analytics-skills ou everyday-causal-skills

Agent skills that teach Claude, Cursor, and other AI agents to read web analytics like a senior analyst. Five model-invoked skills covering a diagnostic method (MECE hypothesis trees, signal vs noise, Simpson's paradox, Pyramid Principle output), traffic-change diagnosis with fingerprints for common causes, channel and funnel quality analysis, and metric benchmarking by business model. A one-time profile setup captures your business context (model, primary conversion, traffic range, ICP, data stack) so every answer is calibrated to your industry and scale instead of generic averages. Works with any analytics source; first-class support for Clamp MCP with exact tool call-outs at each step of the method.

A causal inference plugin for AI agents. Describe a problem in plain language and it helps you choose a method, check assumptions, write the analysis in R or Python, and stress-test the results. Who this is for: Anyone who needs to measure whether something actually worked, such as Marketing and growth teams; Product managers and BI analysts; Data scientists; Revenue and operations teams; Policy researchers; Students and self-taught practitioners. What you get from this plugin The plugin works in five steps, from refining the question you want to answer, to writing the report. You are free to pick and start from any step you like. → Describe your problem → Get a method recommendation → Check assumptions and structure the analysis → Stress-test the results → Write the executive report