code-quality-analysis_skill

This skill performs security review, clarity refactoring, and synthesis analysis to improve code quality across files and pre-deployment checks.

Python

19

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill greyhaven-ai/claude-code-config --skill code-quality-analysis

SKILL.md2.0 KB

Overview

This skill performs multi-mode code quality analysis focused on security, readability, and cross-file synthesis. It combines OWASP Top 10-based security checks, clarity refactoring rules, and project-wide consistency scans to surface actionable issues. It supports a team-mode parallel workflow for integration into CI quality pipelines.

How this skill works

The skill inspects code in three modes: Security Review scans for OWASP Top 10 patterns and common vulnerability classes; Clarity Refactoring applies readability rules and suggests targeted rewrites; Synthesis Analysis correlates findings across files to identify systemic or supply-chain issues. When invoked from a quality-pipeline in team-mode, analyses run in parallel and aggregate results into structured reports and checklists for reviewers.

When to use it

Before deployment for pre-release security and quality gating
During pull request reviews to supplement manual code review
When addressing 'code quality', 'code review', or 'security review' requests
For scheduled quality audits and technical debt sprints
When planning refactoring or eliminating code smells

Best practices

Run Security Review against critical services and public-facing endpoints first
Combine automated clarity suggestions with a human reviewer to preserve intent
Use Synthesis Analysis to detect cross-file misuse of credentials, APIs, or data flows
Integrate the skill into CI as a parallel stage and fail builds only on high-confidence issues
Annotate fixes with references to the checklist entry and OWASP item for traceability

Example use cases

Scan a microservice repo for SQL injection, XSS, and insecure deserialization risks
Refactor legacy modules for naming consistency, function length, and comment clarity
Aggregate cross-file type mismatches and API contract violations across a codebase
Produce a pre-deploy quality report listing security severity, readability score, and remediation steps
Run team-mode parallel analysis during nightly quality pipelines to distribute workload

FAQ

Primary implementation targets Python, but the checks focus on common patterns that can apply across languages; add adapters for other languages as needed.

How are findings prioritized?

Findings are categorized by confidence and impact: high (security-critical), medium (likely defects), and low (style/readability). High-confidence security issues get top priority for remediation.