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 tkpratardan/lemma --skill lemma-leakagegit clone --depth 1 https://github.com/tkpratardan/lemmaWrote 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/tkpratardan/lemma/lemma-leakage)<a href="https://agentmods.dev/skills/tkpratardan/lemma/lemma-leakage"><img src="https://agentmods.dev/badge/skills/tkpratardan/lemma/lemma-leakage/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/tkpratardan/lemma/lemma-leakage"><img src="https://agentmods.dev/badge/skills/tkpratardan/lemma/lemma-leakage.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 | $0.00031 | $0.00236 |
| Opus 5 | $0.00015 | $0.00118 |
| Sonnet 5 | $0.00006 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
lemma-leakage 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 9d 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
Leakage audit
Deliver
Rank leakage findings by severity and show reproducible before/after scores, the contaminated path, the corrected validation design, and remaining risk.
Check
- Reconstruct information available at the real prediction moment.
- Audit features and missingness for target proxies and post-outcome facts.
- Verify that imputers, encoders, selection, and scaling are fit within each training fold.
- Check time travel, repeated entities, near duplicates, source overlap, and outcome-dependent sampling.
- Remove suspicious features or strengthen the split and compare with a simple baseline.
Do not rationalize an implausible score, rely only on feature importance, randomly split temporal or repeated-entity data, or continue tuning before material leakage is resolved.
For the full leakage taxonomy, read references/deep-guide.md.
What ships with it
1 file 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.
- 9d ago First seen · 32 lines · 31 tokens per session scan A f06d0694d3f9
lemma-leakage is a skill published in the GitHub repository tkpratardan/lemma (3 stars, last pushed 29d ago), licensed MIT. It adds 31 tokens to every session and 236 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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