skills/68-research-productivity-skills/do-agent/SKILL.md
This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research do-agentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Explore and ultrathink to design a systematic multi-agent, multi-stage (up to 10 subagents in parallel in each stage) execution plan to accomplish: $ARGUMENTS
执行模式:explore → ultrathink → plan → track → execute → review → revise → deliver final output
Note:
agent_tasks/{short task description}_yyyyddmmhh/ 作为临时工作空间(if a folder with this name already exists, make a new one to avoid overwriting existing materals)agent_tasks/{short task description}_yyyyddmmhh/plan.mdtools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
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".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
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".