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 wanshuiyin/Anti-Autoresearch --skill adversarial-case-buildergit clone --depth 1 https://github.com/wanshuiyin/Anti-AutoresearchWrote 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/wanshuiyin/anti-autoresearch/adversarial-case-builder)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/adversarial-case-builder"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/adversarial-case-builder/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/skills/wanshuiyin/anti-autoresearch/adversarial-case-builder"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/adversarial-case-builder.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00194 | $0.11691 |
| Opus 5.5 | $0.00078 | $0.04676 |
| Sonnet 5.5 | $0.00039 | $0.02338 |
| Haiku 4.5 | $0.00019 | $0.01169 |
Grade A, and why
adversarial-case-builder 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- adversarial-case-builder — 92% identical, 307 lines differ
How it starts
The opening of the file, as written. The whole thing — 746 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Case Builder — the strongest evidence-bound objection
Build the single strongest evidence-bound case to reject $ARGUMENTS, then defend
it point-by-point. Emit adversarial-case-builder.memo.md. Run LAST, AFTER
/evidence-ledger (so claims.json exists) and AFTER the auditor skills (so the
merged *.findings.json exist).
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It runs LAST and synthesizes the ledger + the other auditors' findings into one memo. Even though it is memo-only (the adjudicator caps it atinfo, so it adds no verdict weight), the no-new-signal cadence rule still applies: its output changes only when the ledger / the findings / the paper change, never with the wall clock. Schedule the work that precedes it — ledger + auditors done → run this once. (Mirrors ARIS's external-cadence doctrine.)
Adapted from ARIS
kill-argument, with one deliberate downgrade: memo-only. In a forensics pipeline the headline-attack is most useful as a synthesis of already-anchored evidence, not a free-floating LLM critique — that free-floating mode is exactly the "LLM slop grading LLM slop" failure this repo exists to refuse. So here every attack point must cite an existing ledgerclaim_idorfinding_id, and the skill never emits verdict-bearing findings:tools/adjudicate_findings.pylistsadversarial-case-builderinZERO_WEIGHT_SKILLSand caps anything from it atinfo. The deterministic adjudicator owns the verdict; this skill owns the memo.
Why this exists
The standard auditors (consistency-audit, citation-forensics, …) fan out and
each flags discrepancies in its own dimension. They produce a balanced list —
each discrepancy at its own severity, none committing to "this is the one that sinks
the paper." That misses a specific failure mode: the single most damaging
paragraph a senior area chair would write in a rejection. A balanced reviewer lists
"scope-overclaim" as one major among several and never commits; an adversarial
reviewer must commit — their whole job is to convince the AC to reject in ~200
words.
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.
- 1mo ago First seen · 746 lines · 194 tokens per session scan A d47af199de44
adversarial-case-builder is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 11d ago), licensed MIT. It adds 194 tokens to every session and 11,691 once invoked, about $0.0008 per session on Opus 5.5. 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.
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