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 rules/wyattowalsh/agents/skill-accuracygit clone --depth 1 https://github.com/wyattowalsh/agentsWhat 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 | $0.00009 | $0.00202 |
| Opus 5 | $0.00005 | $0.00101 |
| Sonnet 5 | $0.00002 | $0.00040 |
| Haiku 4.5 | $0.00001 | $0.00020 |
Grade A, and why
skill-accuracy 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 2d 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.
What it actually says
When documenting skills, verify accuracy against actual implementation:
Type Inventory: Read classifier prompts, keyword fallbacks, and DB enum definitions before documenting enum values or category lists. Types that exist only in documentation but not in code are phantom types — they cause silent failures.
Encryption/Security Claims: Verify against actual cryptography library calls (e.g., "AES-256" claimed but Fernet/AES-128-CBC used). Read imports, not comments.
LLM Classifier Documentation: Document all four of:
- Model — exact model identifier
- Fallback mode — behavior when API key missing or call fails
- Confidence threshold — minimum score to act on a result
- Failure handling — how to handle classification failures
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.
- 2d ago First seen · 19 lines · 202 tokens per session scan A 41a2b5a69322
skill-accuracy is a cursor rule published in the GitHub repository wyattowalsh/agents (5 stars, last pushed 11d ago), licensed MIT. It adds 9 tokens to every session and 202 once invoked, about $0.0000 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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