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-escalationgit 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-escalation)<a href="https://agentmods.dev/skills/yogsoth-ai/stress-test/adversarial-escalation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/adversarial-escalation/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-escalation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/stress-test/adversarial-escalation.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 | $0.00031 | $0.00698 |
| Opus 5 | $0.00015 | $0.00349 |
| Sonnet 5 | $0.00006 | $0.00140 |
| Haiku 4.5 | $0.00003 | $0.00070 |
Grade A, and why
adversarial-escalation 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 8d 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-escalation — 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Escalation Strategy
Progressive pressure: escalate attack sophistication based on defender performance.
Method
- debate-architect designs escalation ladder (surface → structural → foundational)
- Level 1: debate-critic probes surface claims and evidence quality
- confidence-calibration measures defender resilience
- Level 2: debate-critic attacks structural coherence and logical dependencies
- Level 3: debate-critic challenges foundational assumptions and paradigm fit
- Each level only reached if defender survives previous level
Budget Table
| Parameter | S | M | L |
|---|---|---|---|
| Debate rounds | 4 | 8 | 12 |
| Participating agents | 3 | 5 | 8 |
| Coverage dimensions | 3 | 5 | 7 |
| External evidence searches | 2 | 5 | 10 |
Orchestration
debate-architect → [design escalation ladder]
→ [for each level]:
debate-critic (level-appropriate attack)
→ debate-defender → debate-judge
→ confidence-calibration
→ (escalate if survived, terminate if collapsed)
→ debate-transcript-analysis → verdict-synthesis
Subagents
- debate-architect (escalation design)
- debate-critic (multi-level attacks)
- debate-defender (responses)
- debate-judge (level adjudication)
- confidence-calibration (escalation trigger)
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| stress-test-dialectical-escalation | Tactic: Progressive debate escalation based on confidence thresholds. Each round increases attack sophistication until defender collapses or proves resilient. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| confidence-calibration | Calibrates confidence scores based on debate progression. Determines whether to escalate, continue, or terminate based on cumulative evidence. |
| debate-architect | Designs debate structure based on artifact type — selects attack vectors, assigns perspectives, determines escalation ladder, and configures round parameters. |
| debate-critic | Generates structured criticism from attack stance using Toulmin model. Produces claims, grounds, warrants, and rebuttals targeting artifact weaknesses. |
| debate-defender | Responds to attacks with counter-evidence and counter-arguments. Defends artifact using evidence, clarification, and rebuttal while acknowledging valid criticisms. |
| debate-judge | Evaluates debate exchanges, adjudicates argument quality, and produces round verdicts with confidence scores and reasoning. |
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.
- 8d ago First seen · 85 lines · 31 tokens per session scan A ec886668ae1f
adversarial-escalation is a skill published in the GitHub repository yogsoth-ai/stress-test (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 698 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-escalation, differing in 0 lines, and is treated as a copy.
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