RigorPilot Skills Research first Agent Skills for Deep Learning Experiments. Main idea: RigorPilot keeps AI assisted deep learning research grounded in comparability, reproducible evidence, and auditable changes while an agent reproduces, improves, or explores a research repository. Not just higher scores. Meaningful deep learning research progress. English 简体中文 ⚡ At a Glance Focus Summary 🧭 Purpose Research first workflow skills for deep learning experiments, not a generic coding agent or score chasing framework. 🔒 Default rule trusted by default : ambiguous requests route to reproduction, setup, run, train, analysis, or safe debugging. 🧪 Exploration boundary Explore work starts only when the researcher explicitly authorizes candidate only exploration. 📄 Flagship output Every reproduction ends with an annotated README: your README replayed verbatim with color coded, evidence linked per section results. 🧠 Thinking loop Exploration follows a greedy, evidence anchored research cycle: observe → ground → hypothesize → design → run → fair compare → keep or roll back. 🌱 Continuous learning An immutable rigor core plus a user owned lessons overlay that personalizes safely with use. 📦 Evidence outputs Artifacts are written to repro outputs/ , analysis outputs/ , train outputs/ , debug outputs/ , explore outputs/ , and related directories. 🌐 Works across agents Skills follow the Agent Skills open standard (Claude Code, Codex, Cursor, VS Code, Gemini CLI, …); a root AGENTS.md routes any AGENTS.md aware agent. 🚀 Start Fast Most users only need one of these commands: Goal Command Install the full RigorPilot skill set npx skills add lllllllama/rigorpilot skills all Install the trusted reproduction entrypoint npx skills add lllllllama/rigorpilot skills skill ai research reproduction Install the explicit exploration entrypoint npx skills add lllllllama/rigorpilot skills skill ai research explore Claude Code project commands: /ai research reproduction /ai research explore /analyze