Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Read and operationalize handoff artifacts created for a future AI agent, including operational handoff md files and compact conversation-context md files. Use when asked to take over, resume, continue from handoff docs, read AgentHandoff outputs, verify current state before acting, or prepare a safe next-step plan from prior agent documentation.
Create continuation-oriented handoff/progress artifacts for another AI agent to safely resume complex work, including an operational handoff md plus a compact-session-style conversation context md. Use when the user asks to summarize current progress for a future/next/other agent, write an Agent README, update a project handoff, preserve recent conversation/history, record experiment state, preserve cluster/run/debug context, or document what has changed, what is running, known risks, exact next steps, and recent user instructions. Do not use for polished human-facing status reports unless the user explicitly asks for both audiences.
PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.