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/stress-test --skill adversarial-personagit clone --depth 1 https://github.com/yogsoth-ai/stress-testWrote 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/stress-test/adversarial-persona)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/adversarial-persona"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/adversarial-persona/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/stress-test/adversarial-persona"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/adversarial-persona.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 | $0.00037 | $0.00755 |
| Opus 5 | $0.00018 | $0.00378 |
| Sonnet 5 | $0.00007 | $0.00151 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
adversarial-persona 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 9d 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
100% identical to adversarial-persona — 0 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Persona Strategy
Construct and deploy hostile personas that attack from distinct motivational frames. Each persona has unique expertise, biases, and attack patterns.
Method
- persona-construction builds detailed adversary profiles (background, motivation, expertise, blind spots)
- Each persona attacks from their specific frame:
- Hostile Reviewer: methodological rigor, statistical validity, novelty claims
- Competing Lab: priority disputes, alternative approaches, resource efficiency
- Funding Skeptic: impact claims, feasibility, timeline realism
- Domain Outsider: jargon opacity, unstated assumptions, accessibility
- probe-execution executes persona-specific attacks
- Cross-persona findings compared to identify convergent vulnerabilities
- finding-aggregation synthesizes across all persona perspectives
Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Attack vectors | 5 | 12 | 20 |
| Probing rounds | 3 | 6 | 10 |
| Personas | 2 | 4 | 6 |
| Assumption checks | 5 | 10 | 20 |
Orchestration
persona-construction → [build N personas per budget]
→ [for each persona]:
attack-vector-generation (persona-specific vectors)
→ probe-execution (execute persona attacks)
→ finding-aggregation (cross-persona synthesis)
→ attack-resilience-scoring
Subagents
- persona-construction (adversary profile building)
- attack-vector-generation (persona-specific attack design)
- probe-execution (persona attack execution)
- finding-aggregation (cross-persona synthesis)
- attack-resilience-scoring (convergent vulnerability scoring)
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| adversarial-roleplay | Tactic: Construct detailed hostile persona, attack artifact from that persona's perspective, record successful attack paths for aggregation. |
| structured-attack-campaign | Tactic: Full attack lifecycle — threat surface enumeration, attack vector generation, systematic probing, and finding aggregation across all surfaces. |
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.
- 9d ago First seen · 90 lines · 37 tokens per session scan A 93ba21e9a444
adversarial-persona is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 755 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to adversarial-persona, differing in 0 lines, and is treated as a copy.
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