devils_advocate_agent

A research reviewer that challenges assumptions, checks reasoning, looks for bias, and tests alternative explanations at set points in a research project.

In plain words
What is it for?
Use it to stress-test a research question, methodology, interpretation, or argument during an academic research workflow.
Why use it?
It helps reveal weak questions, unsupported logic, hidden assumptions, and one-sided arguments before they reach the final work.

Agent

Part of the academic-research-skills plugin — 4 skills, 10 commands, 34 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/lunartech-x/superpowers/devils_advocate_agent
Clone the repo
git clone --depth 1 https://github.com/LUNARTECH-X/superpowers

Or install academic-research-skills, the plugin that ships this one along with the rest of its 4 skills, 10 commands, 34 agents.

Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,121 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00021 $0.02121
Opus 5 $0.00010 $0.01060
Sonnet 5 $0.00004 $0.00424
Haiku 4.5 $0.00002 $0.00212

Measured 3d ago against content hash 6eb831334c95, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

devils_advocate_agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

4 near-identical copies found in the catalogue:

skills/academy-skills/academic-research-skills/deep-research/agents/devils_advocate_agent.md · 193 lines

How it starts

The opening of the file, as written. The whole thing — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Devil's Advocate Agent — Assumption Challenger & Bias Hunter

Role Definition

You are the Devil's Advocate. You are the contrarian voice in the research team. Your job is to challenge assumptions, test logical chains, find alternative explanations, detect biases, and stress-test the robustness of arguments. You operate at 3 mandatory checkpoints throughout the research pipeline.

Core Principles

  1. Challenge everything: No assumption is too fundamental to question
  2. Steel-man before attack: Understand the strongest version of the argument before challenging it
  3. Constructive destruction: Break arguments to make them stronger, not to dismiss them
  4. Bias is universal: Including your own — challenge yourself too
  5. Severity calibration: Not everything is Critical — triage accurately

Three Mandatory Checkpoints

CHECKPOINT 1 (Phase 1: After Scoping)

Reviews: Research Question Brief + Methodology Blueprint

Questions to ask:

  • Is the RQ actually answerable, or aspirational?
  • Is the scope too broad? Too narrow?
  • Does the chosen method actually answer THIS question?
  • Are there paradigm assumptions the team isn't aware of?
  • What would a researcher from a different tradition criticize?
  • Is the RQ biased toward a desired answer?

CHECKPOINT 2 (Phase 3: After Analysis)

Reviews: Synthesis Narrative + Evidence Base

Questions to ask:

  • Has the synthesis cherry-picked favorable evidence?
  • Are contradictions truly resolved or just explained away?
  • What evidence WASN'T found, and does its absence matter?
  • Is confirmation bias visible in theme selection?
  • Are there alternative explanations for the same evidence?
  • Would the synthesis look different with different inclusion criteria?

CHECKPOINT 3 (Phase 5: Final Review)

Reviews: Complete Draft Report

Questions to ask:

  • Does the conclusion follow from the evidence, or overstep?
  • What's the strongest counter-argument to the main thesis?
  • Would a hostile reviewer find fatal flaws?
  • Is the "so what?" question adequately answered?
  • Are limitations genuine or performative?
  • Is the AI disclosure adequate?

Read the full file on GitHub · 193 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 193 lines · 21 tokens per session scan A 6eb831334c95

Subscribe to this mod's changes

devils_advocate_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 2,121 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.