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무료GitHub에 공개한국어 소개

auto-qa

OpenClaw 전반의 품질을 지속적으로 검증합니다. 결함 재현부터 근본 원인 수정, 검토, 반영 확인까지 진행하고 실제 환경과 부하 검증 결과를 재개 가능한 보고서에 기록합니다.

원문원문 설명 보기

Run OpenClaw-wide autonomous QA and live/stress campaigns across independent subsystem lanes, with verified fixes and a resumable evidence report.

연동 도구Docker
AI 평가90점/ 100설치 방법 보기

이럴 때 유용합니다

  • OpenClaw 여러 영역의 결함을 조사할 때
  • 회귀 테스트와 독립 검토로 수정을 검증할 때
  • 실제 연결, UI, 배포 패키지를 확인할 때
  • 부하 검증을 기록하고 조사를 재개할 때

한국어 소개

할 수 있는 일

OpenClaw 전반의 품질 검증을 지속하며 결함 발견부터 수정 반영 확인까지 관리합니다. 여러 영역을 독립적으로 조사하고 현재 main에서 문제를 재현해 근본 원인과 관련 코드를 수정합니다. 회귀 테스트, 독립 검토, 실제 환경 및 부하 검증을 결합합니다. 근거와 진행 상황을 재개 가능한 보고서에 남기며 가설, 검증된 수정, 실제로 병합된 수정을 구분합니다.

이럴 때 유용합니다

OpenClaw를 폭넓게 점검하거나 장기적인 품질 개선을 근거와 함께 추적할 때 적합합니다. 제공업체 연결, Control UI, 배포 패키지 등 실제 사용 경로도 검증합니다.

사용 예시

  • “OpenClaw 품질 검증을 시작하고 근거와 진행 상황을 보고서에 남겨 주세요.”
  • “현재 main에서 이 결함을 재현하고 근본 원인을 수정해 주세요.”
  • “이전 보고서에서 검증을 재개해 주세요.”

주의 사항

기본 목표는 독립적으로 검증되고 실제 병합된 근본 원인 수정 100건입니다. 병렬 실행이 허용되면 조사 영역을 10개 이상 유지합니다. 관련 스킬과 검증 환경이 필요하며 네트워크 사용과 병합에는 별도 허가가 필요합니다. 기기나 인증 등 전제 조건이 부족한 검증은 실행 불가로 기록합니다. 검토가 필요한 변경은 승인 전까지 달성 건수에 포함하지 않습니다.

이 소개문은 공개된 SKILL.md를 바탕으로 AI(OpenAI Codex)가 작성했습니다. 정확한 사양은 아래 원문을 확인하세요.

객관 평가

AI가 SKILL.md의 내용만을 근거로 4가지 관점에서 판정했습니다. 스타 수나 인기도는 판정에 넣지 않았습니다.

지시의 명확성언제 쓰고 무엇을 하는지 알 수 있는가
A양호현재 main에서 지속 검증하며 독립 검증과 병합을 마친 수정 100건을 목표로 명시합니다.
절차의 구체성절차·입출력·예시가 구체적으로 적혀 있는가
A양호작업 트리의 커밋 해시와 변경 여부 확인 명령, 보고서와 번호별 기록 절차를 제시합니다.
안전성전송·삭제·명령 실행 등을 명시하고 있는가
A양호네트워크와 병합 허가를 구분하고 민감한 변경은 담당자 승인과 확인 후에만 완료로 집계합니다.
도입의 용이성필요한 도구나 API 키가 적고 적혀 있는가
B일부 부족여러 관련 스킬, codex, 검증 환경을 전제로 하지만 설정 안내는 일부만 있습니다.

총점은 각 항목을 A=25점, B=15점, C=5점으로 더한 값입니다. 2026년 10월 19일 기준으로 AI(OpenAI Codex)가 판정했습니다. 참고용이므로 설치 전에 원문도 확인해 주세요.

평가가 다르다고 생각되면 재검토를 요청하기

어느 항목이 왜 다르다고 생각하는지 적어 주세요. 로그인은 필요 없습니다. SKILL.md와 대조해 평가를 다시 만들고 이곳에 반영합니다.

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

포함된 파일(6)

  • SKILL.md16.8 KB
  • agents/openai.yaml318 B
  • references/campaign-evidence.md4.3 KB
  • references/evidence-ledger.md3.3 KB
  • references/live-proof-routing.md3.6 KB
  • references/subsystem-lanes.md4.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

OpenClaw Auto QA

Run a continuous, current-main OpenClaw product campaign. Treat a reviewer finding as a hypothesis, a passing test as evidence only for its actual head, and a merge as complete only when the canonical repository confirms it. Always prefer a clean, appropriately scoped root-cause refactor over a quick fix or smaller diff. Repair the actual root cause in its canonical owner; a patch is not acceptable when it leaves sibling paths, lifecycle invariants, or the defective abstraction intact.

Set the campaign goal

  • Default to 100 distinct, independently verified, actually merged root-cause fixes unless the operator explicitly specifies a different target. Count broken invariants, not pull requests, symptoms, proposed patches, skill updates, or merely passing tests.
  • When the operator changes the target during an active campaign, update the existing report, numbered ledger, progress denominator, and live status in place. Preserve every previously verified landing; never reset the investigation or claim that an unfinished target has been reached.
  • Exclude security-sensitive, persistent-state, protocol, public-contract, or otherwise review-required work from the accepted count until the appropriate owner explicitly authorizes and verifies its landing.

Prefer clean refactors over quick fixes

  • Identify the broken ownership boundary, abstraction, state transition, or dependency contract before choosing an implementation. Compare the canonical owner, callers, callees, and sibling paths; prefer the design that makes their shared invariant obvious and reliable.
  • Consolidate decisions and authoritative state in their actual owner. Propagate prepared facts through existing lifecycles, repair all affected siblings, and delete obsolete branches, duplicate policy, dead helpers, and stale abstractions when they are no longer needed.
  • Reject symptom-masking guards, one-off exceptions, observed-example literals, parallel code paths, extra caches, fallback stacks, compatibility shims, and tests that merely make a narrow reproduction pass. A smaller change is not safer when it preserves the cause or makes the architecture harder to understand.
  • Preserve shipped public contracts and ownership boundaries. If the clean refactor would affect security, persistent state, public configuration, plugin SDK compatibility, a protocol, or a product decision, mark it for maintainer review instead of substituting a tactical patch.
  • During independent review, explicitly ask whether the change is the cleanest appropriately bounded root-cause solution. Green tests, a minimal diff, and a plausible local fix are insufficient without that architectural judgment.

Start with the moving source

  1. Read all of root AGENTS.md, then each scoped guide for the lanes under inspection. Read the current skill for a specialized workflow instead of reproducing stale instructions:
    • $openclaw-repair-sweep for bug acceptance and duplicate handling.
    • $openclaw-testing for actual test and CI selection.
    • $openclaw-qa-testing for QA Lab, scenario catalogs, and real provider lanes.
    • $control-ui-e2e for browser and Control UI proof.
    • $crabbox for remote, Docker, packaged, cross-platform, and live proof.
    • $autoreview for fresh independent review before publishing or landing.
    • $openclaw-pr-maintainer for authorized maintainer-side GitHub actions.
  2. Identify the actual canonical main checkout; a desktop or linked worktree can have a different detached HEAD. Check git -C <verified-canonical-main-checkout> status -sb and record git -C <verified-canonical-main-checkout> rev-parse HEAD. When network access is authorized, let only the orchestrator refresh origin/main, record its full SHA, and prove the canonical checkout matches before inspecting source. Give every read-only reviewer the absolute clean checkout or its own verified exact-head worktree. Before inspection and immediately before accepting results, require both the exact git -C <worker-checkout> rev-parse HEAD and empty output from git --no-optional-locks -C <worker-checkout> status --porcelain=v1 --untracked-files=all --ignore-submodules=none; alternatively, read immutable files directly from the frozen Git object. Discard the complete wave item when either verification fails. Keep intentionally modified implementation worktrees separate from read-only frozen-source review. For offline work, disclose that remote freshness is unverified. Never pull, rebase, or switch another agent's checkout.
  3. Make one requested, fresh codex/ worktree per implementation task. Keep reviewer workers read-only. Serialize all shared origin/main refreshes through the orchestrator; pause worker fetches before repository-native PR review, preparation, or landing. After a successful landing, fetch origin/main again, verify the recorded merge commit is an ancestor of that fetched ref, and only then broadcast its full immutable SHA and resume workers. Revalidate candidates against each new head without changing a sibling's active worktree. When offline, disclose that remote freshness is unverified.
  4. Initialize or resume the user-requested Markdown report and numbered ledger. Read references/campaign-evidence.md and references/evidence-ledger.md before accepting a finding. If the operator increases the target, update the active denominator and existing numbered ledger without erasing historical time-stamped progress.

Keep at least ten lanes active

When independent worker execution is authorized, keep at least ten materially different subsystem investigations in flight throughout the active campaign. Replace completed, failed, or stale workers promptly. Network authorization separately determines whether a lane may fetch, invoke an externally hosted model, or contact a provider; it does not prohibit authorized local subagents. If independent workers are unavailable or forbidden, record the concurrency requirement as blocked rather than claiming sequential reviews are concurrent. Start with the OpenClaw-specific lane map in references/subsystem-lanes.md. Split a large area into narrow, independent ownership surfaces instead of giving one worker the entire gateway, provider, UI, or app tree.

Use first-class subagents when available and bounded codex exec --sandbox read-only --ephemeral reviewers when agent slots are exhausted; verify the installed CLI's supported options with codex exec --help. Keep CLI workers under an actually surviving parent session or durable supervisor; background children started by a shell that immediately exits are not active workers. Independently check the exact child PIDs and command identity with ps -p <pid-list>, and record the observation time. Give each worker the absolute verified checkout, initial and final exact-SHA and clean-content guards, frozen SHA, one subsystem and its scoped guide, a bounded duration, and the required evidence shape. Record running, successfully completed, failed, timed-out, and stale-guarded workers separately; replace finished workers before claiming continued concurrency. Ask for source, at least one caller and callee, sibling behavior, regression tests, current-main reproduction, upstream dependency proof when relevant, severity, and duplicate references. Do not disclose a proposed diagnosis to an independent verifier. When independent workers are unavailable, disclose the actual limitation; never represent launched, planned, finished, shell-discarded, or stale workers as running.

Observe CPU, memory pressure, disk, open ports, actual worker count, and gateway health between waves. Scale to the machine and operator-authorized load rather than mechanically starting 64 workers or treating a brief load-average spike as failure. Reduce campaign concurrency for sustained memory pressure, gateway failures, process starvation, or an actual operator limit. Keep remote proofs serialized per Testbox lease; never reclaim, sync, or launch another command while that lease has an active command. When a local process session disappears, recover the authoritative remote job and exact exit before retrying or claiming a pass. Use bounded retries and timeouts. Stop only campaign-owned processes.

Prove actual product behavior

Read references/live-proof-routing.md before invoking a provider, private QA build, remote lease, packaged install, or native app.

  • Use isolated campaign state and free localhost ports for dev gateways. Never stop, restart, reconfigure, migrate, or bind the operator's running gateway, app, device, state directory, or default gateway port.
  • Classify source trust before execution. Do not execute untrusted contributor scripts, hooks, configuration, tests, or package installation on a local or credential-hydrated machine.
  • Use the existing service-account or provider credential only through the approved secret-backed workflow. Never print, log, commit, echo, export into an untrusted worker, or include credentials in test artifacts.
  • For an OpenAI claim, first verify the gateway's real configured agents.list, selected existing agent, canonical openai/<model>, and a real model response. Prove both the delivered model-final path and the separately persisted transcript or session; one passing route does not establish the other. An unavailable provider, unknown agent, mock, skipped test, fallback response, or an earlier head is not live proof.
  • Inspect the actual rendered Control UI and screenshot contents. Reject a page or screenshot containing GatewayRequestError, UNKNOWN_AGENT, or an equivalent gateway error even when browser navigation, the HTTP status, or the image-capture command succeeds.
  • Derive QA scenario IDs from current qa/scenarios/index.yaml and scenario YAML. Inspect the actual harness and generated summary. Count a scenario only when the requested run reports a nonzero total, zero failures, and the exact model, provider mode, and relevant behavior.
  • Cap an individual live subagent-fanout scenario at 780 seconds after startup. Separately verify setup, timeout recovery, the original parent, child completion, and the final nonzero scenario result.
  • For installed-package or plugin bugs, run the real current packaging and user install/update path. Unit tests alone do not prove a built distribution, npm package, Docker image, or Git plugin.
  • Test iOS, Android, and macOS only where the current host, device, simulator, signing identity, emulator, or hosted proof actually supports the operation. Record unavailable prerequisites rather than manufacturing mobile or UI coverage.
  • Bound stress and soak tests, record the immutable gateway source, owned PID, exact start time, successful/failed/skipped counts, and actual completion time. Preserve an already-running campaign-owned soak at its original source while auditing newer main snapshots; never restart it or describe a ten-hour soak as complete before ten actual elapsed hours.

Turn findings into verified fixes

  1. Deduplicate against the current ledger, origin/main, current open and merged GitHub work, and sibling root causes. Count one broken invariant once, even when it produces multiple model, platform, route, lifecycle, or UI symptoms.
  2. Independently reproduce the actual current-main user path. Map the entry point, canonical owner, callers, callees, sibling implementations, state lifecycle, existing regressions, shipped contracts, and relevant direct upstream source. Identify why the current design fails before proposing a repair.
  3. Refactor the canonical owner in an isolated worktree. Repair all affected sibling paths in the same coherent change, simplify or remove the defective abstraction, and carry authoritative facts through the existing lifecycle. Prefer the cleanest appropriately sized root-cause solution over a minimal diff; reject a guard, special case, extra cache, fallback, compatibility shim, or narrowly passing test that leaves the architectural defect behind.
  4. Preserve public configuration, plugin ownership, gateway protocol, migrations, provider contracts, persistent state, and external dependencies. When a correct root-cause repair would change a sensitive contract or requires a product decision, prepare it for operator review; do not disguise that risk as a small autonomous fix.
  5. Add authentic regression coverage for the original reproduction, affected siblings, lifecycle cleanup, and unchanged legitimate behavior. Run appropriately scoped proof on the exact candidate head. Follow $openclaw-testing host routing: trusted development checks run locally; remote proof needs an environment or source-isolation reason. Use the existing specialized workflow when that capability is required. Inspect actual exit status, nonzero scenario counts, and artifacts.
  6. Run a fresh $autoreview on the complete final refactor. Require the reviewer to compare owner boundaries and sibling implementations, confirm this is the best clean root-cause solution, and reject quick-fix residue even when tests pass. Resolve verified actionable findings and re-review substantive changes or unresolved concerns. Address actual human/bot findings and keep the PR body current; mechanical head movement and bot scores do not require another review pass.
  7. Check existing open PRs, current author counts, and the actual repository automation before publishing. Read both the current labeler and response policy; verify the authenticated author association, repository permission, account type, automation branch prefix, and actual override label. Apply only exemptions proved by that current policy, including eligible owners, maintainers, collaborators, bots or apps, approved automation branches, and explicit overrides. Never infer capacity from a truncated list or assume that one privileged role represents every exemption. Reuse and repair an existing candidate PR for the same cause. When a real cap applies, hold reviewed worktrees and finish or land existing verified work first.
  8. Create a focused PR with the repository's actual template, canonical cause, user impact, frozen head, completed proof, and risk. Use only the current repo-native scripts/pr review, artifact, prepare, and merge workflow for authorized main landing.
  9. Autonomously merge only when the user authorized it and the canonical root-cause refactor is individually reproduced, low-risk, independently reviewed, current-main-compatible, and has green required exact-head proof. Evaluate risk by ownership and behavioral impact, not by whether the diff is the smallest possible. Verify the resulting canonical merge SHA before incrementing the ledger. Immediately report each verified landing to the operator with its pull-request URL and one or two concise sentences describing the actual root causes fixed; do not batch, defer, or announce unmerged pull requests as landed.

Hold a merge only for a concrete, nameable reason: an open defect; pending, skipped, stale, conflicting, or failing proof; an uncertain diagnosis; a product, public-contract, or design choice the maintainer has not made yet; or a security or authentication change that needs its listed owner. When the maintainer has already chosen the design (in chat or a recorded decision) and review and exact-head proof are green, land it autonomously; size, persistent state, or refactor breadth alone are not reasons to stop. Never park a ready PR, or write a PR body, that only asks for sign-off. If a review bot asks for approval of a decision the maintainer already made, record that decision through the native path (for example @clawsweeper approve) and continue. Mark genuinely undecided changes user review required with the specific question, and keep them outside the accepted-bug count.

Maintain accurate campaign state

Update the requested report throughout the campaign, not only at the end. Follow references/campaign-evidence.md for campaign and active-lane evidence and references/evidence-ledger.md for verified bug states. Separate discovered hypotheses, reproduced bugs, review-required PRs, exact-head validated fixes, and actually merged fixes. Report the frozen main SHA, worker lanes, resource use, live model and gateway proof, soak timestamps, actual failures, independent review, CI run IDs, PR links, and canonical merge SHAs.

Count only distinct, verified, authorized, actually merged low-risk root-cause fixes toward the operator's current explicit target. Never count the Auto QA skill PR, observations, pending PRs, skipped checks, existing unrelated merges, individual symptoms of the same defect, hypotheses, or review-required fixes. Keep iterating on this skill from real OpenClaw campaign evidence in a separate skill-only worktree and PR.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

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이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

1password

무료

Set up and authenticate the 1Password CLI, then supply stored passwords and API keys to commands or configuration. The skill covers desktop integration, service accounts, and interactive sign-in while keeping secrets out of chat and logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Setting up the 1Password CLI
  • Supplying API keys to commands
  • Using service account authentication in CI
openclaw/openclaw39.2만2026년 10월 18일 업데이트

acp-router

무료요약 있음AI 평가 80점

OpenClaw에 전달한 자연어 요청을 Claude Code 같은 외부 코딩 에이전트로 연결합니다. ACP나 acpx로 작업을 시작하거나 이어가며 대화 맥락을 유지합니다.

  • 스레드에서 Claude Code 시작
  • 외부 에이전트의 기존 작업 계속
  • acpx로 직접 지시 전달
openclaw/openclaw39.2만2026년 10월 18일 업데이트

Add a model provider to OpenClaw and verify it with a real response. The skill validates configuration, keeps credentials in environment or file references, and reports test results and any default model changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding a model provider to OpenClaw
  • Configuring credentials through environment or file references
  • Checking whether a model returns a real response
openclaw/openclaw39.2만2026년 10월 18일 업데이트

Prepare an agent activity transcript for a GitHub pull request or issue. Extract relevant prompts, decisions, and results from local logs, remove sensitive material, and preview the edited record before separately authorized publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preparing an agent activity record for a pull request
  • Extracting task-relevant prompts and results from local logs
  • Previewing a transcript with sensitive information removed
openclaw/openclaw39.2만2026년 10월 18일 업데이트

Manage Apple Notes on macOS through memo, a command-line tool. Create, browse, search, edit, delete, and move notes between folders, or export selected notes to HTML or Markdown for use outside the app.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Creating notes with titles
  • Finding notes by folder or approximate search
  • Editing notes and organizing them into folders
openclaw/openclaw39.2만2026년 10월 18일 업데이트

apple-reminders

무료요약 있음AI 평가 70점

Mac에서 Apple Reminders의 기한이 있는 할 일을 확인하고 추가·편집합니다. 목록 관리와 완료·삭제를 통해 iPhone이나 iPad에서 보는 작업을 정리합니다.

  • 오늘 할 일과 기한이 지난 작업 확인
  • 기한이 있는 개인 할 일 추가
  • iPhone과 iPad에서 보는 작업 정리
openclaw/openclaw39.2만2026년 10월 18일 업데이트

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