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Found 9,761 Skills
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
This skill should be used when the user asks to "create a TypeScript project", "set up Node.js project", "scaffold new project", "initialize TypeScript repo", "create a new library", "set up a CLI tool", or mentions setting up a new TypeScript/Node.js codebase.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for SSR, template rendering, route loaders, hydration payloads, server-client render boundaries, and template-to-handler enforcement gaps. Use when the user asks to inspect SSR or template routes, trace render context or hydration data, compare template gating with handler enforcement, explain preview or hidden-route rendering, or connect render pipeline behavior to the decisive branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Use this skill whenever working with QuestDB — a high-performance time-series database. Trigger on any mention of QuestDB, time-series SQL with SAMPLE BY, LATEST ON, ASOF JOIN, ILP ingestion, or the questdb Python/Go/Java/Rust/.NET client libraries. Also trigger when writing Grafana queries against QuestDB, creating materialized views for time-series rollups, working with order book or financial market data in QuestDB, or any SQL that involves designated timestamps or time-partitioned tables. QuestDB extends SQL with unique time-series keywords — standard PostgreSQL or MySQL patterns will fail. Always read this skill before writing QuestDB SQL to avoid hallucinating incorrect syntax.
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
Advanced Rust patterns for ownership, traits, async, error handling, macros, type system tricks, unsafe, and performance. Use when tackling complex Rust problems — not basic syntax, but multi-concern tasks like designing cancellation-safe async services, choosing between trait objects and generics, building typestated APIs, structuring error hierarchies across crate boundaries, writing proc macros, or optimizing hot paths with zero-cost abstractions. Do not use for basic Rust syntax, simple CLI tools, or beginner ownership questions.
Build, validate, and troubleshoot deep links for Codex, Cursor, VS Code, Visual Studio, and similar tools. Use when users ask for clickable links (especially in Slack) that open threads, files, folders, or app settings.
Apply when improving VTEX IO Node or .NET services for latency, throughput, and resilience: in-process LRU, VBase, stale-while-revalidate, AppSettings loading, request context, parallel client calls, and avoiding duplicate work. Covers application-level performance patterns that complement edge/CDN caching. Use when optimizing backends beyond route-level Cache-Control.
Edit the Prisma Next data contract — add models, fields, relations, indexes, enums, type aliases, polymorphic types (`@@discriminator` / `@@base`), use extension namespaces (`pgvector.Vector(...)`, `cipherstash.EncryptedString(...)`), wire `prisma-next.config.ts` with `defineConfig` from the `@prisma-next/<target>/config` façade, and run `prisma-next contract emit`. Use for schema, models, fields, attributes, soft delete, paranoid, scopes, validations, callbacks, prisma schema, PSL, contract.prisma, contract.ts, contract.json, contract.d.ts, façade imports, `@prisma-next/postgres/config`, `@prisma-next/postgres/contract-builder`, `@prisma-next/postgres/control`, `@prisma-next/mongo/config`, `@prisma-next/mongo/contract-builder`, `extensions:`, `extensionPacks`, pgvector, cipherstash, postgis, paradedb, PN-CLI-4002, PN-CLI-4003, PN-CLI-4011.
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
SkillsMP Skill Marketplace Search and Management Tool. Provides complete functions for searching, viewing details, installing and updating skills on the https://skillsmp.com/ website. Supports two search modes: keyword search and AI semantic search. Use this tool when you need to: (1) Search for skills on specific topics (such as "SEO", "video production"), (2) Find relevant skills through natural language descriptions (such as "how to create a web scraper"), (3) View detailed skill information (version, author, rating, examples), (4) Install discovered skills with one click, (5) Check for updates of installed skills, (6) Manage local skill library.
Import existing Azure resources into Terraform using Azure CLI discovery and Azure Verified Modules (AVM). Use when asked to reverse-engineer live Azure infrastructure, generate Infrastructure as Code from existing subscriptions/resource groups/resource IDs, map dependencies, derive exact import addresses from downloaded module source, prevent configuration drift, and produce AVM-based Terraform files ready for validation and planning across any Azure resource type.