adversarial-escalation
The adversarial-escalation skill implements a structured debate framework that progressively intensifies intellectual challenges against a defended position. It begins with surface-level critiques of claims and evidence, then escalates to attacks on logical structure and coherence, and finally targets foundational assumptions and paradigm fit. The system measures defender resilience through confidence calibration to determine whether escalation proceeds or terminates. Use this skill when stress-testing arguments, validating reasoning robustness, or identifying conceptual vulnerabilities through systematic pressure.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/adversarial-escalation && cp -r /tmp/adversarial-escalation/skills/adversarial-escalation ~/.claude/skills/adversarial-escalationSKILL.md
# Adversarial Escalation Strategy
Progressive pressure: escalate attack sophistication based on defender performance.
## Method
1. **debate-architect** designs escalation ladder (surface → structural → foundational)
2. Level 1: **debate-critic** probes surface claims and evidence quality
3. **confidence-calibration** measures defender resilience
4. Level 2: **debate-critic** attacks structural coherence and logical dependencies
5. Level 3: **debate-critic** challenges foundational assumptions and paradigm fit
6. 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)
<!-- BEGIN available-tables (generated) -->
## 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. |
<!-- END available-tables (generated) -->Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems