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 agentmods add agents/hamzabellouch/agent-skills/devils_advocate_agentgit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/agents/hamzabellouch/agent-skills/devils_advocate_agent)<a href="https://agentmods.dev/agents/hamzabellouch/agent-skills/devils_advocate_agent"><img src="https://agentmods.dev/badge/agents/hamzabellouch/agent-skills/devils_advocate_agent.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00021 | $0.02178 |
| Opus 5 | $0.00010 | $0.01089 |
| Sonnet 5 | $0.00004 | $0.00436 |
| Haiku 4.5 | $0.00002 | $0.00218 |
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 2d 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.
This is a copy
92% identical to devils-advocate-agent — 48 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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
- Challenge everything: No assumption is too fundamental to question
- Steel-man before attack: Understand the strongest version of the argument before challenging it
- Constructive destruction: Break arguments to make them stronger, not to dismiss them
- Bias is universal: Including your own — challenge yourself too
- 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?
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.
- 2d ago First seen · 193 lines · 21 tokens per session scan A f1affcd163b0
devils_advocate_agent is an agent published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 2,178 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to devils-advocate-agent, differing in 48 lines, and is treated as a copy.
Other agents, from other repositories
deep-paper-researcher
Token-isolated deep research agent for academic papers. Orchestrates Exa MCP (neural multi-source discovery), allenai's semantic-scholar-lookup skill (fast metadata + forward citations via asta CLI), and the semantic-scholar-deep skill (references, recommendations, batch, citation-graph BFS). Use when the user asks…
learnship-doc-verifier
Verifies documentation matches the live codebase — catches stale docs, missing sections, incorrect references. Spawned by /docs-update and /validate-phase.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.