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- Arjenschwarz
- Agentic Coding
- Code Audit
code-audit_skill
- Python
16
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
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Installation
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npx veilstrat add skill arjenschwarz/agentic-coding --skill code-audit- SKILL.md3.0 KB
Overview
This skill performs a parallel code audit by running two focused subagents—one that simplifies code and one that critiques design—and consolidates their findings into a prioritized report. It highlights shared concerns, architecture issues, and simplification opportunities, then offers to create actionable Transit tasks. Use it to get a quick, structured quality assessment of the current working directory.
How this skill works
The skill spawns two parallel subagents: a code-simplifier that scans for complexity hotspots and unnecessary abstractions, and a design-critic that inspects architecture, module boundaries, and data flow. Each subagent returns structured findings with location, summary, and severity. The skill deduplicates and groups findings into Simplification, Architecture, and Shared Concerns, sorts them by severity, and presents a clear report. Finally, it can convert selected findings into Transit tasks (chore or bug) for tracked remediation.
When to use it
- Before a major refactor to identify simplification targets and architecture risks
- During code review cycles to surface systemic issues beyond single PRs
- When onboarding to a new codebase to get a consolidated quality snapshot
- To prepare for production release by finding high-severity design or complexity problems
- When creating a prioritized backlog of technical debt and maintenance work
Best practices
- Run from the project root so both subagents inspect the full codebase
- Provide or reference any project conventions (coding standards, CLAUDE.md) to guide analysis
- Review Shared Concerns first — they represent the highest signal from both analyses
- Group related findings into a single Transit task when work spans multiple files or is a single change
- Prioritize high-severity items for immediate action and track medium/low as planned improvements
Example use cases
- Audit a legacy Python package to identify complexity hotspots and unsafe abstractions
- Assess a microservice repository to find architecture boundary violations and data flow risks
- Generate a prioritized todo list of refactors and bug fixes before a release
- Create tracked Transit tasks for technical debt items found across modules
- Combine simplification suggestions into a single chore to improve readability and reduce LOC
FAQ
The skill matches findings by location and concern, merging entries that point to the same file/issue to avoid duplicates.
What types of Transit tasks are created?
Simplification items become type 'chore' and design problems that could cause failures become type 'bug'. Tasks include the finding details and suggested actions.