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Found 1,750 Skills
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Format and validate code in various languages. Python, JavaScript, JSON, YAML, Markdown, and more. Uses standard formatters when available.
.NET and ASP.NET Core security patterns. Covers Identity, authentication, dependency auditing, secure coding practices, and OWASP for .NET ecosystem. USE WHEN: user works with "C#", ".NET", "ASP.NET Core", "Entity Framework", asks about ".NET vulnerabilities", "NuGet security", ".NET authentication", "Blazor security" DO NOT USE FOR: general OWASP concepts - use `owasp` or `owasp-top-10` instead, Java/Python security - use language-specific skills
OpenTelemetry with Grafana stack. Covers OTel SDK instrumentation for Go/Java/Python/Node.js/.NET, OTLP protocol and endpoint configuration, sending telemetry to Grafana Cloud via OTLP endpoint, Grafana Alloy as OTel collector, sampling strategies, Kubernetes OTel Operator, and migration from other observability tools. Use when instrumenting apps with OTel, configuring OTLP endpoints, setting up collectors, or migrating to OpenTelemetry.
Use when the user needs self-hosted or local Chroma for semantic search, including `ChromaClient`, `HttpClient`, or Python `EphemeralClient`, local persistence, Docker or `chroma run`, or OSS Chroma without Chroma Cloud features.
Grafana Pyroscope continuous profiling platform. Covers instrumentation of Go/Java/Python/Ruby/Node.js/ .NET/Rust apps via SDKs or eBPF (Alloy), flame graph analysis, ProfileQL queries, server configuration and architecture, Grafana Cloud Profiles integration, and trace-profile linking (Span Profiles). Use when working with profiling data, instrumenting apps for Pyroscope, analyzing performance profiles, or deploying Pyroscope server.
CuTe Python DSL API reference and implementation patterns for NVIDIA GPU kernel programming. Provides execution model, core API table, key constraints, common patterns, and documentation index. Use when: (1) writing or modifying CuTe DSL kernel code, (2) looking up CuTe DSL API syntax, (3) implementing attention/GEMM/MLA patterns in CuTe DSL, (4) understanding CuTe DSL execution model and compilation pipeline, (5) checking what CuTe DSL can and cannot do.
DocuSeal development reference. Embed signing forms and template builder into web and mobile apps (JS/React/Vue/Angular, WebView, JWT, CSS theming). REST API with all endpoints, request/response schemas, code examples (cURL, CLI, Node.js, TypeScript, Python, Ruby, PHP, Go, C#, Java), and webhooks. Use when the user wants to integrate DocuSeal document signing or template management into their application.
This skill guides development of full-stack features on EdgeOne Pages — Edge Functions, Cloud Functions (Node.js / Go / Python runtimes), Middleware, KV Storage, and local dev workflows. It should be used when the user wants to create APIs, serverless functions, middleware, WebSocket endpoints, or full-stack features specifically on EdgeOne Pages — e.g. "create an API", "add a serverless function", "write middleware", "build a full-stack app", "add WebSocket support", "set up edge functions", "use KV storage", "create a Go API", "build a Python backend", "use Flask/FastAPI/Gin on EdgeOne Pages". Do NOT trigger for framework-native features (Next.js API routes, Next.js middleware, Nuxt server routes) or generic Express/Koa development outside an EdgeOne Pages project. Do NOT trigger for deployment — use edgeone-pages-deploy instead. Do NOT trigger for other platforms (Cloudflare Workers, Vercel Functions, AWS Lambda).
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Guidelines for identifying and resolving missing Google Cloud authentication and Application Default Credentials (ADC). Use this skill if `gcloud`, `bq`, `dataform`, or Python libraries return authentication errors.
A repository of BigQuery-specific logic, knowledge, and specialized standards. Use this skill whenever you are doing anything with BigQuery, including: 1. BigQuery query optimization 2. BigFrames Python code 3. BigQuery ML/AI functions.