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 mxslr/mlcraft --skill domain-anomaly-detectiongit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/domain-anomaly-detection)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-anomaly-detection"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-anomaly-detection.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 | $0.00119 | $0.00479 |
| Opus 5 | $0.00060 | $0.00239 |
| Sonnet 5 | $0.00024 | $0.00096 |
| Haiku 4.5 | $0.00012 | $0.00048 |
Grade A, and why
domain-anomaly-detection 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 7d 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
Anomaly Detection - Method Selection
Anomalies are rare and often unlabeled, so most setups are one-class: train on normal data, then flag deviations. Route by data type.
Decision table
| Data type | Recommended (recent) | Notes |
|---|---|---|
| Images or industrial defects | PatchCore (2022) or EfficientAD (WACV 2024) | few or no anomalies at train time. MVTec-style. Metric is image and pixel AUROC, and AUPRO. |
| Time-series or sensors | reconstruction or forecasting residual with a threshold; Anomaly Transformer as a deep option | read the metric caveat below. |
| Tabular (fraud, intrusion) | Isolation Forest and LOF baselines; deep methods (deep SVDD, autoencoder) when data is large | metric is AUROC and PR-AUC. |
| Graph or network | one-class or reconstruction-based GNN | fraud rings, intrusion. |
Cross-cutting practice
- Set the decision threshold on a validation set of known-normal data (plus a few anomalies if available), not on test.
- Imbalance is extreme, so use PR-AUC or AUROC, not accuracy.
- IMPORTANT time-series caveat: the common "point-adjustment" evaluation massively inflates F1 and is misleading. Report PR-AUC, range or affiliation-based metrics, or VUS, and state clearly whether point-adjustment was used.
- Explainability: reconstruction-error maps (images and time-series), SHAP or feature attribution (tabular), and which timestamp or region triggered the alert.
- Improve results: use
accuracy-improvement-loop; evaluate withrigorous-evaluation.
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.
- 7d ago First seen · 24 lines · 119 tokens per session scan A 2d76e3c81113
domain-anomaly-detection is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 2mo ago), licensed MIT. It adds 119 tokens to every session and 479 once invoked, about $0.0006 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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