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/clima/ensemblekalmanprocesses.jl/math-auditornpx skills add CliMA/EnsembleKalmanProcesses.jl --skill math-auditorgit clone --depth 1 https://github.com/CliMA/EnsembleKalmanProcesses.jlWrote 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/clima/ensemblekalmanprocesses.jl/math-auditor)<a href="https://agentmods.dev/skills/clima/ensemblekalmanprocesses.jl/math-auditor"><img src="https://agentmods.dev/badge/skills/clima/ensemblekalmanprocesses.jl/math-auditor.svg" alt="Measured on agentmods" 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 | $0.00167 | $0.02396 |
| Opus 5 | $0.00084 | $0.01198 |
| Sonnet 5 | $0.00033 | $0.00479 |
| Haiku 4.5 | $0.00017 | $0.00240 |
Grade A, and why
math-auditor 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Math Audit
Adversarial review of a scientific Julia package for mathematical accuracy and consistency — not software architecture (flag architecture only when it causes mathematical wrongness, e.g. mutation aliasing, accidental type demotion, or inconsistent conventions between modules).
The output is written for the package's own developers: findings must cite exact
file:line, state the correct mathematics, and give a concrete failure scenario.
A finding that can't survive an attempt at refutation doesn't ship.
What "adversarial" means here
Each reviewer's job is to break the code, not describe it. Concretely, hunt for:
- Wrong equations: update formulas, gradients, covariances, likelihoods that differ from the cited papers or from the docstring's own LaTeX. Derive the correct expression independently and diff it against the code.
- Convention drift: rows-vs-columns for ensemble members,
N-1vsNnormalization, factor-of-2 / sign errors, CholeskyLvsU, covariance vs precision, whether noise is added in obs-space or transformed space — especially inconsistencies between modules that must agree. - Statistical validity: is added noise sampled with the right covariance and
scaling (e.g.
Δtscaling in stochastic dynamics)? Are means/covariances computed over the right dimension? Deterministic vs stochastic variants actually equivalent in expectation? - Numerical soundness: unguarded
inv/\on possibly-singular matrices, loss of symmetry/PSD-ness, subtraction-based variance formulas, missing regularization,sqrtof negative-by-roundoff eigenvalues. - Edge cases the math must survive: ensemble size 1–2, dimension 1 (scalar vs matrix degeneracy), zero variance, NaN/failed ensemble members, empty minibatches.
- Test-math consistency: do the tests actually pin the mathematics
(analytic solutions, invariants, convergence rates), or just check shapes and
"it runs"? A wrong equation whose test only checks
size()is a double finding: the bug and the missing test.
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.
- 5d ago First seen · 197 lines · 167 tokens per session scan A 90ba4d80e66d
math-auditor is a skill published in the GitHub repository CliMA/EnsembleKalmanProcesses.jl (124 stars, last pushed 4d ago), licensed Apache-2.0. It adds 167 tokens to every session and 2,396 once invoked, about $0.0008 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 2026-08-30.
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