Code Smell Diagnosis
SkillDev toolsUse when diagnosing structural maintainability problems in existing frontend code before deciding how to refactor it, especially when evidence, false-positive checks, and candidate transformations are needed without editing business code.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Code Smell Diagnosis skill
What this skill tells your AI
The instructions your AI receives, as published by bovinphang/frontend-craft in skills/fec-code-smells/SKILL.md and read by ahel’s review.
Overview
Diagnose structural problems from concrete evidence. A smell is a prompt to investigate, not proof that code must change.
Procedure
- Establish the requested scope and project facts.
- Collect concrete structural and change-coupling evidence.
- Match evidence against the smell catalog.
- Apply false-positive checks.
- Report confidence, impact, and candidates from the mapping guide.
- If the request is diagnosis-only, do not modify business code.
Finding Contract
Every finding states location, evidence, smell ID/name, impact, confidence (HIGH, MEDIUM, LOW), candidate refactorings, priority, and material false-positive considerations. LOW confidence is never an automatic-edit signal.
Quick Reference
| Need | Reference |
|---|---|
| Standard 24-smell model | Smell catalog |
| Evidence and false positives | Detection guide |
| Candidate refactorings | Smell-to-refactoring map |
Common Mistakes
- Treating a metric threshold as proof.
- Reporting a smell without a file/symbol and evidence.
- Treating every loop, switch, comment, wrapper, or pipeline as defective.
- Editing code when the user asked for diagnosis only.
Signals
- GitHub stars
- 21
- Forks
- 3
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
fec-code-smells- Source
- github.com/bovinphang/frontend-craft