devils-advocate

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Adversarially challenge research assumptions, mechanisms, and arguments in writing. Use when stress-testing a claim or design before committing to it. Not for an interactive oral drill or a full referee report; use $grill-me or a review agent.

AI & Automation 144 stars 27 forks Updated 3 days ago MIT

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Skill Content

# Devil's Advocate Skill > Challenge research assumptions and identify weaknesses in your arguments. ## Purpose Based on Scott Cunningham's Part 3: "Creating Devil's Advocate Agents for Tough Problems" - addressing the "LLM thing of over-confidence in diagnosing a problem." **For formal code audits with replication scripts and referee reports, use the Referee 2 agent instead (`.claude/agents/referee2-reviewer.md`).** This skill is for quick adversarial feedback on arguments, not systematic audits. ## When to Use - Before submitting a paper - When stuck on a research problem - When you want to stress-test an argument - During paper revision planning ## When NOT to Use - **Code audits** — use the Referee 2 agent instead - **Replication verification** — use the Referee 2 agent instead - **Quick proofreading** — just ask for a read-through - **When you want validation** — this skill is designed to challenge, not affirm ## Workflow 1. **Understand the claim** — Read the paper/argument being evaluated 2. **Generate competing hypotheses** — If evaluating a research question or design, load `references/competing-hypotheses.md` and generate 3-5 rival explanations before critiquing 3. **Run the debate** — Use the multi-turn debate protocol below (default) or single-shot mode for quick checks 4. **Deliver the verdict** — Synthesize surviving critiques with severity ratings ## Multi-Turn Debate Protocol (Default) Inspired by the simulated scientific debates in Google's AI Co-...

Details

Author
flonat
Repository
flonat/flonat-research
Created
7 months ago
Last Updated
3 days ago
Language
Python
License
MIT

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