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 goiltpatpat/patpat --skill patpat-learngit clone --depth 1 https://github.com/goiltpatpat/patpatWrote 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/goiltpatpat/patpat/patpat-learn)<a href="https://agentmods.dev/skills/goiltpatpat/patpat/patpat-learn"><img src="https://agentmods.dev/badge/skills/goiltpatpat/patpat/patpat-learn/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/goiltpatpat/patpat/patpat-learn"><img src="https://agentmods.dev/badge/skills/goiltpatpat/patpat/patpat-learn.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 | $0.00040 | $0.00371 |
| Opus 5 | $0.00020 | $0.00186 |
| Sonnet 5 | $0.00008 | $0.00074 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
patpat-learn 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 8d 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
Patpat Learn
When invoked directly, read the operating protocol in full. Do not load the router.
Read encode lessons, repository truth, and apply the learning playbook.
Use evidence from the active task: user corrections, failed verification, repeated retries, review findings, or verifier defects. Separate one-off facts from recurring failure modes. Encode only the latter.
Choose the strongest narrow mechanism that prevents recurrence: type or contract, focused test, lint or validator rule, deterministic script, workflow instruction, then documentation. Prefer enforcement over reminders and update an existing authoritative location before creating a new file. Mine this conversation for recurring working-style or failure rules. Propose edits to existing files only. Present the proposal and wait for approval. Do not auto-apply. There is no new SKILL.md and no *-mode mint on this path. Never create a new SKILL.md. Never mint a personal *-mode skill.
Do not mine unrelated conversations or store sensitive task data. Do not modify shared or user-global rules without explicit authority. Verify that the chosen mechanism detects or prevents the original failure, then report what remains unprotected.
Proof closure
Close repository mutations through:
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.
- 8d ago Changed bb4d68765302
- 12d ago First seen · 24 lines · 40 tokens per session scan A 2cdaf2704cad
patpat-learn is a skill published in the GitHub repository goiltpatpat/patpat (1 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 371 once invoked, about $0.0002 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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