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multi-ai-verification

Multi-layer quality assurance with 5-layer verification pyramid (Rules → Functional → Visual → Integration → Quality Scoring). Independent verification with LLM-as-judge and Agent-as-a-Judge patterns. Score 0-100 with ≥90 threshold. Use when verifying code quality, security scanning, preventing test gaming, comprehensive QA, or ensuring production readiness through multi-layer validation.

Quality

77%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/multi-ai-verification/SKILL.md

The canonical home for this skill is multi-ai-verification in fernandezbaptiste/Skrillz

SKILL.md
Quality
Evals
Security

Quality

Content

62%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 body delivers a genuinely clear, well-gated 5-layer workflow with concrete commands and explicit recovery loops, but it pays for that with severe length: fabricated example reports, duplicated rubrics, and a tool catalog Claude already knows inflate it far beyond token efficiency. The monolithic single-file structure with no external references compounds the context-window cost. The main improvement is moving appendices and report templates to reference files and stripping fictional filler data.

Suggestions

Move Appendix B (scoring rubrics), Appendix C (tool catalog), and the per-layer sample report templates into files under references/ and keep SKILL.md as a lean overview with clearly signaled one-level-deep links.

Replace the fabricated filled-in report examples ('245/245 tests passing', bcrypt rounds gap analysis, '88/100') with empty report skeletons or short format descriptions — fictional results pad tokens without adding guidance.

Delete the Appendix C tool list (ESLint, mypy, Playwright, etc. — knowledge Claude already has) and the Quick Reference table that restates per-layer automation/time/tool details already covered in Operations 1-5.

DimensionReasoningScore

Conciseness

The ~1,010-line body is noticeably verbose: fully fabricated sample reports ('✅ 245/245 tests passing', 'Weak password hashing rounds', '88/100'), a Quick Reference table duplicating per-layer details already given, Appendix B rubrics repeating scoring detail from Operation 5, and Appendix C cataloging tools (ESLint, mypy, Playwright) Claude already knows. The padding exceeds anchor 3's 'some unnecessary explanation' but the material remains largely actionable rather than concept-explanation, so anchor 1 does not fit.

2 / 5

Actionability

Provides mostly executable guidance: concrete commands ('ajv validate -s plan.schema.json -d plan.json', 'npx tsc --noEmit', 'npx semgrep --config=auto src/', 'bandit -r src/', 'npx pa11y http://localhost:3000') and explicit numeric gates (coverage ≥80%, score ≥90). Minor gaps keep it below anchor 5: the 'task({...})' blocks are pseudocode of the Task tool and 'npx axe-core src/' is not a real CLI invocation.

4 / 5

Workflow Clarity

Clear 5-layer sequence where each operation has purpose, process, outputs, and a validation checklist, with explicit per-layer gates and an explicit failure feedback loop ('Failed Gate → Gap Analysis → Apply Fixes → Re-Verify → Repeat Until Pass'). This matches anchor 5's explicit validation steps, feedback loops, and checklists.

5 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent), so everything — including ~300 lines of Appendix B rubrics, the Appendix C tool catalog, and per-layer report templates — is inlined in one ~1,000-line file. Section headers provide some structure per anchor 3, but content that clearly belongs in separate reference files is inline and the only 'references' are self-pointers ('For rubrics, see Appendix B'), so anchors 4-5 do not apply.

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 that clearly communicates both capability and trigger conditions with concrete, quantified details. Its main weaknesses are minor: a few natural trigger synonyms ('code review', 'testing') are absent, and some phrases overlap with adjacent code-review/security skills. Third-person voice is used correctly throughout.

DimensionReasoningScore

Specificity

Lists several specific concrete actions — '5-layer verification pyramid (Rules → Functional → Visual → Integration → Quality Scoring)', 'LLM-as-judge and Agent-as-a-Judge patterns', 'Score 0-100 with ≥90 threshold' — with only minor gaps (linting and test execution are not named as actions). It goes beyond anchor 3's '1-2 concrete actions' but stops short of anchor 5's comprehensive coverage.

4 / 5

Completeness

Clearly answers both 'what' (multi-layer verification pyramid with LLM-as-judge and Agent-as-a-Judge patterns, 0-100 scoring with ≥90 threshold) and 'when' (explicit 'Use when' clause with five concrete trigger phrases). This matches anchor 5 exactly; the 'when' is explicit and specific, so anchor 4's 'could be more explicit' does not apply.

5 / 5

Trigger Term Quality

Includes natural phrases users would say — 'verifying code quality', 'security scanning', 'comprehensive QA', 'production readiness' — but misses common variations like 'code review', 'testing', or 'run tests'. Good coverage per anchor 4, though not the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

Distinctive framing ('5-layer verification pyramid', 'Agent-as-a-Judge', 'preventing test gaming') carves a clear niche, but generic terms like 'code quality' and 'security scanning' carry minor overlap risk with code-review and security-scanning skills. Mostly distinct per anchor 4, not the minimal-conflict clarity of anchor 5.

4 / 5

Total

17

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1010 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

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

Warning

Total

14

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

.claude/skills/multi-ai-verification/SKILL.md

Reviewed

Table of Contents

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