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code-review-ai-ai-review

You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, C

42

Quality

43%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/code-review-ai-ai-review/SKILL.md

The canonical home for this skill is code-review-ai-ai-review in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

38%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 is information-rich and code-heavy but bloated with concepts Claude already knows, mixes executable code with pseudocode that has undefined pieces, and inlines everything into one large file with a dangling reference. It reads more as a reference dump than a focused, navigable skill.

Suggestions

Move the complete Python orchestrator, the GitHub Actions YAML, and the ReviewComment interface into separate reference files and keep SKILL.md as a concise overview that links to them.

Remove or condense background Claude already knows (OWASP Top 10 enumeration, SOLID principles, anti-pattern definitions) to lean actionable guidance.

Fix or remove the dangling 'resources/implementation-playbook.md' reference, and make the TypeScript routing example either fully executable or explicitly label it as illustrative pseudocode.

DimensionReasoningScore

Conciseness

The ~440-line body explains concepts Claude already knows (the full OWASP Top 10 list, SOLID principles, anti-patterns like Singleton/God objects) and pads with lengthy illustrative code; it is noticeably verbose with several sections that could be trimmed or removed.

2 / 5

Actionability

It provides substantial concrete code (Python orchestrator, GitHub Actions YAML, bash, Go/TypeScript snippets), but key examples are pseudocode with undefined constructs (new AIEngine/HumanReviewRequired classes, self.get_pr_diff(), issue.to_github_comment()), leaving gaps that prevent copy-paste execution.

3 / 5

Workflow Clarity

The 'Automated Code Review Workflow' gives a numbered triage sequence followed by parallel static analysis and AI review, and a CI/CD quality gate checks for CRITICAL issues, but inter-step validation checkpoints are implicit and the outward-facing batch operation (posting comments / requesting changes) lacks explicit verify-before-act feedback, capping clarity at 3.

3 / 5

Progressive Disclosure

No bundle files exist; the body is a monolithic ~440-line document that inlines content clearly belonging in separate files (full Python example, full CI/CD YAML, ReviewComment interface), and its one reference (resources/implementation-playbook.md) points to a non-existent path, so structure is minimal and navigation is poor.

2 / 5

Total

10

/

20

Passed

Description

48%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 identifies a clear niche and toolset but is written in second person, lacks an explicit 'Use when' trigger, and is truncated mid-sentence, which together cap specificity and completeness. It is recognizable but not exemplary.

Suggestions

Rewrite in third person and finish the truncated sentence (e.g., 'AI-powered code review specialist that combines automated static analysis, intelligent pattern recognition, and modern DevOps practices.').

Add an explicit 'Use when...' clause with natural trigger phrases such as 'Use when reviewing pull requests, running automated code analysis, or triaging security and performance issues in diffs.'

Replace buzzwords ('intelligent pattern recognition') with the concrete actions a user would name ('detects bugs, vulnerabilities, and performance regressions in pull requests').

DimensionReasoningScore

Specificity

The description names the domain ('AI-powered code review specialist') and lists several concrete components ('automated static analysis, intelligent pattern recognition, and modern DevOps practices', 'GitHub Copilot, Qodo, GPT-5'), but the verbs are vague ('combining', 'Leverage') and the field is literally truncated mid-token ('GPT-5, C'). Base specificity ~3, reduced by 1 per guideline for second-person voice ('You are an expert...').

2 / 5

Completeness

A clear 'what' is present (AI-powered code review combining static analysis and DevOps practices), but there is no 'Use when...' clause or equivalent explicit trigger guidance, so completeness is capped at 3 per the guideline; the truncation further weakens the 'what'.

3 / 5

Trigger Term Quality

It includes the natural term 'code review' and relevant jargon ('static analysis', 'DevOps'), but beyond the headline phrase the keywords are technical rather than the variations a user would naturally say, and there are no trigger synonyms or file extensions.

3 / 5

Distinctiveness Conflict Risk

'AI-powered code review specialist' anchored to specific toolchains (CodeQL, SonarQube, Semgrep, Copilot) carves a fairly distinct niche with only minor overlap against a general manual code-review skill.

4 / 5

Total

12

/

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.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

14

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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