Name: code-review-ai-ai-review
Rating: 44 (1 reviews)
Author: administrakt0r

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

Quality

45%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/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

Quality

Content

46%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 rich with concrete, mostly-executable code across many languages, but it is overly long, repeats known concepts, and is a monolithic wall with a broken external reference rather than a lean overview pointing to well-organized supporting files.

Suggestions

Trim known-concept lists (OWASP Top 10, SOLID, anti-patterns) to a brief checklist or move them to a reference file, and remove the tautological 'Use this skill when' / 'Do not use' filler.

Split the large end-to-end examples (Python orchestrator, GitHub Actions workflow, Go microservice reviewer) into separate files under references/ and keep SKILL.md a concise overview with one-level-deep links.

Add an explicit validation feedback loop before posting comments (e.g. dry-run parse review-comments.json, fail on schema/parse errors, retry) and fix or remove the non-existent resources/implementation-playbook.md reference.

Dimension	Reasoning	Score
Conciseness	The ~450-line body is noticeably verbose: it re-explains concepts Claude already knows (OWASP Top 10, SOLID principles, anti-patterns) and repeats review-prompt/routing logic across multiple large code blocks, plus tautological filler like 'Working on ai-powered code review specialist tasks or workflows'.	2 / 5
Actionability	It provides mostly executable, concrete guidance (a complete Python CodeReviewOrchestrator, GitHub Actions YAML, Go checklist struct, bash secret-scanning), with only minor illustrative gaps such as the non-runnable ReviewRoutingStrategy TypeScript interface.	4 / 5
Workflow Clarity	The triage and multi-tool analysis are sequenced and a CI/CD Quality Gate checkpoint exists, but the main review workflow lacks an explicit validate→fix→retry loop for the batch operation of posting multiple review comments to a PR, which caps workflow clarity at 3.	3 / 5
Progressive Disclosure	Almost all material is inlined into one monolithic SKILL.md with large code blocks that belong in separate files, and the single reference to 'resources/implementation-playbook.md' points to a file that does not exist (no references/scripts/assets bundle directories are present).	2 / 5
	Total	11 / 20 Passed

Description

45%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 the domain and tooling but relies on buzzwords, omits any 'when to use' trigger, and is cut off mid-sentence. Second-person voice and truncation further weaken it.

Suggestions

Rewrite in third person and finish the sentence; replace 'intelligent pattern recognition' / 'modern DevOps practices' with concrete actions like 'identify bugs, vulnerabilities, and performance issues in pull requests'.

Add an explicit 'Use when...' clause with natural triggers, e.g. 'Use when reviewing a pull request or when the user asks to review code for bugs, vulnerabilities, or performance issues'.

Narrow the scope to reduce overlap with general code-review/security-review skills, e.g. emphasize the AI-tool orchestration angle that distinguishes it.

Dimension	Reasoning	Score
Specificity	The description names the code-review domain and lists specific tools (Copilot, Qodo, GPT-5) but the actions are generic and buzzwordy ('intelligent pattern recognition', 'modern DevOps practices'); the second-person voice ('You are an expert...') triggers a 1-point specificity penalty.	2 / 5
Completeness	It conveys a clear 'what' (AI-powered code review specialist) but provides no 'Use when...' trigger guidance, and the text is truncated mid-sentence ('GPT-5, C'); the missing 'when' clause caps completeness at 3.	3 / 5
Trigger Term Quality	'code review' is a natural trigger term, but the tool names (GitHub Copilot, Qodo, GPT-5) are not phrases users say when they need a review, and common variations like 'pull request' or 'review my code' are absent.	3 / 5
Distinctiveness Conflict Risk	The AI-code-review niche is somewhat specific, but it overlaps materially with existing general code-review and security-review skills, so conflict risk remains moderate.	3 / 5
	Total	11 / 20 Passed

Validation

87%

Warnings & errors only

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

Criteria	Description	Result
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
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/code-review-ai-ai-review/SKILL.md
Commit: 57c135b

Reviewed: 2 days ago

Table of Contents

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