skills/11-James-Traina-compound-science/skills/workflows-ideate/SKILL.md
Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research workflows:ideateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Divergent exploration before convergent brainstorming. Generate many candidates, then filter ruthlessly.
Read $ARGUMENTS. If the user provides a specific research question, ideate around it. If they provide a broad topic, explore broadly.
Generate 15-20 candidate research directions. Use these research-adapted ideation frames:
Dispatch methods-explorer and literature-scout agents in parallel to ground the ideation in real methods and recent papers.
Iron rule: Generate the full candidate list before critiquing any idea. Push past the first few obvious directions.
Entry condition: Phase 1 produced at least 15 candidate directions (the iron rule). Exit condition: 5-7 survivors identified, all rejected candidates have one-line rejection reasons.
For each candidate, evaluate:
Dispatch identification-critic to attack the top 10 candidates. Only candidates surviving adversarial scrutiny advance.
Target: 5-7 survivors with explicit rejection reasons for all others.
Write the ideation document to docs/ideation/ with YAML frontmatter:
---
status: complete
date: YYYY-MM-DD
topic: <descriptive topic>
candidates_generated: <N>
survivors: <N>
---
Content:
/workflows:brainstorm on the top 1-2 candidatesEnd with: "Ideation complete. Run /workflows:brainstorm [top candidate] to develop requirements for the strongest direction."
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".
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".