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.
npx skills add flonat/flonat-research --skill devils-advocategit clone --depth 1 https://github.com/flonat/flonat-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flonat/flonat-research/devils-advocate)<a href="https://agentmods.dev/skills/flonat/flonat-research/devils-advocate"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/devils-advocate.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01579 |
| Opus 5 | $0.00028 | $0.00790 |
| Sonnet 5 | $0.00011 | $0.00316 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
devils-advocate 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Understand the claim — Read the paper/argument being evaluated
- Generate competing hypotheses — If evaluating a research question or design, load
references/competing-hypotheses.mdand generate 3-5 rival explanations before critiquing - Run the debate — Use the multi-turn debate protocol below (default) or single-shot mode for quick checks
- 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:
- Theoretical foundations — Are the assumptions justified?
- Methodology — Limitations? Alternative approaches?
- Data — Selection bias? Measurement issues? External validity?
- Causal claims — Alternative explanations? Confounders?
- Contribution — Novel enough? Does it matter?
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 133 lines · 56 tokens per session scan A d2a182609200
devils-advocate is a skill published in the GitHub repository flonat/flonat-research (132 stars, last pushed 13d ago), licensed MIT. It adds 56 tokens to every session and 1,579 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
fin-paper-convert
Compile LaTeX to PDF and convert to target journal format.
fin-paper-plan
Generate structured paper outline adapted to target journal.
doc-audit
Evaluate one project document in detail — a workbook section, big picture, strategy map, handoff message, paper, brief, or primer — without editing it. Runs the mechanical lint, builds a claim ledger, types every headline against the eight claim statuses in CLAUDE.md, traces cited evidence to the scripts and logs that…
latex-compile
Compile a LaTeX document and fix every error plus aesthetic issue (overfull/underfull boxes, widows, alignment, fonts) for a clean PDF and log. Use this instead of running pdflatex/latexmk manually — it avoids the latexmk stale-log trap and silent grep failures on binary log output, and it reformats rather than…
nb-to-wolfbook
Convert Mathematica .nb or .m files to Wolfbook .wb format so they open and run in VS Code. Use when bringing existing .nb/.m files into Wolfbook, or to make an existing .wb bridge-safe.
claim-audit
Audit what a passing script actually established, before writing any prose about it — build the computed-object ledger, rewrite every check's label as the weakest statement that makes its body pass, and separate the verdict on someone else's work from your own new claim. Run after the script passes and BEFORE the…