workflows:ideate
SkillDev toolsDivergent research ideation, generate many candidate directions, then adversarially filter to the strongest
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What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/11-James-Traina-compound-science/skills/workflows-ideate/SKILL.md and read by Ahel’s review.
Divergent exploration before convergent brainstorming. Generate many candidates, then filter ruthlessly.
Phase 0: Scope the Ideation
Read $ARGUMENTS. If the user provides a specific research question, ideate around it. If they provide a broad topic, explore broadly.
Phase 1: Generate Candidates (Divergent)
Generate 15-20 candidate research directions. Use these research-adapted ideation frames:
- Identification weakness — What existing results have weak identification? What new variation could fix it?
- Computational bottleneck — What problems are infeasible with current methods but tractable with new estimators or hardware?
- Data limitation workaround — What would become possible with data that is now available but underexploited?
- Alternative estimator class — What if the standard approach (e.g., linear IV) were replaced with a different class (e.g., ML, structural, Bayesian)?
- Relaxed assumption — What results depend on assumptions that could be relaxed? What happens when you relax them?
- Literature gap — What do practitioners need that academics haven't provided? What do adjacent fields know that this field doesn't?
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.
Phase 2: Adversarial Filter (Convergent)
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:
- Feasibility (0-100): Can this be done with available data, methods, and time?
- Contribution (0-100): Would a top journal care about this result?
- Identification (0-100): Is there a credible identification strategy?
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.
Phase 3: Output
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:
- Surviving candidates ranked by (Contribution x Identification x Feasibility)
- For each survivor: 2-3 sentence description, confidence score, key risk
- Rejected candidates with one-line rejection reason
- Recommended next step:
/workflows:brainstormon the top 1-2 candidates
Handoff
End with: "Ideation complete. Run /workflows:brainstorm [top candidate] to develop requirements for the strongest direction."
Signals
- GitHub stars
- 5k
- Forks
- 536
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
workflows-ideate- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
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