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.
Claude Code
> /plugin marketplace add clamp-sh/analytics-skills> /plugin install analytics-skills@clamp-sh
Codex
$ npx skills add clamp-sh/analytics-skills -a codex
Cursor
$ npx skills add clamp-sh/analytics-skills -a cursor
Gemini CLI
$ npx skills add clamp-sh/analytics-skills -a gemini-cli
GitHub Copilot
$ copilot plugin marketplace add clamp-sh/analytics-skills$ copilot plugin install analytics-skills@clamp-sh
OpenCode
$ npx skills add clamp-sh/analytics-skills -a opencode
Windsurf
$ npx skills add clamp-sh/analytics-skills -a windsurf
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. Ships with tool-maps for Amplitude, Clamp, GA4, Mixpanel, and PostHog; add your own under tool-maps/. The schema-authoring and experiment-reading skills are built on the open Event Schema spec.
| Skill | What it does |
|---|---|
analytics-profile-setup | One-time interview that captures your business context (model, primary conversion, traffic range, ICP, data stack) into a local analytics-profile.md. Every other skill reads this file so answers are calibrated to your industry and scale. Run this first. |
analytics-diagnostic-method | The spine. Five-step method: load profile, frame the question, build a MECE hypothesis tree, triangulate, present with the Pyramid Principle. Covers signal vs noise and Simpson's paradox. Referenced by every other skill. |
traffic-change-diagnosis | Drill path for "why did traffic change". Fingerprints for tracking regressions, bot spikes, deploy-correlated drops, campaign ramps, SEO decay, and platform changes. Measurement checks first, always. |
channel-and-funnel-quality | Volume × engagement × conversion as a matrix. Vanity-traffic detection. Expected drop-off ranges per funnel step type. Mix-shift handling. Industry-specific benchmarks. |
metric-context-and-benchmarks | What's a good bounce / engagement / duration / CVR / churn / LTV:CAC / activation, by model. When each metric lies. Minimum sample sizes before trusting a rate. |
event-schema-author | Authors event-schema.yaml from existing track() calls. A portable, typed declaration of every product analytics event the codebase fires. The CLI generates a TypeScript type so call sites are autocompleted and type-checked at build time. Vendor-neutral; works with any analytics SDK. |
experiment-result-reader | Read a running A/B test honestly. Pulls per-variant exposure and conversion counts, computes lift, applies sample-size and sequential-testing discipline, checks for mix-shift and sample-ratio mismatch, and returns a verdict with caveats instead of a false-positive. Reads the experiment from the experiments: section of event-schema.yaml when present. |
bayesian-experiment-reader | Bayesian counterpart to experiment-result-reader. Beta-Binomial for CVR, Normal-Normal for continuous. Returns P(variant beats control), credible intervals, and expected loss. Ship-decision rule: ship if P(better)>95% AND expected loss < tolerance, instead of "stat sig yes/no." |
sequential-monitoring | mSPRT and confidence sequences for honest peeking. Answers "can I call this early?" without α-inflation from looking at the dashboard every day. Pairs with both experiment readers. |
causal-query-classifier | Pearl's three-rung hierarchy as a query classifier. Tags every analytics question as rung-1 (association), rung-2 (intervention), or rung-3 (counterfactual). Refuses to escalate rung-1 findings into rung-2 recommendations without an identification strategy. |
causal-dag-builder | Emits a Mermaid causal DAG before answering "did X cause Y" on observational data. Applies the back-door criterion to pick the adjustment set. Surfaces confounders, mediators, and colliders explicitly instead of "controlling for everything." |
causal-evidence-checklist | Bradford Hill's 9 viewpoints as a rubric before recommending a rollback/ship action. Refuses "high confidence" verdicts when fewer than ~5 criteria pass. |
anomaly-detection-time-series | STL decomposition, Bayesian online changepoint detection, Prophet, quantile regression, SPRT, Granger causality. Use when traffic-change-diagnosis fingerprints match ambiguously, the change date is contested, or the user asks "is this real?" |
/plugin marketplace add clamp-sh/analytics-skills
/plugin install analytics-skills@clamp-sh
Skills auto-load and become available as /analytics-skills:<name> when the task matches.
npx skills add clamp-sh/analytics-skillsgit clone https://github.com/clamp-sh/analytics-skills.git then cp -R analytics-skills/skills/* ~/.codex/skills/git clone then cp -R skills/* ~/.claude/skills/These are model-invoked skills. You don't need to call them by name; just ask real analytics questions and Claude will load the relevant skill.
First run, on a new project:
Set me up. I want the skills calibrated to my business.
This loads analytics-profile-setup, walks a 5-minute interview, and writes analytics-profile.md to the repo root. Every subsequent question gets industry-aware answers.
After setup, ask real questions:
Traffic dropped 30% on Tuesday. What happened?
Loads traffic-change-diagnosis. Walks the hypothesis tree (measurement → time-shape → channel → cohort → content), pulls numbers from your analytics source, returns a diagnosis. Not a screenshot of a chart.
Is our signup funnel broken? Pricing → checkout is converting at 14%.
Loads channel-and-funnel-quality and metric-context-and-benchmarks. Compares against expected step drop-off, slices by cohort, flags whether 14% is low, normal, or suspicious given your sample size and model.
Our bounce rate is 68%. Is that bad?
Loads metric-context-and-benchmarks. Handles the GA4 vs UA definition gotcha, looks up the relevant page-type range, flags the sample-size caveat if needed.
The skills are platform-neutral; per-platform MCP invocations live in tool-maps/. One file per supported analytics tool, all covering the same canonical 17-row workflow taxonomy.
| Tool | Tool-map | Surface |
|---|---|---|
| Amplitude | amplitude.md | query_amplitude_data covers most rows; cohorts and experiments are dedicated tools |
| Clamp | clamp.md | dedicated MCP tool per row of the canonical taxonomy |
| GA4 | ga4.md | run_report for aggregate rows; funnels and cohort retention not exposed by the wrapper |
| Mixpanel | mixpanel.md | Run-Query types (insights, funnels, flows, retention) |
| PostHog | posthog.md | Trends/Funnels/Retention/Paths insights plus HogQL |
The full row-by-row coverage matrix is at tool-maps/capability-matrix.md. analytics-profile-setup records the active platform in analytics-profile.md under tool_map:; downstream skills load the matching tool-map automatically.
Using a different analytics source? The method still applies. Add a tool-map for it (see tool-maps/README.md for the template).
Issues and PRs welcome. See CONTRIBUTING.md for how to add a skill and the style conventions we follow.
MIT. See LICENSE.
Frameworks and benchmarks the skills lean on:
Stars on GitHub
Reads text from scanned pages, photos and PDFs and returns it as structured Markdown or JSON, so you can pull tables, formulas and headers out of a document without retyping them. Covers many languages and works as input for AI tools.
@paddlepaddleGives your AI assistant a memory that persists across sessions, so it remembers your preferences and earlier work in this repository instead of starting fresh. Search, forget, pause and resume memories with simple commands.
@mem0aiGives AI agents a library of ready-made skills for scientific work such as metabolic flux analysis, protein experiment design, time series modeling, and analytical method validation, so research tasks need less hand-built code. It works with any agent that supports the open Agent Skills standard.
@k-dense-aiGives Claude Code skills, agents, and commands for PostHog analytics, session replay, feature flags, error tracking, and LLM observability. Includes MCP server setup and project access checks.
@posthogAnalytics 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. Install it with the command on its page. It includes analytics-diagnostic-method, analytics-profile-setup, anomaly-detection-time-series. Its skills do not fire on their own yet. Free and open source.
Is this plugin yours?
Claim it with GitHubSubmit a pluginPromote it