/adversarial-roleplay
Tactic: Construct detailed hostile persona, attack artifact from that
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill adversarial-roleplay --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-roleplay
Context preview
The summary Claude sees to decide when to auto-load this skill.
Tactic: Construct detailed hostile persona, attack artifact from that
SKILL.md
adversarial-roleplay.SKILL.mdname: adversarial-roleplay
description: 'Tactic: Construct detailed hostile persona, attack artifact from that
persona''s perspective, record successful attack paths for aggregation.'
type: tactic
strategies:
- adversarial-persona
- groupthink-mitigation
- alternative-analysis
dependencies:
sops:
- attack-vector-generation
- finding-aggregation
- persona-construction
- probe-execution
Adversarial Roleplay Tactic
Deploy constructed hostile personas to attack the artifact from distinct motivational frames.
Orchestration
1. **persona-construction** builds detailed adversary profile:
- Background and expertise domain
- Motivation for attacking (career incentive, resource competition, ideological)
- Known blind spots and biases of this persona type
- Preferred attack patterns
2. **attack-vector-generation** generates vectors specific to persona's expertise and motivation 3. **probe-execution** executes attacks while maintaining persona consistency 4. Successful attack paths recorded with persona attribution 5. Process repeats for each persona (budget-limited) 6. **finding-aggregation** cross-references findings across personas for convergent vulnerabilities
Subagents Dispatched
- persona-construction (1 call per persona)
- attack-vector-generation (1 call per persona)
- probe-execution (N calls per persona, budget-limited)
- finding-aggregation (1 call at end, cross-persona)
Termination Conditions
- All budgeted personas deployed and exhausted
- Convergent vulnerability found by 2+ personas (high-confidence finding)
- Single persona finds critical vulnerability (early report)
- Budget exhausted (report per-persona findings separately)
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | 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-roleplay description: 'Tactic: Construct detailed hostile persona, attack artifact from that persona''s perspective, record successful attack paths for aggregation.' type: tactic strategies: - adversarial-persona - groupthink-mitigation - alternative-analysis dependencies: sops: - attack-vector-generation - finding-aggregation - persona-construction - probe-execution
Adversarial Roleplay Tactic
Deploy constructed hostile personas to attack the artifact from distinct motivational frames.
Orchestration
1. **persona-construction** builds detailed adversary profile:
- Background and expertise domain
- Motivation for attacking (career incentive, resource competition, ideological)
- Known blind spots and biases of this persona type
- Preferred attack patterns
2. **attack-vector-generation** generates vectors specific to persona's expertise and motivation 3. **probe-execution** executes attacks while maintaining persona consistency 4. Successful attack paths recorded with persona attribution 5. Process repeats for each persona (budget-limited) 6. **finding-aggregation** cross-references findings across personas for convergent vulnerabilities
Subagents Dispatched
- persona-construction (1 call per persona)
- attack-vector-generation (1 call per persona)
- probe-execution (N calls per persona, budget-limited)
- finding-aggregation (1 call at end, cross-persona)
Termination Conditions
- All budgeted personas deployed and exhausted
- Convergent vulnerability found by 2+ personas (high-confidence finding)
- Single persona finds critical vulnerability (early report)
- Budget exhausted (report per-persona findings separately)
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | 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

