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Found 1,619 Skills
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
SvelteKit - Full-stack Svelte framework with file-based routing, SSR/SSG, form actions, and adapters for deployment
Elite Talos Linux expert specializing in immutable Kubernetes OS, secure cluster deployment, machine configurations, talosctl CLI operations, upgrades, and production-grade security hardening. Expert in Talos 1.6+, secure boot, disk encryption, and zero-trust infrastructure. Use when deploying Talos clusters, configuring machine configs, troubleshooting node issues, or implementing security best practices.
Production-grade backend service development across Node.js (Express/Fastify/NestJS/Hono), Bun, Python (FastAPI), Go, and Rust (Axum), with PostgreSQL and common ORMs (Prisma/Drizzle/SQLAlchemy/GORM/SeaORM). Use for REST/GraphQL/tRPC APIs, auth (OIDC/OAuth), caching, background jobs, observability (OpenTelemetry), testing, deployment readiness, and zero-trust defaults.
Build integrations with Rocket.net's WordPress hosting API. Manage sites, domains, backups, plugins, themes, CDN cache, FTP accounts, and more programmatically. Use when: building WordPress hosting management tools, automating site deployment, creating reseller portals, managing multiple WordPress sites, integrating with Rocket.net hosting services, automating backup workflows, or building custom control panels.
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".
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.
Provides comprehensive guidance for Jenkins CI/CD including pipeline creation, job configuration, plugins, and automation. Use when the user asks about Jenkins, needs to set up CI/CD pipelines, configure Jenkins jobs, or automate build and deployment processes.
Expert blueprint for Battle Royale games including shrinking zone/storm mechanics (phase-based, damage scaling), large-scale networking (relevancy, tick rate optimization), deployment systems (plane, freefall, parachute), loot spawning (weighted tables, rarity), and performance optimization (LOD, occlusion culling, object pooling). Use for multiplayer survival games or last-one-standing formats. Trigger keywords: battle_royale, zone_shrink, storm_damage, deployment_system, loot_spawn, networking_optimization, relevancy_system, snapshot_interpolation.
MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold).
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.
Guides development of Fusion portal shells — scaffolding, module configuration, app loading, routing, header/context integration, analytics, and deployment using the Fusion Framework CLI portal commands. USE FOR: create portal, scaffold portal, configure portal modules, portal app loading, portal routing, portal header, context selector, portal analytics, portal telemetry, portal manifest, ffc portal dev, portal deployment, embed apps in portal. DO NOT USE FOR: app-level feature development (use fusion-app-react-dev), backend service changes, Fusion Help Center integration, skill authoring.