Openclaw Pr Maintainer logo

Openclaw Pr Maintainer

OrganizationPopular
openclaw
openclaw-pr-maintainer

Review, triage, repair, or land OpenClaw issues and pull requests with current-source evidence and the native maintainer workflow.

Overview

Publisheropenclaw
Repositoryopenclaw
Skill nameopenclaw-pr-maintainer
Stars
391K
Forks
82.2K
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Openclaw Pr 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/openclaw/openclaw/tree/main/.agents/skills/openclaw-pr-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/openclaw/openclaw.git /tmp/openclaw
mkdir -p .claude/skills
cp -r /tmp/openclaw/.agents/skills/openclaw-pr-maintainer .claude/skills/openclaw-pr-maintainer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openclaw Pr 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 Openclaw Pr 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 Openclaw Pr 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.

OpenClaw PR Maintainer

Use for the requested OpenClaw issue/PR operation. Root AGENTS.md owns repair, product, source-trust, and approval policy; this skill owns the GitHub workflow. A pasted ref is context, not permission to publish or expand the task.

Select the scope

  • Review, triage, or list: read-only. Explain the change, actionable findings, useful evidence, and material uncertainty. Do not edit, assign, label, comment, or close unless separately authorized.
  • Fix only: investigate, repair locally, and validate. Publishing still requires ship/land or equivalent scoped authority.
  • Land/ship or autonomous repair: finish the authorized scope through current source proof, review, native merge submission, and verified remote merge. Keep checking pending requests and repair CI failures or conflicts under the same authority; accepted auto-merge is pending work, not task completion. Preserve contributor credit and unrelated work. Routine steps need no repeated permission; security, schema, product, release, and other root gates remain.
  • Queue/discovery: read triage. Batch live reads and shared discovery; do not turn one named PR into an unsolicited queue sweep.
  • Author or regression attribution question: read author and provenance research. Account age and contribution-history queries are conditional, not a prerequisite for every PR.

The lead remains hands-on. Use bounded independent workers where they add useful evidence; serialize shared checkout/ref mutations and GitHub writes. Do not create user-owned app tasks to hide internal subtasks. Preserve a stalled worker's patches, claims, and proof before taking over or reassigning its work.

Inspect the actual item

Start with git status -sb; preserve unrelated changes. Use available local gitcrawl data for related work when useful, then bare PATH gh with narrow JSON fields for live decisions. Stale/missing archives fall through to gh; do not broadly sync archives just to begin. PR source comes from gh pr view/diff and the checkout, not web search.

bash
gh pr view <pr> --repo openclaw/openclaw \
  --json number,title,state,body,author,assignees,baseRefName,headRefName,headRefOid,files,comments,reviews
gh issue view <issue> --repo openclaw/openclaw \
  --json number,title,state,body,author,assignees,comments

Check assignment before deep work. Mention another assignee and treat a fresh assignment as active ownership unless the user directs otherwise. Assignment alone is not a block when stale or explicitly overridden. Claim an unassigned named item only under assignment or landing/autonomous-resolution authority, using the verified authenticated login. Never remove others' assignments without direction. Account/activity research belongs to its conditional route.

Establish the outcome

Read the affected owner, entry points, callers, relevant siblings, tests, docs, history, and current main as needed to support a conclusion. Inspect dependency contracts directly when they determine behavior; apply the root's personal Codex-source inspection requirement when relevant. Investigate contrary evidence before defending a verdict. Do not fill an arbitrary evidence matrix or invent a rejected alternative merely to satisfy a form.

  • Already fixed: prove the original behavior is repaired on current main, with the canonical commit/PR and matching source or runnable proof. Under closure authority, comment that evidence and close; otherwise report it.
  • Real defect: reproduce it, repair its owning invariant, and prove the repaired path and affected siblings. Prefer a coherent simplification over a symptom guard. Size and LOC are context, not correctness gates.
  • Existing useful PR: improve the editable branch rather than replacing it unnecessarily. If it cannot safely be updated, create an authorized credited replacement before closing the source. Never squash a contributor-owned PR after replacing its ancestry; use a maintainer-owned replacement.
  • Uncertain or product decision: state the concrete gap. Do not merge a speculative repair or automatically close work as out of scope.

Use $openclaw-testing to choose proportional proof and its source-trust/host route. Trusted development proof defaults local; remote proof needs an environment or isolation reason. Unavailable optional live proof may use meaningful boundary proof with the limitation stated. Explicit live requests, external API contracts, and changes whose risk requires authenticated execution keep their required proof. Never describe mocks, skipped checks, or an older head as live evidence.

Review and publish

Before committing or landing nontrivial code, run $autoreview and resolve verified actionable findings. Use it for a review-only request when that request asks for independent autoreview; ordinary findings-only review does not need a second reviewer by default. Do not add another mandatory CLI review pass. Reopen review for substantive changes or unresolved concerns, not a patch-identical rebase or a mechanical head change. Address real human/bot findings and explain rejected ones; bot scores and Rank-up lists do not create separate obligations.

Use the current PR template. Lead with the plain-language problem and concrete user impact; keep the explanation short and leave implementation inventories in the diff or optional details. Keep important risks, migrations, required actions, useful evidence, known gaps, and contributor credit visible and current. Do not invent a user benefit for internal-only work. Explain material tradeoffs when they matter; do not require universal LOC tables, provenance fields, or alternate-fix essays. CHANGELOG.md is release-owned; user-facing release-note context stays in the PR/commit. Omit agent transcripts unless explicitly requested.

Before public mutation, verify author/committer and authenticated writer identity. Use body files for shell-sensitive text. Post findings only under the authorized write scope, and update an existing relevant comment/body instead of duplicating it. Publication, media, exact-head CI, and native landing mechanics are in landing; read that reference before publishing or merging.

Verify the final merge/closure state and source rather than trusting a local summary. Report the problem, owner-level change, important proof and limitations, credit, and linked final state in concise prose. Record worthwhile follow-ups; do not manufacture another task after a bounded request is complete.

Finalize and clean up

Once the requested outcome and required verification are complete, remove the task's disposable proof and scratch. This includes test logs, receipts, local proof archives, and .crabbox outputs. Do not retain, archive, export, or hand off these files merely to remove the completed task's worktree. Follow native closeout.

Required pre-merge proof stays mandatory. Preserve requested deliverables, explicit retention requests, unfinished source, recovery state needed by unfinished operations, active owners, credentials, agent state, and shared dependencies. Optional follow-ups do not keep a completed task open.

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 Openclaw Pr Maintainer AI skill do?

Review, triage, repair, or land OpenClaw issues and pull requests with current-source evidence and the native maintainer workflow.

Why use Openclaw Pr Maintainer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/openclaw/tree/main/.agents/skills/openclaw-pr-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 Openclaw Pr 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 Openclaw Pr Maintainer?

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

Is the Openclaw Pr Maintainer AI skill free?

It is published on GitHub by openclaw. Check the repository for licensing terms. 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 👇