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Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.
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High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
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Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
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Use when a carrier needs to build an internal dispatcher training program, onboard new dispatchers, or certify dispatcher competency. Covers job description, required skills, training modules, certification frameworks (CDS, CTP), shadowing, evaluation, and career path.
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Plan and edit a presentation explaining the progression from unstructured Claude Code use to configured engineering workflows. It provides a teaching sequence, proficiency levels, and a TodoApp example connecting project instructions, skills, agents, and automation.
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Search current library documentation and code examples with Context7 CLI, a command-line tool. Find API usage and configuration guidance, select available version-specific documentation, and prioritize examples in the programming language you use.
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Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.
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Investigate why a PyTorch model behaves a certain way by changing its internal values with pyvene. The skill guides experiments that swap or remove activations, train interventions, compare outputs, and save or share intervention configurations.
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Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
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Plan organizational changes such as tool migrations, restructures, and strategy shifts using ADKAR, a five-stage model for adopting change. Structure communication, training, and follow-up while addressing employee concerns, resistance, and fatigue from overlapping changes.
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Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.
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Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.
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Guides experiments that change internal values in PyTorch models to test what causes their behavior. Uses pyvene to swap or remove activations, locate influential components, train interventions, and save experiments for reuse.
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Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
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Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.
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Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.
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Applies the SUCCESs Framework from Made to Stick by Chip Heath and Dan Heath. Use when crafting messages that need to be remembered, writing pitches, creating presentations, designing training materials, telling stories that drive action, or diagnosing why a message isn't landing. The six principles (Simple, Unexpected, Concrete, Credible, Emotional, Stories) diagnose and fix communication failures. Triggers include 'how do I make this memorable', 'my pitch isn't landing', 'people forget what we told them', 'how do I tell a better story', 'our training materials are boring', 'how do I present data compellingly', 'nobody remembers our message', 'how do I get people to care about this', 'my writing is too abstract'. NOT for channel selection (use Traction), not for brand messaging structure (use StoryBrand), not for positioning (use Obviously Awesome).
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Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer — do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.
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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
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Conducts structured post-breach remediation using a lessons learned framework covering root cause remediation, control gap closure, policy updates, training modifications, monitoring enhancements, and regulatory follow-up. Provides a systematic approach to preventing breach recurrence and demonstrating accountability to supervisory authorities. Keywords: post-breach, remediation, lessons learned, root cause, control gap, policy update, training.
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TRIGGER THIS when building DEI strategy, creating diversity goals, designing inclusive programs, building equity analyses, developing training, addressing inclusion issues, or tracking DEI metrics. Develops comprehensive diversity, equity, and inclusion strategies aligned to business goals with measurable outcomes, training programs, metrics frameworks, and accountability systems.
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AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.
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Drafts an Adverse Event Reporting Policy compliant with 21 CFR 312.32 (IND safety reporting), 21 CFR 314.80 (postmarketing), and ICH E2A, with multi-jurisdictional overlays (EMA, PMDA, Health Canada). Covers seriousness/causality frameworks, expedited reporting timelines, roles, documentation standards, training mandates, and QA mechanisms. Use when drafting or updating an adverse event reporting policy, pharmacovigilance policy, AE/SAE reporting SOP, or safety reporting framework for a pharmaceutical company, CRO, biotech, or clinical research institution.
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Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).
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