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Found 171 Skills
Alibaba Cloud APIG Migration Skill. Migrate Kubernetes nginx Ingress resources to Alibaba Cloud API Gateway (APIG, ingressClass: apig). Users provide Ingress YAML (paste, file, or directory) — no cluster access required for analysis. Covers annotation compatibility classification, Higress native mapping, built-in plugin selection, custom WasmPlugin development, migrated Ingress YAML generation, and migration report with deployment guide. Triggers: "nginx ingress migration", "APIG compatibility", "gateway migration", "ingress-nginx to APIG", "nginx迁移", "网关迁移", "Ingress兼容性分析", "APIG迁移", "迁移评估", "annotation兼容性", "WasmPlugin开发".
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'.
Behavioral classification, performance analysis, and trading style detection for Solana wallets
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.
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.
Multi-source AI news aggregation and digest generation with deduplication, classification, and source tracing. Supports 20+ sources, 5 theme categories, multi-language output (ZH/EN/JA), and image export.
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.
Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.
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.
Design taxonomy structure for categories, tags, or hierarchical classification. Supports flat, hierarchical, and faceted patterns.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.