Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSnpx agentmods add skills/dds-solutions/ai-tadpole-os/clean-codeWrote 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/dds-solutions/ai-tadpole-os/clean-code)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/clean-code"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/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/dds-solutions/ai-tadpole-os/clean-code"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/clean-code.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 | $0.00037 | $0.01242 |
| Opus 5.5 | $0.00015 | $0.00497 |
| Sonnet 5.5 | $0.00007 | $0.00248 |
| Haiku 4.5 | $0.00004 | $0.00124 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / clean-code
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Over-engineering, God-objects, deep nesting, swallowed exceptions, unverified speculative code.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Clean Code — Pragmatic Engineering Standards
Philosophy: Be concise, direct, and solution-focused. Code is written for humans to read and machines to execute. Minimize cognitive load at all times.
📚 Progressive Disclosure & L3 Reference Index
Consult deep L3 Reference Guides and verification tools on demand:
- Refactoring & Git Standards: refactoring_and_git_standards.md
- Deterministic Clean Code Linter: clean_code_linter.py
📐 1. Core Engineering Principles
| Principle | Meaning & Practical Implementation |
|---|---|
| Single Responsibility (SRP) | Each function or module does ONE thing well. Functions $\le 50$ lines, files $\le 500$ lines. |
| Guard Clauses & Early Returns | Invert if conditions to handle errors and boundary cases first. Eliminate else ladders and keep nesting depth $\le 3$. |
| Cognitive Load Minimization | The "3 AM Outage Test": If a sleepy on-call engineer cannot understand the logic in 30 seconds, refactor it. |
| Zero Over-Engineering | Do not build speculative interfaces, dynamic registries, or generic wrappers for single-use logic (YAGNI & KISS). |
| Colocation & Domain Boundaries | Place tests, types, and domain helpers physically close to where they are consumed. Avoid grab-bag utils/ dumps. |
| Self-Documenting Naming | Reveal intent through precise identifiers (is_active, get_session_by_token). Avoid redundant inline comments. |
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.
- 3d ago Changed · +34 lines · +15 tokens per session ae0a2d76ef08
- 1mo ago First seen · 58 lines · 22 tokens per session scan A bb8829f1b4e0
clean-code is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 1,242 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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