detect-code-smells

detect-code-smells is a skill for Claude Code, Codex from dvasyliev/ai-dev-tools-getting-started. It costs 17 tokens per session (147 once invoked), scanned A, original, no licence file.

A code-review guide for spotting common code smells, which are patterns that can make code harder to maintain.

In plain words
What is it for?
Use it to review code and suggest small fixes that preserve its current behavior.
Why use it?
It helps identify structural problems without changing what the code does.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/dvasyliev/ai-dev-tools-getting-started/detect-code-smells
Any agent
npx skills add dvasyliev/ai-dev-tools-getting-started --skill detect-code-smells
Clone the repo
git clone --depth 1 https://github.com/dvasyliev/ai-dev-tools-getting-started

Made for: Claude Code, Codex.

Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 147 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.00147
Opus 5 $0.00009 $0.00073
Sonnet 5 $0.00003 $0.00029
Haiku 4.5 $0.00002 $0.00015

Measured 3d ago against content hash c8d80e9fe385, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

detect-code-smells 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 3d 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.

cursor/skills/detect-code-smells/SKILL.md · 30 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 3d ago First seen · 30 lines · 17 tokens per session scan A c8d80e9fe385

Subscribe to this mod's changes

detect-code-smells is a skill published in the GitHub repository dvasyliev/ai-dev-tools-getting-started (21 stars, last pushed 4mo ago), with no licence file. It adds 17 tokens to every session and 147 once invoked, about $0.0001 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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