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multi-ai-code-review

Multi-perspective code review using Claude, Gemini, and Codex as specialized agents. 5-dimensional analysis (security, performance, maintainability, correctness, style) with LLM-as-judge consensus, quality scoring, and CI/CD integration. Use when reviewing PRs, auditing code quality, preparing production releases, or establishing code review workflows.

Quality

76%

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-code-review/SKILL.md

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

SKILL.md
Quality
Evals
Security

Quality

Content

57%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 skill is rich in concrete prompts, commands, and output templates, making it genuinely actionable, but it is overlong for a SKILL.md: redundant sections, inlined detail that belongs in the reference files, a broken reference path, and workflows lacking explicit validation checkpoints. Restructuring toward a lean overview with one-level-deep references would address the main weaknesses.

Suggestions

Create the missing references/performance-patterns.md (or remove the broken citation), and move the CI/CD workflow YAML, report templates, and example review session into the existing reference files so SKILL.md stays a lean overview with one-level-deep pointers.

Cut redundant sections — the 'Example Review Session', the mostly-comment 'Launch Multi-Agent Review' block, and the 'Related Skills' list — and fold the 'Multi-AI Coordination' table into the operations that use it.

Add explicit validation checkpoints to each operation (e.g., verify the diff was captured before launching agents, re-run an agent when findings look incomplete, confirm quality-gate thresholds before issuing a merge/block recommendation).

DimensionReasoningScore

Conciseness

The body is mostly actionable rather than explanatory, but contains noticeable padding: the 'Example Review Session' duplicates the Operation 2 report format, the 'Multi-AI Coordination' section repeats earlier material and includes a bash block holding only comments, and a 'Related Skills' list adds little. This is beyond 'minor instances that could be trimmed' but not severely verbose concept exposition.

3 / 5

Actionability

Concrete review prompts, executable git/grep commands, real gemini/codex CLI invocations, and a complete GitHub Actions workflow YAML give mostly executable guidance. Minor gaps: placeholder templates like '[PASTE CODE OR DIFF]', an effectively empty 'Launch Multi-Agent Review' block, and vague bullets such as 'Run all security checks'.

4 / 5

Workflow Clarity

The five operations are well-sequenced with time/automation estimates, but validation checkpoints are missing or implicit everywhere except Operation 3's consensus step — no verify-output, retry, or error-recovery steps in Operations 1, 2, 4, or 5. This matches 'sequence present but checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

A clear References section points one level deep, but 'references/performance-patterns.md' does not exist (broken reference), and heavy content that belongs in the existing reference files — notably the full CI/CD workflow YAML that duplicates references/ci-cd-integration.md — plus report templates and the example session are inlined in SKILL.md.

3 / 5

Total

13

/

20

Passed

Description

88%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: concrete capabilities, explicit 'Use when...' triggers, third-person voice, and a distinctive multi-model consensus framing. Only minor gaps in trigger synonym coverage (e.g., 'pull request', 'diff', 'code audit') and slight generic overlap with ordinary code-review skills.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — '5-dimensional analysis (security, performance, maintainability, correctness, style) with LLM-as-judge consensus, quality scoring, and CI/CD integration' — with comprehensive coverage and no vague filler. It exceeds the 'several specific actions; minor gaps' anchor because both the dimensions and the mechanisms are fully enumerated.

5 / 5

Completeness

It explicitly answers both 'what' (multi-perspective review via Claude/Gemini/Codex agents, 5 named dimensions, consensus, quality scoring, CI/CD integration) and 'when' (a dedicated 'Use when' clause with four concrete trigger phrases), in third-person voice.

5 / 5

Trigger Term Quality

'Use when reviewing PRs, auditing code quality, preparing production releases, or establishing code review workflows' contains natural user phrasings, but common variations such as 'pull request' spelled out, 'diff', 'code audit', and 'security review' are missing. Good keyword coverage rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

The multi-model ensemble/consensus angle is a distinct niche, but the broad 'code review' domain overlaps generic code-review skills, so it is 'mostly distinct; minor overlap risk' rather than minimal conflict risk.

4 / 5

Total

18

/

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

skill_md_line_count

SKILL.md is long (537 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

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

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

13

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

.claude/skills/multi-ai-code-review/SKILL.md

Reviewed

Table of Contents

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