/adversarial-persona
Strategy: Role-play attacks from hostile personas — competing lab researcher,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill adversarial-persona --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/adversarial-persona
Context preview
The summary Claude sees to decide when to auto-load this skill.
Strategy: Role-play attacks from hostile personas — competing lab researcher,
SKILL.md
adversarial-persona.SKILL.mdname: adversarial-persona
description: 'Strategy: Role-play attacks from hostile personas — competing lab researcher,
hostile reviewer, funding skeptic, domain outsider — each with distinct attack motivations
and blind spots.'
type: strategy
tactics:
- adversarial-roleplay
- structured-attack-campaign
dependencies:
tactics:
- adversarial-roleplay
- structured-attack-campaign
sops:
- attack-resilience-scoring
- attack-vector-generation
- finding-aggregation
- persona-construction
- probe-execution
Adversarial Persona Strategy
Construct and deploy hostile personas that attack from distinct motivational frames. Each persona has unique expertise, biases, and attack patterns.
Method
1. **persona-construction** builds detailed adversary profiles (background, motivation, expertise, blind spots) 2. 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
3. **probe-execution** executes persona-specific attacks 4. Cross-persona findings compared to identify convergent vulnerabilities 5. **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-scoringSubagents
- 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)
<!-- BEGIN available-tables (generated) -->
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. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | attack-resilience-scoring | Compute overall resilience score (0.0-1.0) based on attack results, coverage, and vulnerability severity distribution. | | 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. |
<!-- END available-tables (generated) -->
Read more
name: adversarial-persona description: 'Strategy: Role-play attacks from hostile personas — competing lab researcher, hostile reviewer, funding skeptic, domain outsider — each with distinct attack motivations and blind spots.' type: strategy tactics: - adversarial-roleplay - structured-attack-campaign dependencies: tactics: - adversarial-roleplay - structured-attack-campaign sops: - attack-resilience-scoring - attack-vector-generation - finding-aggregation - persona-construction - probe-execution
Adversarial Persona Strategy
Construct and deploy hostile personas that attack from distinct motivational frames. Each persona has unique expertise, biases, and attack patterns.
Method
1. **persona-construction** builds detailed adversary profiles (background, motivation, expertise, blind spots) 2. 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
3. **probe-execution** executes persona-specific attacks 4. Cross-persona findings compared to identify convergent vulnerabilities 5. **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-scoringSubagents
- 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)
<!-- BEGIN available-tables (generated) -->
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. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | attack-resilience-scoring | Compute overall resilience score (0.0-1.0) based on attack results, coverage, and vulnerability severity distribution. | | 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. |
<!-- END available-tables (generated) -->
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

