Awesome-Agent-Skills-for-Empirical-Research workflows:ideate

Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest

install
source · Clone the upstream repo
git clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/11-James-Traina-compound-science/skills/workflows-ideate" ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-workflows-ideate && rm -rf "$T"
manifest: skills/11-James-Traina-compound-science/skills/workflows-ideate/SKILL.md
source content

Research Ideation

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:

  1. Identification weakness — What existing results have weak identification? What new variation could fix it?
  2. Computational bottleneck — What problems are infeasible with current methods but tractable with new estimators or hardware?
  3. Data limitation workaround — What would become possible with data that is now available but underexploited?
  4. Alternative estimator class — What if the standard approach (e.g., linear IV) were replaced with a different class (e.g., ML, structural, Bayesian)?
  5. Relaxed assumption — What results depend on assumptions that could be relaxed? What happens when you relax them?
  6. 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:brainstorm
    on the top 1-2 candidates

Handoff

End with: "Ideation complete. Run

/workflows:brainstorm [top candidate]
to develop requirements for the strongest direction."