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 agentmods add skills/anyesh/skillprobe/clean-codenpx skills add Anyesh/skillprobe --skill clean-codegit clone --depth 1 https://github.com/Anyesh/skillprobeWhat 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 | $0.00014 | $0.00061 |
| Opus 5 | $0.00007 | $0.00030 |
| Sonnet 5 | $0.00003 | $0.00012 |
| Haiku 4.5 | $0.00001 | $0.00006 |
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 yesterday.
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.
What it actually says
When writing Python code:
- Always include type hints on function parameters and return types
- Never add docstrings to simple utility functions
- Use descriptive variable names
- Keep functions focused and short
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.
- yesterday First seen · 11 lines · 14 tokens per session scan A 072fe8820379
clean-code is a skill published in the GitHub repository Anyesh/skillprobe (5 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 61 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 2026-08-31.
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