Skill UI

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

This skill provides a systematic workflow for diagnosing failures in GAIA benchmark runs. Users can analyze task traces, classify failure modes (e.g., Tool Gap, Reasoning Miss, Extraction Bug), identify root causes, and apply targeted fixes to improve AI agent performance. It is essential for optimizing LLM benchmarks and finding performance regressions.

Guides users through the entire end-to-end process of running the GAIA benchmark. It covers environment validation, cost estimation, model execution, and generating a signed, HAL-compatible submission package for leaderboards. This ensures high integrity and reproducibility for model performance evaluation.