learning-pi-through-force
learning-pi-through-force contiene 4 skills recopiladas de muellerzr, con cobertura ocupacional por repositorio y páginas de detalle dentro del sitio.
Skills en este repositorio
Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification via an 8-phase pipeline (Scope, Plan, Retrieve, Triangulate, Synthesize, Critique, Refine, Package). Use when you need comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.
Safely find and fetch arXiv papers using the structured arXiv API and the /html/ endpoint. Use instead of guessing arXiv URLs (which often 404). Invoke with an arXiv ID, an arXiv URL, or a search query like "GLM-4.5 technical report".
Efficiently fetch HuggingFace model data (README, config.json, and optional predecessor config) using parallel curl. Use at the START of any model research task. Invoke with a HF model ID like Qwen/Qwen3.5-397B-A17B, optionally followed by a predecessor ID.
Research a newly released Hugging Face model and produce a comprehensive deep-research report (model card + technical reports + novelty analysis vs predecessor + glossary + benchmark analysis), exported as markdown + PDF + metadata.json, optionally emailed. Invoke with a HuggingFace model ID like moonshotai/Kimi-K2-Thinking. Use when the user gives a HF model identifier or asks to deeply research a model release.