Skill UI

A comprehensive guide for authors submitting papers to web conferences. It details how to structure empirical claims, ensuring evidence matches the claim's scope (scale, freshness, generalizability). The guide covers avoiding methodological flaws like temporal or popularity leakage, selecting datasets with proper provenance, and performing rigorous ablations and statistical analysis for maximum academic rigor.

This guide provides a comprehensive framework for designing rigorous and defensible evaluation sections for machine learning and information retrieval papers, particularly for top-tier conferences like WSDM. It covers the four critical evidence quadrants—Effectiveness, Validity, Efficiency, and Robustness—and details necessary protocols, such as temporal splits, proper baseline selection (including heuristics), ablation studies, and hardware-aware performance reporting. Use this when auditing or writing the experimental section of a research paper.

Displays a dashboard of all autoresearch experiments, showing results, active optimization loops, and progress. Supports per-experiment detail, domain filtering, and format export (Markdown/CSV) for sharing. Useful for monitoring ongoing experiments.

Inspect and manage Caveman Cloud experiment lifecycles safely. Use to start, approve, cancel, promote, or roll back experiments with strict evidence gates and read-only enforcement until server-authoritative controls ship.

This tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.

A comprehensive knowledge base covering the theoretical fundamentals and advanced algorithms of Deep Reinforcement Learning (Deep RL). It details core concepts like Bellman equations, policy gradients, and the trade-offs between policy optimization and Q-learning. The guide analyzes leading algorithms such as PPO, SAC, TRPO, and DDPG, and provides rigorous best practices for debugging and running scientifically sound RL experiments.