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production-code-audit

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

26

Quality

19%

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

Fix and improve this skill with Tessl

tessl review fix ./skills/production-code-audit/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

13%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is severely bloated and largely non-actionable. It spends most of its ~400+ lines showing hypothetical conversation transcripts of what an ideal audit output would look like, rather than providing concrete, executable instructions Claude can follow. The content explains many concepts Claude already knows (SQL injection, N+1 queries, password hashing), lacks validation checkpoints for what are inherently destructive batch operations, and inlines everything into a single massive file with no progressive disclosure.

Suggestions

Cut the content by 70-80%: Remove the hypothetical conversation examples (Examples 1-3), the extensive issue taxonomy lists (Claude already knows what SQL injection and N+1 queries are), and the audit report template. Focus only on the concrete workflow steps and tool usage patterns.

Add explicit validation checkpoints: Before applying batch fixes, require creating a git branch/commit, running tests after each category of fix, and stopping if tests fail. The current 'fix everything automatically' approach is dangerous for destructive batch operations.

Replace hypothetical output examples with concrete tool-usage patterns: Instead of showing what Claude would say, show specific patterns like how to use listDirectory recursively, how to grep for common vulnerability patterns, and how to use strReplace safely with verification.

Split into bundle files: Move the checklist to CHECKLIST.md, the report template to REPORT_TEMPLATE.md, and keep SKILL.md as a concise overview with the core workflow and references to these files.

DimensionReasoningScore

Conciseness

Extremely verbose at ~400+ lines. Extensively explains concepts Claude already knows (what SQL injection is, what N+1 queries are, what god classes are, basic security concepts). The massive example outputs showing hypothetical before/after transformations are pure padding — they don't teach Claude anything actionable. The checklists, best practices lists, and 'common pitfalls' sections rehash well-known software engineering concepts. The 'Related Skills' and 'Additional Resources' sections add tokens without value.

1 / 5

Actionability

Despite its length, the skill provides almost no executable guidance. The code examples are embedded within hypothetical conversation transcripts showing what Claude would say, not actual executable instructions. The 'Autonomous Scanning Instructions' section mentions tool names (listDirectory, readFile, strReplace) but gives no concrete patterns for how to use them effectively. The skill essentially describes what an ideal audit output looks like rather than providing concrete steps Claude can follow to actually perform one.

2 / 5

Workflow Clarity

The 4-step workflow (Discover → Detect → Fix → Verify) provides a rough sequence but lacks any real validation checkpoints or feedback loops. For a skill involving destructive batch operations (automatically rewriting code across an entire codebase), there are no safeguards, no 'verify before proceeding' gates, no rollback instructions, and no guidance on what to do when tests fail after changes. The instruction to 'fix everything automatically' without validation caps this score. The rubric states missing validation in destructive/batch operations should cap at 3, and this falls below even that threshold.

2 / 5

Progressive Disclosure

This is a monolithic wall of text with no bundle files and no references to external files for detailed content. The massive hypothetical example outputs (Example 1 alone is ~100 lines), the full audit report template, the complete checklist, and the extensive issue detection taxonomy are all inlined. There is no separation of concerns — everything from trigger phrases to report templates to code examples is in one enormous file with no external references.

1 / 5

Total

6

/

20

Passed

Description

25%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.

This description relies heavily on buzzwords and marketing language ('production-grade', 'corporate-level professional quality', 'deep-scan line-by-line') without specifying concrete actions or providing trigger guidance. It lacks a 'Use when...' clause, uses vague superlatives instead of specific capabilities, and would be difficult for Claude to distinguish from other code improvement or refactoring skills.

Suggestions

Replace vague buzzwords with specific concrete actions, e.g., 'Refactors code structure, adds error handling, improves naming conventions, removes dead code, adds type annotations, and optimizes performance bottlenecks.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks to refactor, clean up, improve code quality, reduce technical debt, or bring code to production standards.'

Narrow the scope or clarify boundaries to reduce conflict risk with other code-related skills, e.g., specify which languages or frameworks it targets, or distinguish it from simple code review or linting skills.

DimensionReasoningScore

Specificity

Names the domain (codebase transformation) but actions are vague and buzzword-heavy. 'Deep-scan', 'understand architecture', 'systematically transform', and 'optimizations' are abstract rather than concrete actions. No specific operations like refactoring, adding tests, improving error handling, etc. are mentioned.

2 / 5

Completeness

Has a vague 'what' (scan and transform codebase) but no 'when' clause at all. There is no 'Use when...' guidance or equivalent trigger phrasing. The description reads as a marketing claim rather than actionable selection criteria.

2 / 5

Trigger Term Quality

Contains some relevant terms like 'codebase', 'architecture', 'patterns', and 'optimizations', but these are generic developer jargon. Missing natural user phrases like 'refactor', 'code review', 'clean up code', 'improve code quality', 'technical debt', or specific transformation types users would request.

2 / 5

Distinctiveness Conflict Risk

Very broad scope — 'entire codebase' transformation to 'production-grade quality' could overlap with code review skills, refactoring skills, linting skills, optimization skills, or any code improvement tool. The buzzword-laden language ('corporate-level professional quality') doesn't carve out a distinct niche.

2 / 5

Total

8

/

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.

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (544 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

9

/

11

Passed

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

Table of Contents

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