No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Zhen-Bo · Agent Tool · ★ 239
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is smell-check safe to install? View the security audit →
Read the skill docs » · Install · Example report · 繁體中文 A codebase health check with receipts. smell-check is an Agent Skill for AI coding agents. It audits the paths you choose for code smells and test smells, and every finding carries its evidence. The smells it hunts are the maintainability warnings catalogued in Refactoring, Clean Code, and the test-smell literature. It is a health check for a codebase, not a PR review bot: no merge advice, no code edits, no test runs. Measured, not vibed. Structure metrics come from scripts and tools (, AST counters, ) wherever those can run; anything unmeasure
| Stars | 239 |
| Forks | 18 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.2271074747211/100 |
| Last Updated | 2026-08-29 |
| Created | 2026-01-17 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
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smell-check is Agent Skill for code and test smell audits. Evidence-ranked findings from Refactoring, Clean Code, and the test-smell literature. Formerly pragmatic-code-review.. It is categorized as a Agent Tool with 239 GitHub stars.
smell-check is primarily written in Python. It covers topics such as agent-skills, ai-agents, claude-code.
You can find installation instructions and usage details in the smell-check GitHub repository at github.com/Zhen-Bo/smell-check. The project has 239 stars and 18 forks, indicating an active community.
smell-check is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to smell-check on Agent Skills Hub include habit-hooks, claude-code-workflows, roam-code. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: