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 red-teaminggit 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/red-teaming)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/red-teaming"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/red-teaming/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/red-teaming"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/red-teaming.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 | $0.00072 | $0.01313 |
| Opus 5 | $0.00036 | $0.00656 |
| Sonnet 5 | $0.00014 | $0.00263 |
| Haiku 4.5 | $0.00007 | $0.00131 |
Grade A, and why
red-teaming 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red Teaming Campaign
Core question: Can systematic adversarial attacks find fatal flaws in this artifact?
Methodology Sources
- UFMCS Red Team Handbook v9.0 — Military structured analytic techniques
- CIA Structured Analytic Techniques (SAT) — Key Assumptions Check, Devil's Advocacy
- Anthropic Red Teaming (2022) — AI-safety systematic probing methodology
- NIST AI Risk Management Framework — Threat surface enumeration
- Inie et al. (2024) — 12-strategy taxonomy of adversarial attacks
Strategy Routing
| Artifact Type | Primary Strategy | Fallback Strategy |
|---|---|---|
| hypothesis, claim | assumption-challenge | adversarial-persona |
| research-question | alternative-analysis | groupthink-mitigation |
| idea, approach | systematic-probing | assumption-challenge |
| experiment-design | systematic-probing | alternative-analysis |
| gap | adversarial-persona | groupthink-mitigation |
Budget Table
| Parameter | S (Quick) | M (Standard) | L (Deep) |
|---|---|---|---|
| Attack vectors | 5 | 12 | 20 |
| Probing rounds | 3 | 6 | 10 |
| Personas | 2 | 4 | 6 |
| Assumption checks | 5 | 10 | 20 |
Tactics
- structured-attack-campaign — Threat surface enumeration, vector generation, systematic probing, aggregation
- assumption-cascade — Surface assumptions, dependency sort, root attack, cascade trace
- adversarial-roleplay — Construct hostile persona, attack from persona perspective, record paths
Context Management
Each subagent operates in isolated adversarial context. Persona contamination is prevented by spawning separate agents per attack role. Findings are aggregated only after all probing rounds complete. Attack vectors are deduplicated before scoring.
Output
Produces RedTeamReport containing: threat surface map, attack results by vector, assumption cascade analysis, resilience score (0.0-1.0), critical vulnerabilities, and recommended hardening actions.
Available Strategies
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.
- 7d ago First seen · 120 lines · 72 tokens per session scan A 2785115c466c
red-teaming is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 1,313 once invoked, about $0.0004 per session on Opus 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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