Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ArabelaTso/Skills-4-SE --skill code-smell-detectorgit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arabelatso/skills-4-se/code-smell-detector)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/code-smell-detector"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/code-smell-detector/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/code-smell-detector"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/code-smell-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00129 | $0.03641 |
| Opus 5 | $0.00064 | $0.01820 |
| Sonnet 5 | $0.00026 | $0.00728 |
| Haiku 4.5 | $0.00013 | $0.00364 |
Grade A, and why
code-smell-detector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 609 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Smell Detector
Overview
Identify code quality and design smells in Python codebases, then provide specific refactoring recommendations to improve maintainability and design.
Workflow
1. Understand the Analysis Scope
Define what to analyze:
Questions to ask:
- What directory or files should be analyzed?
- Focus on quality smells, design smells, or both?
- Are there specific concerns (e.g., "this class is too complex")?
- Should test files be included?
Determine scope:
# Check project structure
ls -la
# Count Python files
find . -name "*.py" | wc -l
# Identify large files (potential smells)
find . -name "*.py" -exec wc -l {} + | sort -rn | head -10
2. Detect Code Smells
Use multiple detection strategies.
Strategy 1: Automated Detection
Use the bundled script for AST-based analysis:
# Scan entire project
python scripts/detect_smells.py /path/to/project
# Exclude specific directories
python scripts/detect_smells.py /path/to/project venv,tests,docs
What it detects:
- Long methods (>50 lines)
- Too many parameters (>5)
- Large classes (>15 methods)
- God classes (>20 methods)
- Magic numbers
Strategy 2: Manual Code Review
Read the code to identify design smells. See smell-patterns.md for comprehensive catalog.
Look for:
Code Quality Smells:
- Duplicate code blocks
- Magic numbers (unexplained numeric literals)
- Hardcoded values (paths, URLs, config)
- Commented-out code
- Inconsistent naming
Design Smells:
- God classes (too many responsibilities)
- Feature envy (method uses more from another class)
- Inappropriate intimacy (classes too coupled)
- Data clumps (same parameters repeated)
- Primitive obsession (using primitives instead of objects)
- Long parameter lists (>5 parameters)
Search patterns:
# Find long files (potential large classes)
find . -name "*.py" -exec wc -l {} + | awk '$1 > 300'
# Find magic numbers (basic pattern)
grep -r "[^0-9]\d\{3,\}" --include="*.py" .
# Find hardcoded paths
grep -r '"/.*/"' --include="*.py" .
# Find commented code
grep -r "^[ ]*#.*def \|^[ ]*#.*class " --include="*.py" .
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 609 lines · 129 tokens per session scan A c3528e8b0969
code-smell-detector is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (251 stars, last pushed 19d ago), licensed Apache-2.0. It adds 129 tokens to every session and 3,641 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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