# Devils Advocate

> Use when you need to challenge research assumptions or stress-test arguments.

- Skill: `lhh666-6/devils-advocate` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add lhh666-6/devils-advocate`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lhh666-6/devils-advocate/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: lhh666-6 (https://skillmd.com/u/lhh666-6)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/lhh666-6/devils-advocate

---


# 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 (`.Codex/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-Scientist. A one-shot critique is easy for an LLM to produce but often superficial. Multi-turn debates force each critique to survive a defense, filtering out weak objections and sharpening the strong ones.

### Round 1: Adversarial Critic

Adopt the persona of a hostile but competent reviewer. Challenge on:
1. **Theoretical foundations** — Are the assumptions justified?
2. **Methodology** — Limitations? Alternative approaches?
3. **Data** — Selection bias? Measurement issues? External validity?
4. **Causal claims** — Alternative explanations? Confounders?
5. **Contribution** — Novel enough? Does it matter?

Produce **numbered critiques** (aim for 5-8), each with a concrete statement of the problem.

### Round 2: Defense

Switch persona to the paper's author. For each numbered critique, provide the strongest possible defense:
- Cite evidence from the paper that addresses the concern
- Explain design choices that mitigate the issue
- Acknowledge limitations honestly where the defense is weak
- Propose concrete fixes where the critique has merit

### Round 3: Adjudication

Switch to an impartial senior reviewer. For each critique-defense pair, rule:
- **Critique stands** — the defense is insufficient; this is a real weakness
- **Critique partially addressed** — defense has merit but issue remains
- **Critique resolved** — the defense adequately addresses the concern

### Final Synthesis

Produce a structured report with only the surviving critiques (stands + partially addressed), ranked by severity:

```markdown
## Devil's Advocate Report

### Critical (must fix before submission)
1. [Critique] — [Why the defense failed] — [Suggested fix]

### Major (reviewers will likely raise)
2. [Critique] — [What remains after defense] — [Suggested fix]

### Minor (worth acknowledging)
3. [Critique] — [Residual concern] — [How to preempt]

### Dismissed
- [Critiques that were resolved in Round 2, listed briefly for transparency]
```

## Single-Shot Mode

For quick checks (e.g., "just poke holes in this argument"), skip the multi-turn protocol and produce a direct critique. Use when the user says "quick", "just challenge this", or the input is a paragraph rather than a full paper.

## Example Use

"Play devil's advocate on my research paper about preference drift - specifically challenge my identification strategy and the assumptions about utility functions."

---

## Council Mode (Optional)

For the highest-stakes arguments, run the devil's advocate debate across multiple LLM providers. Different models have genuinely different reasoning patterns — a critique that Codex finds weak, GPT may find devastating, and vice versa. This produces adversarial tension that a single model cannot replicate internally.

**Trigger:** "Council devil's advocate" or "thorough challenge"

**How it works:**
1. Each model independently plays Adversarial Critic (Round 1) using the same paper/argument
2. Cross-review: each model evaluates the others' critiques — identifying which challenges are strongest
3. Chairman synthesis: produces a single report with surviving critiques ranked by cross-model agreement

**Invocation (CLI backend):**
```bash
cd packages/council-cli
uv run python -m council_cli \
    --prompt-file /tmp/devils-advocate-prompt.txt \
    --context-file /tmp/paper-content.txt \
    --output-md /tmp/devils-advocate-council.md \
    --chairman Codex \
    --timeout 180
```

See `skills/shared/council-protocol.md` for the full orchestration protocol.

**Value:** High — the multi-turn debate protocol (Round 1→2→3) becomes genuinely adversarial when different models play different roles. A critique that survives cross-model scrutiny is almost certainly a real weakness.

## Cross-References

| Skill | When to use instead/alongside |
|-------|-------------------------------|
| `/interview-me` | To develop the idea further through structured interview |
| `/multi-perspective` | For multi-perspective analysis with disciplinary diversity |
| `/proofread` | For language/formatting review rather than argument critique |