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Found 18 Skills
Use when testing plans or decisions for blind spots, need adversarial review before launch, validating strategy against worst-case scenarios, building consensus through structured debate, identifying attack vectors or vulnerabilities, user mentions "play devil's advocate", "what could go wrong", "challenge our assumptions", "stress test this", "red team", or when groupthink or confirmation bias may be hiding risks.
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
Find every way users can break your AI before they do. Use when you need to red-team your AI, test for jailbreaks, find prompt injection vulnerabilities, run adversarial testing, do a safety audit before launch, prove your AI is safe for compliance, stress-test guardrails, or verify your AI holds up against adversarial users. Covers automated attack generation, iterative red-teaming with DSPy, and MIPROv2-optimized adversarial testing.
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns. Use when building LLM guardrails, safety checks, or red-team workflows.
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
Real-time monitoring and detection of adversarial attacks and model drift in production
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
Senior Code Architect & Quality Assurance Engineer for 2026. Specialized in context-aware AI code reviews, automated PR auditing, and technical debt mitigation. Expert in neutralizing "AI-Smells," identifying performance bottlenecks, and enforcing architectural integrity through multi-job red-teaming and surgical remediation suggestions.
Red-team a PRD, roadmap, or strategy by attacking its load-bearing assumptions before reality does. Steelmans then attacks each claim, ranks failure modes by impact × likelihood × cheapness-to-test, and returns the cheapest test and kill criteria for each. Use when stress-testing a plan, pressure-testing a strategy, challenging assumptions, or preparing a doc for executive review.
Answers AI agent evaluation methodology questions with practical, opinionated guidance grounded primarily in Microsoft's agent evaluation ecosystem (MS Learn, Eval Scenario Library, Triage & Improvement Playbook, Eval Guidance Kit) supplemented by select industry sources.
Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.
Think and act like an attacker to identify security vulnerabilities, weaknesses, and penetration vectors through adversarial security testing