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Found 1,182 Skills
Convert HuggingFace transformer models to ONNX format for browser inference with Transformers.js and WebGPU. Use when given a HuggingFace model link to convert to ONNX, when setting up optimum-cli for ONNX export, when quantizing models (fp16, q8, q4) for web deployment, when configuring Transformers.js with WebGPU acceleration, or when troubleshooting ONNX conversion errors. Triggers on mentions of ONNX conversion, Transformers.js, WebGPU inference, optimum export, model quantization for browser, or running ML models in the browser.
Implement a feature flag system for gradual rollouts, A/B testing, and kill switches. Use when you need to control feature availability without deployments, test features with specific users, or implement percentage-based rollouts.
Use when the user wants to create, generate, or set up a GitHub Actions workflow. Handles CI/CD pipelines, testing, deployment, linting, security scanning, release automation, Docker builds, scheduled tasks, and any custom workflow for any language or framework.
Comprehensive guide for production-ready Python backend development and software architecture at scale. Use when designing APIs, building backend services, creating microservices, structuring Python projects, implementing database patterns, writing async code, or any Python backend/server-side development task. Covers Clean Architecture, Domain-Driven Design, Event-Driven Architecture, FastAPI/Django patterns, database design, caching strategies, observability, security, testing strategies, and deployment patterns for high-scale production systems.
Kubernetes operations including deployment, management, troubleshooting, kubectl mastery, and cluster stability. Covers K8s workloads, networking, storage, and debugging pods. Use when user mentions Kubernetes, K8s, kubectl, pods, deployments, services, ingress, ConfigMaps, Secrets, or cluster operations.
Java plugin development for Hytale servers. Covers Java 25 setup, Gradle builds, Antigravity/IDE configuration, plugin lifecycle (setup/start/shutdown), command registration, event handling, ECS architecture, and deployment. Use when creating server plugins, extending game functionality, or implementing custom game mechanics.
Provides comprehensive Google Cloud Platform (GCP) guidance including Compute Engine, Cloud Storage, Cloud SQL, BigQuery, GKE (Google Kubernetes Engine), Cloud Functions, Cloud Run, VPC networking, load balancing, IAM, Cloud Build, infrastructure as code (Terraform, Deployment Manager), security configuration, cost optimization, and multi-region deployment. Produces infrastructure code, deployment scripts, configuration guides, and architecture designs. Use when deploying to Google Cloud, designing GCP infrastructure, migrating to GCP, configuring GCE instances, setting up Cloud Storage, managing Cloud SQL databases, working with BigQuery, deploying to GKE, or when users mention "Google Cloud", "GCP", "Compute Engine", "Cloud Storage", "BigQuery", "GKE", "Cloud Run", "Cloud Functions", "VPC", "Cloud SQL", or "Google Cloud Platform".
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to process documentation by running baseline without skill, writing to address failures, iterating to close loopholes
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
Configure Databricks across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Databricks configurations. Trigger with phrases like "databricks environments", "databricks staging", "databricks dev prod", "databricks environment setup", "databricks config by env".
Comprehensive security audit of codebase using multiple security-auditor agents. Use before production deployments or after major features.