enterprise-code-quality
Skill1 starsChanged 6 months agoMITView on GitHub
Use when auditing, reviewing, refactoring, or planning improvements to application or infrastructure code that needs maintainability, typed boundaries, tests, or simpler architecture.
In this repository13 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/llms.txtllms.txt
- ai-app-security-reviewSkill
- codeaudit-orchestratorSkill
- codeauditSkill
- docs-claims-evidence-reviewSkill
- github-issue-pr-workflowSkill
- nextjs-security-reviewSkill
- official-docs-groundingSkill
- python-backend-qualitySkill
- python-mcp-server-qualitySkill
- refactor-with-testsSkill
- skill-supply-chain-auditorSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 12
Prefer small focused files with one clear responsibility.
- Distinctive: line 13
Use typed contracts and schema validation at external boundaries.
- Distinctive: line 14
Prefer clear names and simple control flow over clever abstractions.
- Distinctive: line 15
Avoid god files, vague helpers, dead code, and duplicated logic.
- Distinctive: line 16
Keep error handling explicit and useful without leaking secrets.
- Distinctive: line 23
Test coverage for changed behavior.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Enterprise Code Quality
Standards
- Prefer small focused files with one clear responsibility.
- Use typed contracts and schema validation at external boundaries.
- Prefer clear names and simple control flow over clever abstractions.
- Avoid god files, vague helpers, dead code, and duplicated logic.
- Keep error handling explicit and useful without leaking secrets.
- Refactor only with tests before or alongside the change.
Review Checklist
- File size and responsibility boundaries.
- API, CLI, config, and database input validation.
- Test coverage for changed behavior.
- Data flow across server/client or trust boundaries.
- Duplicated logic that can be safely consolidated.
- Overengineering that hides simple domain logic.
Output Style
Findings should include severity, file evidence, why it matters, and the smallest practical recommendation.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

