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Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).

Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when facing complex risk scenarios, strategic decisions with major downside, or when the user asks 'what if X AND Y both happen?'

Premium FIFA World Cup 2026 market & match intelligence — a hosted, read-only layer that fuses official match truth (fixtures, standings, squads, injuries, player performance) with live prediction markets (Kalshi + Polymarket: prices, order books, price history, movers, cross-venue edges) and AI-grounded context (prematch briefs, move explanations, fan/social pulse). Every entity carries a canonical machina URN cross-walked across api-football, sportradar, opta, entain and ESPN, so a market resolves to a fixture resolves to two teams. This skill is prompt-only and premium: it routes the agent to the hosted World Cup Intelligence project (a per-project Machina MCP server) via `machina-cli`. It runs no code locally and ships no API keys. Use when: the user wants World Cup 2026 odds + match context together, asks "what moved and why", wants a grounded market brief or fan-sentiment read on a fixture, or needs one stable id that joins markets ↔ fixtures ↔ teams across providers. Don't use w

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Designs an A/B test or experiment with variants, success metrics, sample size, and duration for an existing hypothesis. Use when planning an experiment to validate a product change or test an assumption you have already framed. To articulate the hypothesis itself first, use define-hypothesis.

Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.