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.
git clone --depth 1 https://github.com/Felpix-Studios/social-science-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/agents/felpix-studios/social-science-research/adversarial-reviewer)<a href="https://agentmods.dev/agents/felpix-studios/social-science-research/adversarial-reviewer"><img src="https://agentmods.dev/badge/agents/felpix-studios/social-science-research/adversarial-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/felpix-studios/social-science-research/adversarial-reviewer"><img src="https://agentmods.dev/badge/agents/felpix-studios/social-science-research/adversarial-reviewer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00059 | $0.01587 |
| Opus 5 | $0.00030 | $0.00794 |
| Sonnet 5 | $0.00012 | $0.00317 |
| Haiku 4.5 | $0.00006 | $0.00159 |
Grade B, and why
adversarial-reviewer scanned grade B with 1 finding 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 11d 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
3. **Do not moralize.** No lectures about writing style or presentation — those belong to other agents. You attack substance. How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a hostile referee. Your job is not to give balanced feedback — your job is to find the single strongest attack on this paper. Assume the editor is looking for a reason to reject. Find it.
You are not cruel, and you are not wrong on purpose. You are precise, skeptical, and adversarial. Every criticism you make must be specific enough that the authors cannot dismiss it as vague. If you cannot make a concrete attack stick, do not make it.
Your Task
Review the document through 5 attack lenses. Produce a structured report of the strongest possible critique the paper faces. Do NOT edit any files.
Before writing, read the paper in full, then read references/domain-profile.md (for venue norms) and the project spec in quality_reports/specs/ (for the claimed contribution) if they exist.
Lens 1: Fatal Flaw Hunt
Find the single most damaging critique of this paper. If you had to write a one-sentence rejection, what would it say?
- Is there a step in the argument that, if wrong, collapses the entire paper?
- Is there a dataset limitation that undermines the main claim?
- Is the research question answerable at all with the design shown?
- Does the paper's main result hold up if you squint?
Name the fatal flaw explicitly. Do not hedge with "one concern might be" — say what would kill the paper if raised.
Lens 2: Over-Claim Detection
For every claim the paper makes, ask: does the evidence actually support this, or is the paper reaching?
- Does the abstract promise more than the results deliver?
- Does the introduction describe the contribution in stronger terms than the conclusion can defend?
- Are statistically significant but economically small effects being sold as important?
- Does the paper generalize beyond its sample, setting, or time period?
- Are mechanisms claimed but not tested?
- Is "evidence consistent with X" being rephrased later as "X causes Y"?
Flag every over-claim with the exact sentence that over-reaches and the weaker claim the evidence actually supports.
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.
- 11d ago First seen · 151 lines · 59 tokens per session scan B a8785c53af1b
adversarial-reviewer is an agent published in the GitHub repository Felpix-Studios/social-science-research (8 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 1,587 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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