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experiment-audit

Audit experiment integrity before claiming results. Uses fresh-agent GPT-6-Astra review (same-family provisional in the base Codex mirror) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.

Quality

77%

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Fix and improve this skill with Tessl

tessl review fix ./skills/skills-codex/experiment-audit/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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 delivers a genuinely actionable, well-sequenced audit workflow with a rigorous adversarial reviewer prompt and complete output templates. Its weaknesses are verbosity from duplicated report formats and heavy provenance bookkeeping, an internal contradiction in Step 1 about reading vs. not reading files, and reliance on out-of-bundle shared-references with no in-bundle reference structure.

Suggestions

Consolidate the EXPERIMENT_AUDIT.md and EXPERIMENT_AUDIT.json templates: keep one canonical schema and derive the other from it, removing redundant fields (verdict/overall_verdict, date/generated_at, auditor/reviewer_model) to cut roughly 40 lines.

Resolve the Step 1 contradiction: explicitly permit reading eval scripts solely to extract ground-truth paths (e.g., 'grep for dataset/GT loading lines only; do not summarize content'), so 'collect paths only' and 'look in eval scripts for data loading' are consistent.

Move the reviewer prompt template and the report templates into bundled reference files (e.g., references/reviewer-prompt.md, references/report-template.md) and list them in a clearly signaled navigation section, so SKILL.md becomes an overview and the shared-references pointers are verifiable.

DimensionReasoningScore

Conciseness

The body is mostly concrete (audit checklist, reviewer prompt), but the dual report templates duplicate each other — "Parse the reviewer's response and write EXPERIMENT_AUDIT.md" followed by a full JSON block whose fields overlap ("verdict" and "overall_verdict", "date" and "generated_at", "auditor" and "reviewer_model") — and the dense opening blockquotes ("Before each reviewer slot, follow [Selectable Reviewers]...") could be tightened. Matches 'mostly efficient but includes some unnecessary explanation or could be tightened'; not 2 because no section explains concepts Claude already knows.

3 / 5

Actionability

Concrete guidance throughout: exact globs ("*eval*.py, *metric*.py, *test*.py, *benchmark*.py"), a fully specified reviewer prompt ("model: gpt-6-astra / reasoning_effort: ultra" plus a complete A-F audit checklist), and copy-paste report templates — matching 'mostly executable guidance; concrete code or commands with minor gaps'. Not 5 because the actual reviewer invocation is never shown as a real tool call ("spawn_agent:" is a sketch referencing out-of-bundle contracts), leaving '[list paths]' placeholders without a resolution step.

4 / 5

Workflow Clarity

Four clearly sequenced steps (collect artifacts → send to reviewer → parse and write report → print summary) with error-recovery handling ("unavailable selections are REVIEW_UNAVAILABLE", "emit BLOCKED/ERROR rather than a provisional PASS") — matching 'clear sequence with most checkpoints present; minor validation gaps'. Not 5 because Step 1 instructs "look in eval scripts for data loading" while simultaneously demanding "DO NOT summarize, interpret, or explain any file content", an internal contradiction, and malformed/unparseable reviewer responses are not handled.

4 / 5

Progressive Disclosure

The ~300-line body inlines the full reviewer prompt and two complete report templates that belong in reference files, while pointing to external files not present in the bundle ("../shared-references/reviewer-backends.md", "shared-references/reviewer-independence.md", "reviewer-routing.md", "review-tracing.md", "integration-contract.md") that are buried in blockquotes and inline prose rather than clearly signaled — matching 'some structure but could be better organized; references present but not clearly signaled; content that should be separate is inline'. Not 4 because no bundled reference files exist to verify and the navigation structure is not laid out as an overview.

3 / 5

Total

14

/

20

Passed

Description

83%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.

A strong description: it clearly states what the skill does (four concrete integrity checks), when to use it (explicit bilingual trigger phrases plus a pipeline hook), in third person, with good distinctiveness. Its only weakness is the jargon-laden parenthetical about the reviewer model and same-family provisional status, which hurts readability without adding user-relevant information.

DimensionReasoningScore

Specificity

Quotes: "check for fake ground truth, score normalization fraud, phantom results, and insufficient scope" — four concrete, domain-specific check targets, matching the 'several specific actions; minor gaps in coverage' anchor. Not 5 because the parenthetical "same-family provisional in the base Codex mirror" is opaque jargon that dilutes the action list; not 3 since four enumerated fraud patterns exceed '1-2 concrete actions'.

4 / 5

Completeness

Quotes: what — "Audit experiment integrity... to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope"; when — "Use when user says '审计实验', 'check experiment integrity', 'audit results', '实验诚实度', or after experiments complete before writing claims." Both are explicit with concrete trigger phrases, matching the top anchor exactly; 4 would require the when-clause to be less explicit or specific.

5 / 5

Trigger Term Quality

Quotes: "check experiment integrity", "audit results", "审计实验", "实验诚实度", "after experiments complete before writing claims" — good natural-phrase coverage with bilingual synonyms, matching 'good keyword coverage; a few natural terms missing'. Not 5 because common variants like "verify results" or "are my results real" are absent; not 3 since both English and Chinese user phrasings plus a workflow trigger are present.

4 / 5

Distinctiveness Conflict Risk

Quotes: "Audit experiment integrity before claiming results" — a clear niche with distinct triggers, matching 'mostly distinct; minor overlap risk with closely related skills'. Not 5 because the standalone trigger "audit results" is generic enough to overlap with code-review or general audit skills; not 3 since the experiment-fraud domain and its trigger phrases are unmistakable.

4 / 5

Total

17

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

13

/

16

Passed

Repository

wanshuiyin/Auto-claude-code-research-in-sleep

Path

skills/skills-codex/experiment-audit/SKILL.md

Reviewed

Table of Contents

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