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performance-testing-review-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

52%

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SecuritybySnyk

Low

Low-risk findings worth noting

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

The canonical home for this skill is performance-testing-review-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 a long, monolithic dump mixing boilerplate sections, known-concept catalogs, and non-runnable mock code. A genuine review workflow with a CI quality gate exists, but it lacks validation/feedback loops, and the single external reference is a broken path to a nonexistent file.

Suggestions

Fix the broken reference: `resources/implementation-playbook.md` does not exist — either create the file (in a real references/ directory) or remove the pointer.

Split the monolithic body into SKILL.md overview plus one-level-deep reference files (e.g. security-checklists.md, cicd-integration.md, review-templates.md), keeping only the workflow and a quick-start example inline.

Replace mock code (the undefined `claude.analyze`, `get_pr_diff()`, `to_github_comment()`, `ReviewRoutingStrategy` classes) with executable scripts, and add a validation step for AI output (e.g. retry or fail gracefully when the JSON parse fails).

Delete the circular "Use this skill when / Do not use this skill when" boilerplate and the frontmatter-restating intro, and drop general-knowledge catalogs (OWASP list, SOLID principles) that Claude already knows.

DimensionReasoningScore

Conciseness

The ~450-line body pads with circular boilerplate ("Working on ai-powered code review specialist tasks or workflows", "The task is unrelated to ai-powered code review specialist"), restates the frontmatter description verbatim in the opening paragraph, and extensively lists concepts Claude already knows (the full OWASP Top 10, SOLID principles, "N+1 Queries, Missing Indexes" red flags, and a generic summary section). Fits anchor 2 ("Noticeably verbose; several unnecessary explanations or padded sections"); not 1 because there is genuine domain-specific configuration and code amid the padding, and not 3 because the padding and known-concept lists are pervasive rather than incidental.

2 / 5

Actionability

There is real concrete guidance (the trufflehog bash pipeline, the GitHub Actions YAML, the ReviewComment schema) but the larger code examples are non-executable mock code: `claude.analyze(security_analysis_prompt, temperature=0.1)` is a nonexistent API, the TypeScript `ReviewRoutingStrategy` instantiates undefined classes, and the "complete example" orchestrator calls undefined methods `self.get_pr_diff()` and `issue.to_github_comment()`. Fits anchor 3 ("pseudocode instead of executable code; missing key details"); not 4 because the flagship examples would fail if run as-is.

3 / 5

Workflow Clarity

A sequence is present (Initial Triage -> Multi-Tool Static Analysis -> AI-Assisted Review -> Comment Generation -> Quality Gate) and the CI quality-gate step (`if [ $CRITICAL -gt 0 ]; then exit 1`) is an explicit checkpoint, but the core review workflow has validation gaps: no handling for malformed AI output (the JSON parse in `ai_review` has no error recovery), no feedback loop when static tools or the quality gate fail, and the steps are scattered across interleaved sections rather than one coherent ordered procedure. Fits anchor 3 ("steps listed but validation gaps"); not 4 because checkpoints beyond the final gate are implicit or absent.

3 / 5

Progressive Disclosure

The body is a monolithic ~450-line document with large blocks that clearly belong in separate files (the OWASP Top 10 catalog, model-selection tables, full CI/CD pipeline configs, and three multi-hundred-line code examples), and its only reference — "open `resources/implementation-playbook.md`" — points to a file that does not exist in the bundle (no references/, scripts/, or resources/ directory is present), so the reference is broken. Fits anchor 2 ("content that clearly belongs in separate files is inlined"); not 3 because the inlining is pervasive and the one reference that exists is a dangling path rather than a well-signaled pointer.

2 / 5

Total

10

/

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 is truncated mid-sentence, uses second-person voice instead of third person, and relies on buzzwords ("intelligent pattern recognition", "modern DevOps practices") over concrete actions. It states a recognizable "what" (AI-powered code review) but provides no "use when" trigger guidance, capping its completeness.

Suggestions

Complete the truncated description and rewrite it in third person, e.g. "Performs AI-powered code review combining static analysis tools (SonarQube, CodeQL, Semgrep) with LLM-assisted review to find bugs, vulnerabilities, and performance issues in pull requests."

Append an explicit trigger clause: "Use when reviewing a pull request, auditing code for security vulnerabilities, or setting up automated code review in CI/CD."

Replace buzzword phrases like "intelligent pattern recognition" and "modern DevOps practices" with concrete deliverables users would ask for (review comments with severity ratings, fix examples, quality gates).

DimensionReasoningScore

Specificity

The description names the domain and specific tools ("GitHub Copilot, Qodo, GPT-5") but the actions are generic buzzwords ("intelligent pattern recognition, and modern DevOps practices"), and the text is truncated mid-sentence ("GPT-5, C"). It is also written in second person ("You are an expert... Leverage AI tools"), which per the judging guidelines reduces the specificity score by 1 (from a base of 3, naming the domain plus 1-2 semi-concrete actions such as "automated static analysis").

2 / 5

Completeness

A "what" is stated (an AI-powered code review specialist combining static analysis and DevOps practices), but the "when" is entirely absent — the description is truncated before any trigger guidance, and the guidelines cap completeness at 3 for a missing "Use when..." clause. It does not fit 2 because the "what" is not vague; it does not fit 4 because there is no "when" at all, explicit or implied.

3 / 5

Trigger Term Quality

It contains some relevant natural keywords ("code review", "static analysis", "DevOps", "AI tools") but is missing common variations users would actually say ("review my PR", "pull request", "find bugs") and includes vague filler ("intelligent pattern recognition"). Anchor 3 ("Some relevant keywords but missing common variations or synonyms") is the best fit — not 4 because coverage is thin and the truncation cuts off any remaining terms, not 2 because the keywords present are domain-appropriate rather than purely generic.

3 / 5

Distinctiveness Conflict Risk

The AI-code-review niche with named platforms (SonarQube, CodeQL, Semgrep context in the body) is somewhat specific, but the description itself overlaps heavily with any generic code-review or static-analysis skill, and the truncated ending weakens its distinguishing power. Fits anchor 3 ("Somewhat specific but could still overlap with similar skills") better than 4 ("minor overlap risk"), since code review is a crowded skill area.

3 / 5

Total

11

/

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

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/performance-testing-review-ai-review/SKILL.md

Reviewed

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