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auto-qa

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

58

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./.agents/skills/auto-qa/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

73%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-structured, highly actionable campaign playbook with strong validation feedback loops and clean progressive disclosure into real reference files. Its main weakness is verbosity from repeated philosophy and long qualifying sentences that could be tightened.

DimensionReasoningScore

Conciseness

The body is mostly substantive operational guidance rather than basic-concept padding, but the root-cause-vs-quick-fix philosophy repeats across several sections and many long qualifying sentences could be tightened, fitting 'mostly efficient but could be tightened'; not 2 because the bulk is genuine domain detail.

3 / 5

Actionability

Concrete commands and identifiers abound — 'git -C <checkout> rev-parse HEAD', 'codex exec --sandbox read-only --ephemeral', 'ps -p <pid-list>', named workflows like '$autoreview', and real reference paths — giving mostly executable guidance with only minor gaps from placeholder-style '<...>' forms.

4 / 5

Workflow Clarity

The 'Turn findings into verified fixes' section is a 9-step sequence with explicit validation checkpoints (exact-SHA + clean-content guards, discard-on-failure, fresh $autoreview, verify merge SHA before incrementing the ledger) and feedback loops, matching the top anchor despite this being a high-risk batch/merge operation.

5 / 5

Progressive Disclosure

Detailed materials are split into four real, one-level-deep reference files (campaign-evidence.md, evidence-ledger.md, subsystem-lanes.md, live-proof-routing.md) signaled with markdown links, but some dense operational paragraphs remain inlined in SKILL.md, leaving minor organization gaps versus the top anchor.

4 / 5

Total

16

/

20

Passed

Description

62%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description clearly conveys what the skill does and occupies a distinct niche, but it lacks an explicit 'when to use' trigger clause and leans on internal jargon over natural user phrasing. Adding a 'Use when...' sentence with everyday synonyms would lift the weaker dimensions.

Suggestions

Append a 'Use when...' clause naming natural trigger phrases users would say (e.g. 'Use when the operator asks for an autonomous QA sweep, stress/load testing, or a merged root-cause fix campaign across OpenClaw subsystems').

Add plain-language synonyms alongside the jargon (e.g. 'bug-fix campaign', 'stress/load testing', 'regression sweep') so trigger-term quality covers natural variations.

Keep the third-person voice but tighten 'independent subsystem lanes' toward user-facing language to reduce overlap with internal terminology.

DimensionReasoningScore

Specificity

Lists several concrete actions — 'Run...autonomous QA and live/stress campaigns', 'verified fixes', 'resumable evidence report' — with only minor coverage gaps, matching the 'lists several specific actions' anchor; not 5 because it is not a comprehensive enumeration of actions.

4 / 5

Completeness

It has a clear 'what' but no explicit 'Use when...' trigger clause; per the judging guidelines a missing explicit trigger clause caps completeness at 3.

3 / 5

Trigger Term Quality

Relevant domain keywords ('autonomous QA', 'live/stress campaigns', 'subsystem lanes') are present but the phrasing is jargon-heavy and missing the natural synonyms a user would actually say, fitting 'some relevant keywords but missing common variations'.

3 / 5

Distinctiveness Conflict Risk

The OpenClaw-specific QA-campaign framing is a clear niche with distinct triggers and minimal conflict risk with other skills, matching the top anchor.

5 / 5

Total

15

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
openclaw/openclaw
Reviewed

Table of Contents

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