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 clamp-sh/analytics-skills --skill anomaly-detection-time-seriesgit clone --depth 1 https://github.com/clamp-sh/analytics-skillsWrote 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/clamp-sh/analytics-skills/anomaly-detection-time-series)<a href="https://agentmods.dev/skills/clamp-sh/analytics-skills/anomaly-detection-time-series"><img src="https://agentmods.dev/badge/skills/clamp-sh/analytics-skills/anomaly-detection-time-series/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/clamp-sh/analytics-skills/anomaly-detection-time-series"><img src="https://agentmods.dev/badge/skills/clamp-sh/analytics-skills/anomaly-detection-time-series.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 | $0.00182 | $0.05405 |
| Opus 5 | $0.00091 | $0.02703 |
| Sonnet 5 | $0.00036 | $0.01081 |
| Haiku 4.5 | $0.00018 | $0.00541 |
Grade A, and why
anomaly-detection-time-series 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 11d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anomaly detection for time series
The fingerprint library in traffic-change-diagnosis is a fast first pass: it pattern-matches the shape of a drop against twelve common causes. It is right most of the time and wrong when it matters most — when two fingerprints fit the same shape, when the user's eyeballed change date is off by a day, or when a counterfactual baseline is needed instead of a slope match. This skill adds six formal methods that resolve those cases. None of them replace the fingerprints; they adjudicate when fingerprints conflict.
When NOT to use this
- The fingerprint match is unambiguous (single hypothesis, >0.7 confidence in the diagnosis worksheet). Running STL or BOCD on a clean tracking-regression drop is ceremony.
- The series is shorter than 14 days. STL needs at least two full seasonal cycles; BOCD's posterior is noisy with less than ~30 observations; Prophet needs more history than that to fit changepoints. For short series, stick to
analytics-diagnostic-method's denominator + sample-size discipline. - The user wants a real-time alert on a single metric and hasn't already decided what "anomalous" means. That's a monitoring-design conversation, not a detection method; load
sequential-monitoringinstead. - The metric is count-based with most days at zero (e.g. a niche conversion event). STL and Prophet assume continuous-ish residuals; you'll get nonsense decompositions. Use Poisson-tail tests instead.
Pick one method per question
Do not run all six on every series. Each method answers a different question. Pick by question shape:
| Question | Method | Output |
|---|---|---|
| Is the change date the user gave actually the change date? | BOCD | Posterior P(changepoint = day t) for every t |
| Is today's number real, or is Tuesday always like this? | STL decomposition | Residual z-score against seasonal baseline |
| Where would the series be without the drop? | Prophet | Forecast interval treating pre-drop as baseline |
| Is this hour anomalous, given the usual hour-of-day spread? | Quantile regression | P10/P50/P90 bands per hour |
| Should the alert fire now or wait for more data? | SPRT | Log-likelihood ratio crossing a Wald boundary |
| X and Y both happened this week — did X precede Y predictively? | Granger causality | F-test on lagged regressors |
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.
- 11d ago First seen · 256 lines · 182 tokens per session scan A a4b008c4b490
anomaly-detection-time-series is a skill published in the GitHub repository clamp-sh/analytics-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 182 tokens to every session and 5,405 once invoked, about $0.0009 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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