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 yogsoth-ai/de-anthropocentric-research-engine --skill adversarial-roleplaygit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/adversarial-roleplay)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/adversarial-roleplay"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/adversarial-roleplay/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/yogsoth-ai/de-anthropocentric-research-engine/adversarial-roleplay"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/adversarial-roleplay.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 | $0.00029 | $0.00494 |
| Opus 5.5 | $0.00012 | $0.00198 |
| Sonnet 5.5 | $0.00006 | $0.00099 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
adversarial-roleplay 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-roleplay — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Roleplay Tactic
Deploy constructed hostile personas to attack the artifact from distinct motivational frames.
Orchestration
- persona-construction builds detailed adversary profile:
- Background and expertise domain
- Motivation for attacking (career incentive, resource competition, ideological)
- Known blind spots and biases of this persona type
- Preferred attack patterns
- attack-vector-generation generates vectors specific to persona's expertise and motivation
- probe-execution executes attacks while maintaining persona consistency
- Successful attack paths recorded with persona attribution
- Process repeats for each persona (budget-limited)
- finding-aggregation cross-references findings across personas for convergent vulnerabilities
Subagents Dispatched
- persona-construction (1 call per persona)
- attack-vector-generation (1 call per persona)
- probe-execution (N calls per persona, budget-limited)
- finding-aggregation (1 call at end, cross-persona)
Termination Conditions
- All budgeted personas deployed and exhausted
- Convergent vulnerability found by 2+ personas (high-confidence finding)
- Single persona finds critical vulnerability (early report)
- Budget exhausted (report per-persona findings separately)
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| attack-vector-generation | Generate specific attack strategies for a given threat surface, producing concrete probes that can be executed. |
| finding-aggregation | Aggregate, deduplicate, and classify findings from multiple probes into a coherent vulnerability report. |
| persona-construction | Build a detailed adversarial persona with background, motivation, expertise, blind spots, and preferred attack patterns. |
| probe-execution | Execute a single attack probe against an artifact, record the result with evidence and severity classification. |
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 · 63 lines · 29 tokens per session scan A 8476dc8ea602
adversarial-roleplay is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 5d ago), licensed Apache-2.0. It adds 29 tokens to every session and 494 once invoked, about $0.0001 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 2026-09-03.
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