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Found 2,006 Skills
Comprehensive Kubernetes debugging and troubleshooting toolkit. Use this skill when diagnosing Kubernetes cluster issues, debugging failing pods, investigating network connectivity problems, analyzing resource usage, troubleshooting deployments, or performing cluster health checks.
Build and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
Manage Dokploy infrastructure: projects, applications, databases, domains, compose services, deployments, and servers via the Dokploy REST API. Use whenever the user mentions dokploy, deploying apps, managing servers, creating databases, adding domains, docker compose deployments, checking deployment status/logs, or any PaaS infrastructure management. Even if 'dokploy' isn't mentioned explicitly, trigger when the context involves their self-hosted deployment platform.
Scan any codebase for 14 critical safety issues across security vulnerabilities, server stability (500 errors), and payment misconfigurations. Use when auditing code before deployment, reviewing AI-generated code for production readiness, or...
RT-DETR (Real-Time DEtection TRansformer) for 2D object detection. Designed for real-time inference with competitive accuracy and supports distillation and quantization for deployment optimization. Use when training, evaluating, distilling, quantizing, exporting, or running inference for a TAO RT-DETR model. Trigger phrases include "train RT-DETR", "real-time DETR", "low-latency object detection", "RT-DETR distillation / quantization".
Post team updates to Google Chat Spaces via webhook. Deployment notifications, bug fixes, feature announcements, questions. Reads config from .claude/settings.json, includes git context. Use when: "post to team", "notify team", after deployments, completing features, fixing bugs, asking team questions.
TypeScript code quality patterns for writing and reviewing code. Covers type safety, clean code, functional patterns, Zod usage, and error handling. Triggers on: add entity, create service, add repository, create comparator, add formatter, deployment stage, GraphQL query, GraphQL mutation, bootstrap method, diff support, command handler, Zod schema, error class, implement feature, add function, refactor code, clean code, functional patterns, map filter reduce, satisfies operator, type guard, code review, PR review, check implementation, audit code, fix types.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
Specialized utility for advanced manipulation, analysis, and creation of spreadsheet files, including (but not limited to) XLSX, XLSM, CSV formats. Core functionalities include formula deployment, complex formatting (including automatic currency formatting for financial tasks), data visualization, and mandatory post-processing recalculation.
Use when the user needs end-to-end TypeScript development — from database schema through API layer to UI — with tRPC, Prisma, Next.js, authentication, and deployment. Triggers: full-stack feature implementation, database-to-UI pipeline, tRPC router creation, Prisma schema design, auth setup, deployment configuration.
Brev managed GPU instances with Docker support. Use when running TAO training, evaluation, or inference on Brev GPU instances, managing Brev deployments, or dispatching TAO jobs through the Brev CLI. Trigger phrases include "run on Brev", "Brev GPU instance", "submit job to Brev", "Brev CLI deployment".
Elite AI/ML Senior Engineer with 20+ years experience. Transforms Claude into a world-class AI researcher and engineer capable of building production-grade ML systems, LLMs, transformers, and computer vision solutions. Use when: (1) Building ML/DL models from scratch or fine-tuning, (2) Designing neural network architectures, (3) Implementing LLMs, transformers, attention mechanisms, (4) Computer vision tasks (object detection, segmentation, GANs), (5) NLP tasks (NER, sentiment, embeddings), (6) MLOps and production deployment, (7) Data preprocessing and feature engineering, (8) Model optimization and debugging, (9) Clean code review for ML projects, (10) Choosing optimal libraries and frameworks. Triggers: "ML", "AI", "deep learning", "neural network", "transformer", "LLM", "computer vision", "NLP", "TensorFlow", "PyTorch", "sklearn", "train model", "fine-tune", "embedding", "CNN", "RNN", "LSTM", "attention", "GPT", "BERT", "diffusion", "GAN", "object detection", "segmentation".