clamp-sh

metric-context-and-benchmarks

Interpret analytics metrics with correct context. Use when the user asks "is this good", "what's a normal X", or quotes a rate without denominator. Covers realistic ranges for bounce rate, engagement, session duration, pages per session, conversion rate by model type, SaaS unit economics (LTV:CAC, CAC payback, MRR churn, activation, retention), plus when each metric lies and minimum sample sizes.

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, present with Pyramid Principle), how to separate signal from noise, and how to spot Simpson's paradox before it misleads you.

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 instead of generic averages.

channel-and-funnel-quality

Judge whether traffic is actually valuable and whether funnel drop-off is real or expected. Use when comparing marketing channels, reading a conversion funnel, or deciding where to invest. Covers volume × engagement × conversion as a matrix, vanity-traffic detection, expected step drop-off by funnel type, cohort decomposition, and mix-shift (Simpson's paradox) handling.

API & Backend Listed

event-schema-author

Author and maintain an event-schema.yaml file. A portable, typed declaration of every product analytics event the codebase fires (event names, properties, types, intent). The CLI generates a TypeScript type from it so tracking calls are autocompleted and type-checked at build time. Vendor-neutral; works with any analytics SDK (Clamp, GA4, Mixpanel, Amplitude, PostHog, Segment).

experiment-result-reader

Read the result of a running A/B test honestly. Pulls per-variant exposure and conversion counts, computes lift, applies sequential-testing and sample-size discipline, and surfaces the result in plain language without over-claiming. Built on the experiments section of the event-schema spec; works with any platform that fires a canonical exposure event ($exposure, $experiment_started, or equivalent).

traffic-change-diagnosis

Diagnose why website traffic changed. Use when the user asks "why did traffic drop/spike", investigates an anomaly, or wants to separate tracking regressions from real behaviour changes. Walks a hypothesis tree (measurement → time-shape → channel → cohort → content), recognises common fingerprints (bot spike, tracking regression, deploy-correlated drop, SEO decay, campaign ramp), and applies sample-size discipline.

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, Bayesian online changepoint detection, Prophet, quantile regression, sequential probability ratio test, and Granger causality. Use whenever interpreting a series where day-of-week confounds an eyeballed drop, where two candidate causes share a week, or where an alert needs to fire before an analyst sees the chart. Pairs with analytics-diagnostic-method for the surrounding investigation and with sequential-monitoring for the SPRT details. Triggers when Clamp MCP traffic_timeseries returns a series spanning more than 14 days, or when via Clamp the user shares a daily/hourly metric history that needs a non-eyeball verdict.

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 user). Decision rule combines a confidence threshold with an expected-loss tolerance, so the ship decision reflects both "how likely is this better?" and "how bad is it if I'm wrong?". Use this skill alongside experiment-result-reader when reading any A/B test result. Pairs with analytics-diagnostic-method. Use whenever interpreting an A/B test result the user plans to ship from, when the question is "what's the chance variant wins?", or when a frequentist p-value is on the edge and the user wants the posterior view. Triggers when Clamp MCP returns experiment exposure and conversion data, or when any analytics source surfaces per-variant counts.

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 "controlling for everything". The DAG is an artifact downstream skills read to pick the right adjustment set. Use whenever interpreting cohort comparisons, funnel drop-offs, or any analytics result where the user is reasoning causally without an experiment. Pairs with analytics-diagnostic-method as the causal-structure layer underneath the diagnostic tree. Triggers when Clamp MCP cohorts_compare or funnels_create is called with no experimental holdback, when a Clamp MCP result prompts a causal claim from observational data, or when via Clamp the user asks "why did this segment convert higher".

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 coincidental, confounded, or reversed. Use whenever interpreting a metric change the user is about to act on (rollback, ship, abandon, double-down). Refuses to label a verdict "high confidence" when fewer than ~5 of the 9 criteria pass. Pairs with analytics-diagnostic-method (which provides the hypothesis tree) and channel-and-funnel-quality (which provides the segmentation discipline). Triggers when Clamp MCP returns a comparison the user is about to act on, when a deploy correlates with a metric move, or when the user says "X caused Y" / "did X cause Y" / "should we roll back / ship / kill X" based on a chart. Vendor-neutral methodology; via Clamp MCP the per-criterion checks map directly to traffic.compare, traffic.breakdown, errors.tim

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 rung-2 ship/kill recommendation without naming an identification strategy (back-door, instrumental variable, DiD, RDD, synthetic control). Use this skill whenever interpreting an analytics question that asks why or what-if, to classify it on Pearl's causal hierarchy before answering. Pairs with analytics-diagnostic-method. Triggers when Clamp MCP returns a comparison or trend that the user is about to act on, so the agent labels the claim's rung explicitly instead of laundering correlation into causation. Works with any observational source; Clamp MCP is the canonical integration via traffic.compare, funnels.list, and cohorts.compare.

sequential-monitoring

Always-valid sequential inference for honest peeking at running A/B tests. Applies mSPRT (mixture Sequential Probability Ratio Test) or confidence sequences so the false-positive rate stays at the nominal alpha even when the test is checked daily. Use this skill when the user asks if it's safe to call an A/B test early, or to peek-check a running test. Pairs with experiment-result-reader and bayesian-experiment-reader, and with analytics-diagnostic-method for the framing discipline. Triggers when Clamp MCP returns mid-experiment exposure and conversion counts, or when a user references peeking, early stopping, sequential testing, alpha-spending, mSPRT, or confidence sequences. Vendor-neutral methodology; works with any analytics source, with Clamp MCP as the canonical integration. Use whenever interpreting an in-flight experiment where the planned horizon has not been reached but the user wants a stop/continue decision.