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 agentlas-ai/agentlas-desktop --skill hepdata-chi-squaregit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/hepdata-chi-square)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/hepdata-chi-square"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/hepdata-chi-square/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/agentlas-ai/agentlas-desktop/hepdata-chi-square"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/hepdata-chi-square.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 | $0.00030 | $0.00236 |
| Opus 5.5 | $0.00012 | $0.00094 |
| Sonnet 5.5 | $0.00006 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
hepdata-chi-square 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 1mo 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
HEPData chi-square
- Bind an untampered
agentlas.physics.hepdata-table/v1artifact and choose one dependent-series index. - Supply exactly one prediction per point, preserving missing predictions as
null; prediction units must exactly equal the HEPData series units. - Select only uncertainty labels that the scientific model explicitly treats as mutually independent. The tool combines those components in quadrature and uses the asymmetric side pointing from the measurement toward the prediction.
- Inspect exclusions, residuals, pulls, chi-square contributions, degrees of freedom, reduced chi-square, and p-value. A missing selected component, zero uncertainty, unit mismatch, tampered hash, or nonpositive degrees of freedom fails closed.
- Preserve
sourceLineage.normalizedTableSha256andanalysisSha256with the publication table and Vega artifacts.
Do not call this a fit. The tool does not estimate parameters or infer covariance, correlation, nuisance parameters, or prediction uncertainty.
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.
- 1mo ago First seen · 15 lines · 30 tokens per session scan A f40924e4f683
hepdata-chi-square is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 236 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 2026-09-04.
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