/anomaly-detection-time-series
Formal time-series methods that augment the hand-coded fingerprint library in traffic-change-diagnosis. Use this skill when traffic-change-diagnosis fingerprints overlap, when the user asks "is this real?", or when the change date is contested. Applies STL decomposition,
$ npx -y skills add clamp-sh/analytics-skills --skill anomaly-detection-time-series --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/anomaly-detection-time-series
Context preview
The summary Claude sees to decide when to auto-load this skill.
Formal time-series methods that augment the hand-coded fingerprint library in traffic-change-diagnosis. Use this skill when traffic-change-diagnosis fingerprints overlap, when the user asks "is this real?", or when the change date is contested. Applies STL decomposition,
Analytics skills for Claude, Cursor, and other AI agents. Read web analytics like a senior analyst: diagnose traffic changes, judge channel quality, read funnels, declare typed events, and read A/B tests without the usual rookie mistakes.
Repo: clamp-sh/analytics-skills
Other skills on analytics-skills.
- /analytics-diagnostic-method
The spine of analytics investigation. Use whenever interpreting analytics numbers, answering "why did X change", reading funnels, comparing cohorts, or presenting findings. Teaches a five-step method (load profile, frame the question, build a MECE hypothesis tree, triangulate,
Open skill - /analytics-profile-setup
One-time interview that captures the business context (industry, model, primary conversion, traffic range, ICP, data stack) into a local analytics-profile.md file. Every other analytics skill reads this file so its answers are calibrated to the right benchmarks and terminology
Open skill - /bayesian-experiment-reader
Bayesian counterpart to experiment-result-reader. Computes posterior P(variant beats control), credible intervals, and expected loss from per-variant exposure and conversion data. Beta-Binomial for proportion metrics (CVR), Normal-Normal for continuous metrics (revenue per
Open skill - /causal-dag-builder
Build a refinable causal DAG before answering "did X cause Y" on observational data. Emits a Mermaid diagram of assumed causes, applies the back-door criterion to decide what to condition on, and forces confounders, mediators, and colliders to be named explicitly instead of
Open skill - /causal-evidence-checklist
Bradford Hill's 9 viewpoints (1965) reframed as a checklist for product analytics. Use this skill before recommending a decision based on observational analytics data. Applies the 9 Bradford Hill viewpoints to score whether X actually caused Y, or whether the correlation is
Open skill - /causal-query-classifier
Pearl's three-rung causal hierarchy as a query classifier. Tags every analytics question as rung-1 (association, P(Y|X)), rung-2 (intervention, P(Y|do(X))), or rung-3 (counterfactual, P(Y_x|Y',X')) before answering. Refuses to escalate a rung-1 observational finding into a
Open skill