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 kumaran-is/claude-code-onboarding --skill clean-codegit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/kumaran-is/claude-code-onboarding/clean-code)<a href="https://agentmods.dev/skills/kumaran-is/claude-code-onboarding/clean-code"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/clean-code/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/kumaran-is/claude-code-onboarding/clean-code"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/clean-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00079 | $0.01637 |
| Opus 5 | $0.00039 | $0.00818 |
| Sonnet 5 | $0.00016 | $0.00327 |
| Haiku 4.5 | $0.00008 | $0.00164 |
Grade A, and why
clean-code 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 6d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Code Skill
Iron Law: Before applying any principle, READ the relevant source file first. Do not refactor or flag issues based on memory — verify the actual code (file:line) before making any claim.
This skill embodies the principles of "Clean Code" by Robert C. Martin (Uncle Bob). Use it to transform "code that works" into "code that is clean."
Core Philosophy
"Code is clean if it can be read, and enhanced by a developer other than its original author." — Grady Booch
When to Use
- Writing new code: Ensure high quality from the start.
- Reviewing Pull Requests: Provide constructive, principle-based feedback.
- Refactoring legacy code: Identify and remove code smells.
- Improving team standards: Align on industry-standard best practices.
1. Meaningful Names
- Use Intention-Revealing Names:
elapsedTimeInDaysinstead ofd. - Avoid Disinformation: Don't use
accountListif it's actually aMap. - Make Meaningful Distinctions: Avoid
ProductDatavsProductInfo. - Use Pronounceable/Searchable Names: Avoid
genymdhms. - Class Names: Use nouns (
Customer,WikiPage). AvoidManager,Data. - Method Names: Use verbs (
postPayment,deletePage).
2. Functions
- Small: Functions should be shorter than you think.
- Do One Thing: A function should do only one thing, and do it well.
- One Level of Abstraction: Don't mix high-level business logic with low-level details (like regex).
- Descriptive Names:
isPasswordValidis better thancheck. - Arguments: 0 is ideal, 1-2 is okay, 3+ requires a very strong justification — introduce a parameter object.
- No Side Effects: Functions shouldn't secretly change global state.
3. Comments
- Don't Comment Bad Code — Rewrite It: Most comments are a sign of failure to express in code.
- Explain Yourself in Code:
# BAD: comment explains intent that code should express if employee.flags & HOURLY and employee.age > 65: # GOOD: code expresses intent directly if employee.isEligibleForFullBenefits(): - Good Comments: Legal, informative (regex intent), clarification (external library behavior), TODOs (with ticket).
- Bad Comments: Mumbling, redundant, misleading, mandated, noise, position markers.
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.
- 6d ago First seen · 150 lines · 79 tokens per session scan A 80455e328ae5
clean-code is a skill published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 1,637 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
rework-rate
Measure and interpret PR rework rate — the emerging 5th DORA metric.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.