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Found 3,129 Skills
The fastest and easiest way to build with Stream: Chat, Video, Feeds and Moderation — including live SDK docs search.
Patch, extend, or explain DatoCMS front-end integration code inside an existing web project (Next.js App Router, Nuxt, SvelteKit, Astro, plus React/Vue/Svelte component usage). Use for targeted, per-concern work — adding a draft mode endpoint, wiring Preview Links / Visual Editing flows, fixing Content Link overlays, tuning real-time preview updates/subscriptions, setting up cache-tag invalidation/revalidation flows (Next.js revalidateTag or CDN purge by tags), adding robots/sitemap wiring, or hooking up crawler-safe search integration. Also the go-to skill for framework component/hook wiring with react-datocms, vue-datocms, @datocms/svelte, and @datocms/astro: Image/SRCImage/datocms-image, StructuredText, VideoPlayer (React/Vue/Svelte), SEO/meta helpers (renderMetaTags/toHead/Seo), QuerySubscription/QueryListener realtime patterns, ContentLink components, and Site Search (React/Vue). Prefer this skill whenever the user is modifying a live codebase one concern at a time, asking a framework-specific API question, or mixing several front-end concerns in the same patch.
Search, preview, inspect, and install MagicPath UI components with the magicpath-ai CLI. Use when the user mentions MagicPath, wants to browse or search MagicPath components, preview one, or add one to their project. Also use when the user refers to "designs" — in MagicPath, designs are created and stored as components. Also use when the user mentions themes or theming — MagicPath themes (design systems) contain CSS variables, fonts, and styling instructions.
Choose and create the right Neon branch type for testing and development. Use when users ask about Neon branching, migration testing with real data, isolated test environments, schema-only branch workflows for sensitive data, or branch creation via Neon CLI or Neon MCP. Triggers include "Neon branch", "test migrations safely", "branch production data", "schema-only branch", "reset branch" and "sensitive data testing".
Build, refactor, debug, test, and package Python terminal user interfaces with Textual. Use when the user wants a TUI, terminal dashboard, admin console, multi-screen workflow, keyboard-first tool, data explorer, file browser, markdown or log viewer, editor, command palette, browser-served console app, or a migration from curses/Rich-only UI to Textual—even if they never say “Textual”. Covers TCSS and themes, built-in widgets, screens and modes, reactive state, workers, browser delivery APIs, and pytest Pilot or snapshot testing.
Resilience review and testing: evaluate error handling, graceful degradation, API contract compliance, edge cases, and failure recovery with browser-based fault injection and validation.
Builds and deploys Firebase SQL Connect (aka Firebase Data Connect) backends with PostgreSQL securely. Use when designing schemas with tables and relations, writing authorized queries and mutations, configuring real-time data updates, or generating type-safe SDKs. Use when you need a relational database with Firebase, or when the user mentions SQL Connect or Data Connect.
Update, archive, and delete LaunchDarkly AI Configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them.
Create and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Instrument an existing codebase with LaunchDarkly config tracking. Walks the four-tier ladder (managed runner → provider package → custom extractor + trackMetricsOf → raw manual) and picks the lowest-ceremony option that still captures duration, tokens, and success/error.
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Generates YAML signal configs for agent simulation experiments. Use when the user wants to define what signals to track, how to extract them from run artifacts, and how to aggregate them into experiment-level metrics. Trigger when users say: "generate a signal config", "create signals for my experiment", "I want to track [metric]", "write a signal YAML", "set up extraction for [thing]", "how do I measure [behavior] across runs", "configure signals for [experiment]", "create a signal config", "create signal config file", or "build a signal config".