Openclaw Repair Sweep logo

Openclaw Repair Sweep

OrganizationPopular
openclaw
openclaw-repair-sweep

Run scoped OpenClaw issue/PR repair campaigns: coordinate workers, prove root causes, and land or close verified work under the requested authority.

Overview

Publisheropenclaw
Repositoryopenclaw
Skill nameopenclaw-repair-sweep
Stars
391K
Forks
82.2K
Bundled files
1
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 openclaw on GitHub. Read the source before you install it.

Installation

Install the Openclaw Repair Sweep 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-repair-sweep
  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-repair-sweep .claude/skills/openclaw-repair-sweep
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openclaw Repair Sweep 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 Repair Sweep 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 Repair Sweep 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 Repair Sweep

Use for a multi-item repair campaign. Root AGENTS.md owns repair and safety policy; $openclaw-pr-maintainer owns item review, GitHub writes, and landing. The lead remains hands-on and delegates independent work where useful.

Scope and authority

  • Explicit refs imply a bounded ref campaign. Otherwise discovery defaults to five qualified items (maximum twenty per batch); a whole-queue request means continuing through that queue. Do not pad low-confidence findings.
  • Honor requested workers and focus. Ref/discovery campaigns normally use up to eight item owners; a queue campaign can assign up to sixty-four, but actual active proof workers must fit host/remote capacity. Start small and scale on observed resource health; assigned is not the same as running.
  • An explicit autonomous fix-and-land/sweep invocation covers scoped investigation, repairs, commits, pushes, PRs, verified landings and proven closures. Review, triage, or list wording stays read-only; fix-only stops before publication. Never infer release, schema/config/protocol, credential, security, or product approval from campaign authority.

Use $gitcrawl for available archive discovery, $openclaw-testing for proof, $autoreview for independent code review, and $crabbox when the proof requires remote environment or untrusted-source isolation. Author-history research is conditional on a concrete question, not a campaign prerequisite.

Coordinate ownership

Assign each item or shared root-cause cluster one owner, a checkout, frozen source SHA, authorized actions, and a useful stopping condition. Duplicate symptoms share an owner; repeated defect classes should be repaired in the canonical owner rather than by parallel near-identical patches.

Use isolated issue/PR worktrees and preserve unrelated files. Serialize shared fetch/ref/branch/worktree operations, PR preparation, and merges in short slots; never hold the slot across coding, tests, or a remote wait. Each remote lease has one owner and one active command. Pause only campaign-owned work under resource pressure, and preserve patches, claims, proof, and checkpoints before replacement. The lead may take over stalled work safely; it is not an orchestration-only role.

Read root and scoped guides before acting. Each verdict-bearing worker inspects its relevant dependency contracts directly, including the root's personal Codex source requirement. The lead verifies consequential results, not just summaries. Use independent challenge for nontrivial repairs and closure evidence; one independent autoreview is enough for the code-review requirement.

Investigate and repair

Search existing work before creating another fix: current body/comments, related archive results, live state when it matters, and current source/history. Use bare PATH gh with narrow JSON fields; reuse shared evidence and avoid unnecessary pagination or repeated item fetches.

Prefer, in order:

  1. Prove the original behavior is already fixed on current main and close under the authorized scope with the canonical fix and evidence.
  2. Finish and verify an existing useful editable PR, preserving human credit.
  3. If that PR cannot safely be updated, create a credited replacement before closing it. Do not squash a contributor PR after replacing its ancestry.
  4. Implement a new high-confidence repair when no suitable fix exists.
  5. Record a concrete blocker or product/owner decision. A useful independent simplification may land under its own scope, but it does not close the bug.

A candidate needs a demonstrated current defect, an understood owner and bounded impact, relevant dependency proof, and feasible validation. Repairs requiring new dependencies/configuration, public-contract changes, security, persistence design, or product judgment require their root approvals before implementation or landing as applicable. Preserve useful investigation; do not work around the gate or count review-required work as an accepted fix.

Repair the invariant at its owner, cover relevant siblings, and remove connected obsolete paths when justified. Do not add speculative fallback layers or hardcode the reported example. Prefer simplicity without a LOC quota. Keep release-note context and credit in the PR; CHANGELOG.md remains release-owned. Do not force a second refactor PR after each completed item.

Proof and landing

Choose proportional proof through $openclaw-testing. Trusted development can run locally; remote compute needs an environment or isolation reason. Untrusted repository tooling never runs locally or on a credential-hydrated host. Preserve explicit live requirements and never describe a mock, skipped test, old head, or unfinished soak as live proof.

Run $autoreview on the completed nontrivial candidate and resolve verified findings. Re-review substantive changes or unresolved concerns; do not multiply reviews for mechanical head movement. Address actionable human/bot findings without a separate Rank-up checklist or score-refresh gate.

Land through the native maintainer workflow only with current required CI and review evidence. Verify remote merge state and ancestry before counting success. A timed-out merge request may have succeeded; reconcile it before retrying. Refresh/rebase for conflicts, failed guards, explicit requests, or material stale base risk, not merely because main advanced. Delete only owned temporary state.

For broad campaigns, a shared-main failure may justify one separately scoped repair worker. Check for an existing fix first. Do not duplicate that repair in every PR or silently expand a bounded ref request to unrelated CI work.

Closure and reporting

Before closing, prove the reporter's primary outcome and affected surfaces are fixed, not merely mitigated or diagnosed. Check fix ancestry and containing tags when version claims matter; dates do not prove inclusion. Seek independent challenge for uncertain or consequential closure evidence. Disagreement, a still broken surface, or an owner hold means leave open. Product rejection is a maintainer decision, never an automatic cleanup conclusion.

Recheck live state before mutation and explain the fixed behavior, canonical commit/PR, known containing version, and useful proof. If a closure is challenged, pause the affected closure work and correct the evidence before resuming. No more than three workers should hold simultaneous closure duty; more than fifty close/reopen actions require the root's explicit count/scope approval.

Keep a resumable ledger of owner, source/head, outcome, proof/CI state, PR/merge, credit, blockers, and cleanup. Count verified outcomes, not planned or launched work. Report meaningful progress and each verified landing with links; summarize final behavior, proof, remaining limitations, and checkout state in natural prose.

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 Repair Sweep AI skill do?

Run scoped OpenClaw issue/PR repair campaigns: coordinate workers, prove root causes, and land or close verified work under the requested authority.

Why use Openclaw Repair Sweep on TypingMind?

Because you install it once and use it with any model. Openclaw Repair Sweep 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 Repair Sweep in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/openclaw/tree/main/.agents/skills/openclaw-repair-sweep. 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 Repair Sweep?

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 Repair Sweep?

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

Is the Openclaw Repair Sweep 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 👇