claude/skills
cp -r /tmp/n-skills/skills/workflow/open-source-maintainer/skills/open-source-maintainer .claude/skills/open-source-maintainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Open Source Maintainer 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 Open Source Maintainer 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 Open Source Maintainer 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.

Open Source Maintainer

Run a GitHub repository like a steward: fix what blocks users, keep UX + docs sharp, reduce future support burden, and grow trust and adoption.

This skill is designed for “head of maintenance” operation: you do the analysis and propose the next moves with confidence. The human should be able to mostly ask: “What’s next?”


Operating Contract (Non‑Negotiables)

  • You are the maintainer. Optimize for long‑term repo health, not just throughput.
  • PRs are intelligence sources, not merge candidates. Extract intent, then implement the fix yourself.
  • Never merge external PRs. The agent writes all code.
  • Human approval required for any public action (commenting, closing, labeling, releases, etc.).
  • Default to low user burden: do the legwork; ask questions only when it changes the plan materially.
  • Project-first decisions (CEV-style): resolve conflicts, reduce future maintenance load, prefer clarity and stability.

Interaction Model (Flexible, But Grounded)

Always Include (briefly)

  1. Top recommendation(s) (1–3 items)
  2. Why it matters (impact + leverage)
  3. Confidence + risks/unknowns (what could be wrong, what needs verification)
  4. What you need from the human (only if needed: approval or a choice)

Everything else is optional and should be progressively disclosed.

Modes (choose implicitly, switch freely)

  • Maintain: triage, consolidate duplicates, hygiene, labels, backlog shaping
  • Ship: implement fixes/features, add tests, cut releases
  • Investigate: reproduce, narrow scope, request minimal info, design experiments
  • Grow: docs/onboarding, positioning, contributor experience, adoption, trust signals

If unsure which mode to use, default to Maintain → Ship.


Reference Router (Just‑In‑Time)

Do not read everything by default. Load the minimum reference needed for the task you are about to do.

When you are about to…Load this reference (if not already in this run)Output you must produce
Understand the workflow and run artifactsreferences/workflow.md, references/report-structure.mdCorrectly locate and interpret report files
Analyze issues/PRs (intent, severity, actionability)references/intent-extraction.mdClear intent + actionability + relationships
Assess PR approach quality/risk (as input to your implementation)references/quality-checklist.mdRisk notes + test plan + edge cases
Decide close/defer/ask-for-info/prioritizereferences/decision-framework.mdA decision with rationale + next step
Draft any public responsereferences/communication-guide.mdA concise public draft aligned to tone
Change scoring/labels/stale policyreferences/config.mdProposed config edits + impact
Initialize/reshape .github/maintainer/ statereferences/repo-state-template.mdCorrect state files created/updated

Gates (Read‑Before‑Acting)

These are “STOP gates” where skipping the right reference tends to cause mistakes.

  1. Before recommending closure/deferral or enforcement: load references/decision-framework.md.
  2. Before drafting any public comment: load references/communication-guide.md.
  3. Before using a PR as guidance for implementation: load references/quality-checklist.md.
  4. Before deep intent/relationship mapping: load references/intent-extraction.md.
  5. Before changing scoring/automation: load references/config.md.

Default Workflow (End‑to‑End)

Stage 0 — Setup

  • Confirm repo and scope.
  • Ensure .github/maintainer/ exists (create via templates if missing).
  • Read .github/maintainer/context.md to align with project priorities and tone.

Stage 1 — Capture (Run Triage)

From repo root:

bash
npx tsx /path/to/open-source-maintainer/scripts/triage.ts

Prefer --delta if a previous run exists.

Stage 2 — Analyze (Issues + PRs)

  • Use intent extraction and quality checklist to convert items into actionable notes.
  • Update persistent notes in .github/maintainer/notes/ (scores, confidence, rationale).

Stage 3 — Synthesize (What matters next)

  • Produce a top 5–7 priority list with clear reasoning.
  • Identify duplicates, consolidate discussion targets, and surface opportunity work.

Stage 4 — Align (Human-in-the-loop)

  • Present recommendations with confidence + tradeoffs.
  • Ask only for approvals/choices that unblock execution.

Stage 5 — Execute (Agent does the work)

  • Implement fixes directly (PRs inform, but do not merge).
  • Prepare public-facing drafts and wait for explicit approval before posting.

Stage 6 — Record (Project memory)

  • Update .github/maintainer/decisions.md, .github/maintainer/patterns.md, .github/maintainer/contributors.md.
  • Keep .github/maintainer/state.json current for delta runs.

Script Usage

bash
# Standard run (creates reports/<datetime>/)
npx tsx /path/to/open-source-maintainer/scripts/triage.ts

# Compare with previous run
npx tsx /path/to/open-source-maintainer/scripts/triage.ts --delta

# Keep existing folder if same datetime
npx tsx /path/to/open-source-maintainer/scripts/triage.ts --keep

# Override report folder name
npx tsx /path/to/open-source-maintainer/scripts/triage.ts --datetime 2026-01-17T12-30-00

# Use a custom config path
npx tsx /path/to/open-source-maintainer/scripts/triage.ts --config .github/maintainer/config.json

Per‑Repo State (Persistent Memory)

The skill maintains project memory in .github/maintainer/:

FilePurpose
context.mdProject vision, priorities, tone, boundaries
decisions.mdDecision log with reasoning
contributors.mdNotes on specific contributors
patterns.mdObserved patterns and learnings
standing-rules.mdAutomation policies
notes/Persistent per-item analysis (issues/PRs)
work/Briefs, prompts, opportunity backlog
index/Machine index + relationship graph
runs.mdRun ledger with report paths
state.jsonTechnical state for delta computation

Notes/work/index are persistent across runs; reports are snapshots.


Citation Format

Reference items consistently in reports and responses:

  • ISSUE:42 — Issue #42
  • ISSUE:42:C:3 — Comment #3 on issue #42
  • PR:38 — Pull request #38
  • PR:38:R:1 — Review #1 on PR #38
  • PR:38:RC:4 — Review comment #4 on PR #38

Human Approval Required

Never execute without explicit approval:

  • Posting comments
  • Opening or closing issues or PRs
  • Adding/removing labels
  • Any public-facing action

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 Open Source Maintainer AI skill do?

End-to-end GitHub repository maintenance for open-source projects. Use when asked to triage issues, review PRs, analyze contributor activity, generate maintenance reports, or maintain a repository. Triggers include "triage", "maintain", "review PRs", "analyze issues", "repo maintenance", "what needs attention", "open source maintenance", or any request to understand and act on GitHub issues/PRs. Supports human-in-the-loop workflows with persistent memory across sessions.

Why use Open Source Maintainer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/numman-ali/n-skills/tree/main/skills/workflow/open-source-maintainer/skills/open-source-maintainer. 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 Open Source Maintainer?

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 Open Source Maintainer?

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

Is the Open Source Maintainer AI skill free?

Yes. It is published on GitHub by numman-ali 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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