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Found 171 Skills
Conduct stakeholder analysis using identification, Power-Interest matrix classification, and influence strategy development. Use this skill when the user needs to map stakeholders for a project, manage conflicting interests, prioritize communication, or build a stakeholder engagement plan — even if they say 'who needs to approve this', 'how do I get buy-in', or 'who might block this project'.
Use when a security incident has been detected or declared and needs classification, triage, escalation path determination, and forensic evidence collection. Covers SEV1-SEV4 classification, false positive filtering, incident taxonomy, and NIST SP 800-61 lifecycle.
Guides secure software delivery and DevSecOps for cleared/classified or high-side programs—disconnected or air-gapped CI/CD, artifact promotion across classification boundaries (conceptual), SBOM/signing/ provenance, SAST/DAST/secrets/IaC/container gates, supply-chain controls, STIG/CIS deploy baselines, IaC for classified landing zones, cleared developer workstations, build/deploy audit logging, and ATO/RMF pipeline evidence (not SSP ownership). Use for classified DevSecOps, cleared pipeline, high-side CI/CD, air-gapped build, cross-domain release, classified software delivery, STIG pipeline, ATO evidence CI, SBOM classified, secure software factory—not portfolio cyber governance (classified-cyber-security-senior-manager), ISSO/SSP (information-systems-security-officer-classified-specialist), commercial-only DevSecOps (devsecops), general DevOps (devops), build-only validation (build-validator), pentest (penetration-tester), or enterprise GRC-only (compliance-specialist).
Optimize existing Triton kernels for NVIDIA TileIR backend on Blackwell GPUs (sm_100+). Adds TileIR-specific autotune configs: occupancy, num_ctas, TMA descriptors. Covers kernel classification (dot-related, norm-like, elementwise, reduction), type-specific transformations, and PTX-vs-TileIR benchmarking. Triggered by: "optimize for TileIR", "add TileIR configs", "Blackwell optimization", "TMA descriptors", "2CTA mode", "occupancy tuning". Kernels use standard `import triton`; TileIR activates via ENABLE_TILE=1 when nvtriton is installed.
Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose-keypoint data. Use when training, evaluating, exporting, or running inference for a TAO pose-classification model. Trigger phrases include "train pose classification", "skeleton action recognition", "ST-GCN", "keypoint sequence classifier".
PyTorch-based TAO image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.) with distillation and quantization for deployment. Use when training, evaluating, distilling, quantizing, exporting, or running inference for a TAO image-classification (PyT) model. Trigger phrases include "train image classifier", "TAO classification", "ResNet/EfficientNet/FAN backbone classifier", "classification-pyt".
This skill should be used when establishing comprehensive QA testing processes for any software project. Use when creating test strategies, writing test cases following Google Testing Standards, executing test plans, tracking bugs with P0-P4 classification, calculating quality metrics, or generating progress reports. Includes autonomous execution capability via master prompts and complete documentation templates for third-party QA team handoffs. Implements OWASP security testing and achieves 90% coverage targets.
AI governance and compliance guidance covering EU AI Act risk classification, NIST AI RMF, responsible AI principles, AI ethics review, and regulatory compliance for AI systems.
Write Domain-Driven Design architecture models using DomainLang (.dlang files). Covers domains, bounded contexts, context maps, teams, classifications, terminology, relationships, namespaces, and imports. Use when creating DDD models, mapping bounded context relationships, documenting ubiquitous language, or generating .dlang files for strategic design.
Comprehensive drug-drug interaction (DDI) prediction and risk assessment. Analyzes interaction mechanisms (CYP450, transporters, pharmacodynamic), severity classification, clinical evidence grading, and provides management strategies. Supports single drug pairs, polypharmacy analysis (3+ drugs), and alternative drug recommendations. Use when users ask about drug interactions, medication safety, polypharmacy risks, or need DDI assessment for clinical decision support.
Design taxonomy structure for categories, tags, or hierarchical classification. Supports flat, hierarchical, and faceted patterns.
Deploy the Cortex CLASSIFY_TEXT tutorial notebook to the user's Snowflake account and provide a link to open it in Snowsight. Use when user wants to learn text classification through a Jupyter notebook experience.