Skill UI

Design statistically valid A/B and multivariate tests: formulate hypotheses, choose metrics, calculate sample size, allocate traffic, run experiments, and analyze results for growth.

This stub experiment generator is designed for the meta-paper-write demo. It takes a topic phrase and deterministically produces a 20-row CSV file containing baseline and generated results. This ensures stable and reproducible results for demonstration purposes, but should not be used for real scientific data.

Plan experiments before data collection: choose designs, randomize, block, and generate reproducible DOE layouts to avoid confounding and pseudoreplication.

Runs autonomous optimization loops on artifacts via Hypothesis Tree Refinement: persistent hypothesis trees, isolated git worktree experiments, evidence propagation, and a held-out merge gate to prevent overfitting. For model training, agent harnesses, data synthesis, and benchmarks.

A comprehensive guide and playbook for managing feature flags across services or teams. It covers flag taxonomy (Release, Experiment, Ops, Permission), naming conventions, safe rollout strategies, creation checklists, and cleanup policies to prevent technical debt. Ideal for standardizing feature flag practices and governance.

This skill analyzes comprehensive sprint delivery data, including ticket completion rates, carry-over analysis, and unplanned work. It generates a structured, data-grounded retrospective brief, transforming raw metrics into concrete discussion prompts (Start/Stop/Continue) and actionable, testable experiments for the next iteration. It helps teams focus on measurable solutions rather than subjective debates.

This template guides the process of turning raw A/B test results, growth experiments, or product feature data into a clear, actionable decision report. It structures the analysis beyond mere metrics by forcing the inclusion of hypothesis, setup details, result interpretation, and a final, justified decision (Ship/Stop/Iterate). Essential for Product Managers and Growth Teams.

This tool allows users to create and manage advanced WASM agents within a secure, sandboxed environment. It ensures complete isolation during code execution, making it ideal for running untrusted code, experimenting with complex agent configurations, or developing portable applications that require guaranteed safety without accessing the host system resources.

This tool provides a comprehensive framework for designing rigorous A/B and multivariate experiments. It guides users through defining hypotheses, calculating statistically necessary sample sizes (handling absolute vs. relative MDE), designing variants, setting metrics, and creating detailed monitoring and stopping rules for CRO experimentation.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

This skill helps researchers strengthen AAAI paper submissions by ensuring reproducibility standards are met. It covers experimental traceability, seed reporting, compute disclosure, and dataset licensing. Users learn to map claims to evidence, audit checklists, and prepare artifacts for Phase-1 reviewers to validate rigor across AI scopes.

Provides comprehensive guidelines for designing, auditing, and reporting high-rigor multi-agent experiments, especially in game theory and self-play systems. It emphasizes testing the *interaction* claim—such as convergence to equilibrium, truthful behavior, or emergent cooperation—against held-out opponents, populations, and strategic deviations, moving beyond simple single-agent leaderboard scores.