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Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.
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Analyze compliance operations for regulatory change tracking, control mapping, policy management, audit readiness, and training compliance. Triggers: 'audit compliance program', 'assess GRC platform', 'review regulatory change management', 'check control framework'.
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Drafts a standalone API Acceptable Use Policy (AUP) for incorporation by reference into a master API license or terms-of-service agreement. Produces a publication-ready template with prohibited-use matrix, developer security checklist, graduated enforcement framework, AI/ML training restrictions, and versioning playbook. Trigger when a user needs to draft or update an API AUP, separate behavioral rules from core API terms, define prohibited API uses, add enforcement or change-management mechanics, or mentions acceptable use policy, developer security requirements, or API enforcement.
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Retrieve current library, framework, and API docs via Context7, Context Hub, and web fallback. Use whenever docs are needed for a library, SDK, or internal spec instead of stale training data.
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Retrieve current documentation, API references, and code examples for any library, framework, SDK, CLI tool, or cloud service — including well-known ones like React, Next.js, Prisma, Django, or Spring Boot. Use for API syntax, configuration options, version migrations, setup instructions, and library-specific debugging, even when you already think you know the answer: training data on API details and signatures is frequently outdated. Prefer this over web search for library documentation.
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Knowledge base from the FAA System Safety Handbook (Dec 30, 2000). Use for system safety as a specialty within systems engineering: the five-step safety risk management process (mandated by FAA Order 8040.4 and the AMS), the severity-by-likelihood risk matrix and acceptance bands, the Safety Order of Precedence, system-description models (5M, SHEL(L)), the hazard-analysis activities (PHL/PHA/RHA/SSHA/SHA/O&SHA/HHA), integrated system hazard analysis, the analysis techniques (FTA, FMEA/FMECA, Fault Hazard, Common Cause, Sneak Circuit, Energy Trace), closed-loop hazard tracking (Safety Action Record), and the specialty domains the handbook covers — acquisition-lifecycle safety and contracting (OSA/CSA/SSPP/ISSPP), software safety (control-capability ranking, SHCM, DO-178B), test & evaluation safety, facilities safety, commercial launch safety (AST licensing, expected casualty, MPL/DAMP), system safety training, and Operational Risk Management (ORM) with human/organizational factors. Scope limits: this is a year-2000 FAA how-to; it predates the modern FAA/ICAO Safety Management System (SMS) and Just Culture regulatory frameworks, and many cited orders, advisory circulars, and launch-licensing rules have since been restructured. It is FAA/NAS-centric, leans on MIL-STD-882C, and does not define the SE life-cycle processes themselves (use nasa-npr-7123 / dau-se-guidebook) or reproduce third-party standard text (MIL-STD-882, DO-178B, ISO/IEC/IEEE).
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Fetch up-to-date library, framework, and SDK documentation via Context7's search and docs endpoints. Use when the user asks about a library/API (React, Next.js, Prisma, Supabase, Tailwind, Django, Vue, etc.) and you need current docs, not Claude's training-time knowledge. Skip this skill for generic programming questions that don't require library-specific docs.
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PVDetector, a training-free framework that detects PI attacks during LLM inference by measuring hidd GAP: no mappable capability in latest signal (our
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Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
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High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
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Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
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