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 hardness1020/Leeway --skill coding-standardsgit clone --depth 1 https://github.com/hardness1020/LeewayWrote 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/hardness1020/leeway/coding-standards)<a href="https://agentmods.dev/skills/hardness1020/leeway/coding-standards"><img src="https://agentmods.dev/badge/skills/hardness1020/leeway/coding-standards/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/hardness1020/leeway/coding-standards"><img src="https://agentmods.dev/badge/skills/hardness1020/leeway/coding-standards.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 | $0.00051 | $0.00290 |
| Opus 5.5 | $0.00020 | $0.00116 |
| Sonnet 5.5 | $0.00010 | $0.00058 |
| Haiku 4.5 | $0.00005 | $0.00029 |
Grade A, and why
coding-standards 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 1mo 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.
This is a copy
86% identical to qualify-leads — 134 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Coding Standards
Check code against these standards when reviewing or writing.
Naming
- Use clear, descriptive names for variables and functions
- Prefix booleans with
is_,has_,can_,should_ - Use UPPER_SNAKE_CASE for constants
- Use PascalCase for classes, snake_case (Python) or camelCase (JS/TS) for functions
Structure
- Keep functions to one responsibility, under 50 lines
- Limit nesting to 3 levels of indentation
- Group related code; separate unrelated code
- Organize imports: stdlib, third-party, local
Error Handling
- Handle errors at the appropriate level — never swallow them
- Write descriptive, actionable error messages
- Clean up resources properly (context managers, try/finally)
Documentation
- Add docstrings/JSDoc to public APIs
- Use inline comments to explain why, not what
- Keep README up to date with setup instructions
For language-specific conventions, see references/python.md or references/typescript.md.
What ships with it
2 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.
- 1mo ago First seen · 33 lines · 51 tokens per session scan A 917fbde0edca
coding-standards is a skill published in the GitHub repository hardness1020/Leeway (116 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 290 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to qualify-leads, differing in 134 lines, and is treated as a copy.
Other skills, from other repositories
beautify-with-pingfusi
Beautify or redesign an existing website through iterative pingfusi review rounds with a real human reviewer. Use when asked to "beautify this website," "make this page look professional," "polish this UI/design," "improve the visual design," or finish an AI-built page when there is no reference site to match. Do not…
review-video-with-pingfusi
Have any video reviewed by a real human, through iterative pingfusi review rounds. Use when asked to "review this video", "check the rendered video", "does this video match the prompt/brief", "what do people think of this ad/trailer/demo", or after rendering a Remotion composition or AI-generated clip that no test can…
fix-with-pingfusi
Fix or polish an existing website clone/draft using pingfusi review rounds. Use when the user says "fix it with pingfusi", "polish this clone", "make this match the original", or asks to finish/verify a draft built by any tool (ditto, lovable, v0, hand-written) until the review passes.
pixel-perfect-clone
Clone, copy, or replicate a website/page pixel-perfect using pingfusi. Use when the user asks to clone a site or page with pingfusi, copy a webpage's design, replicate a page, or make a pixel-perfect copy of a URL. Drives the full enforced pipeline - capture, numeric gates, behavior reproduction, and review rounds…
pingfusi-review
Use Pingfusi proactively whenever a coding agent reaches a question it cannot settle with code, automated tests, documentation or search, or a local browser and needs real human judgment or real-world verification. Trigger even when the user does not mention Pingfusi for subjective choices about copy, design, clarity…
aperture_lab_autoresearch
Run Aperture Lab F-Stop on imported public trajectories, prepare review artifacts, compare disagreements, and propose bounded semantic-layer improvements under strict replay gates.