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vibe-code-auditor

Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.

48

Quality

53%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

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

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

SKILL.md
Quality
Evals
Security

Quality

Content

52%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 provides a comprehensive and well-structured code audit framework with a clear output format, scoring algorithm, and prioritization scheme, which makes it quite actionable. However, it is significantly over-verbose, explaining many concepts Claude already knows (basic security patterns, what separation of concerns means, etc.) and inlining all content in a single large file rather than using progressive disclosure. The pattern recognition shortcuts and quick checks add value but could be more concise.

Suggestions

Reduce verbosity by removing explanations of concepts Claude already knows (e.g., what bare excepts are, what separation of concerns means) — keep only the specific heuristics and thresholds (like '3+ repetitions = extract', '>50 lines = risk').

Split the 7 audit dimensions into a separate DIMENSIONS.md reference file, keeping only dimension names and one-line summaries in the main SKILL.md.

Move the detailed output format template into a separate TEMPLATE.md file, referencing it from the main skill with a clear link.

Eliminate the Identity, Purpose, and When to Use sections entirely — these describe the skill's nature rather than providing actionable instructions Claude needs.

DimensionReasoningScore

Conciseness

The skill is significantly verbose at ~300+ lines. It extensively explains concepts Claude already knows (what vibe coding is, what separation of concerns means, what bare excepts are). The identity/purpose/when-to-use sections are largely unnecessary padding. The pattern recognition table and quick checks repeat information Claude inherently understands. Many checklist items describe basic code review knowledge.

2 / 5

Actionability

The skill provides concrete output templates, a specific scoring algorithm, severity tags, and structured report format that Claude can directly follow. The pattern recognition table with specific search strings (e.g., `f"SELECT"`, `except:`) is actionable. However, it lacks executable code examples for the audit process itself — the code snippets shown are illustrative patterns to detect rather than tools to run.

4 / 5

Workflow Clarity

The workflow is reasonably clear: pre-audit checklist → quick scan → evaluate 7 dimensions → produce structured report with prioritized findings. The calibration section by code size is a useful checkpoint. However, there's no explicit validation step for the audit output itself (e.g., verifying findings are substantiated before finalizing), and the transition between scanning and deep analysis could be more explicitly sequenced.

4 / 5

Progressive Disclosure

All content is inlined in a single monolithic file with no bundle files. The 7 audit dimensions with their detailed checklists, the output format specification, the scoring rubric, and the behavior rules could easily be split into separate reference files. The 'Related Skills' section references other skills but the core content itself has no progressive disclosure structure — everything is presented at once regardless of complexity.

2 / 5

Total

12

/

20

Passed

Description

53%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 and timely niche—auditing AI-generated code—but lacks a 'Use when...' clause, which is critical for skill selection. The capability terms are more categorical than concrete, and the trigger terms miss common user phrasings like 'code review', 'review my code', or 'tech debt'.

Suggestions

Add an explicit 'Use when...' clause with trigger phrases like 'review AI-generated code', 'audit generated code', 'check code quality', 'vibe coding review', or 'code review'.

Replace abstract categories ('structural flaws, fragility, production risks') with specific actions like 'checks for missing error handling, identifies hardcoded values, flags missing tests, detects brittle dependencies'.

Include natural synonyms and related terms users might say, such as 'code review', 'tech debt', 'LLM-generated code', 'copilot code', or 'generated code quality'.

DimensionReasoningScore

Specificity

Names the domain (code auditing) and describes a few concrete aspects ('structural flaws, fragility, production risks'), but these are still somewhat abstract categories rather than specific actions like 'check for error handling, validate test coverage, identify hardcoded credentials'.

3 / 5

Completeness

Has a clear 'what' (audit code for structural flaws, fragility, production risks) but completely lacks a 'when' clause. There is no explicit guidance on when Claude should select this skill, which caps this dimension at 3 per the rubric guidelines.

3 / 5

Trigger Term Quality

Includes some relevant terms like 'AI-produced code', 'audit', and 'production risks', but misses natural user phrases like 'code review', 'review my code', 'tech debt', 'code quality', 'generated code', or 'vibe coding'. The term 'rapidly generated' is somewhat unusual phrasing.

3 / 5

Distinctiveness Conflict Risk

The focus on 'rapidly generated or AI-produced code' provides a fairly distinct niche compared to general code review skills. However, there's potential overlap with generic code review or linting skills since 'structural flaws' and 'production risks' are broad enough to apply to any code review context.

4 / 5

Total

13

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

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

Table of Contents

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