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Found 7 Skills
Perform autonomous, multi-step research using the Gemini Deep Research Agent (Interactions API). Supports web search, file/directory context, and resilient streaming.
Orchestrate parallel scientist agents for comprehensive analysis with AUTO mode
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
Run the hive experiment loop — autonomous iteration on a shared task. Use when the agent is in a hive task directory and needs to run experiments, submit results, or participate in the swarm. Triggers on "hive", "run hive", "autoresearch", "start experimenting", "join the swarm", "start the loop", or when .hive/task file is detected.
Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.
Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop). Goes from a broad research direction all the way to a submission-ready paper. Use when user says "全流程", "full pipeline", "从找idea到投稿", "end-to-end research", or wants the complete autonomous research lifecycle.
Use when setting up an autonomous ML experimentation harness (an "autoresearch" loop) — a fixed harness that owns data + metrics plus a hackable training script an LLM iterates on under a fixed time budget. Use when scaffolding harness.py/train.py/program.md, designing structured training-dynamics logging for agents to read, or enforcing that experiments cannot game the metric.