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Found 9,243 Skills
Use this skill when the user wants reusable code, scripts, examples, or docs that build on Steel cloud browsers with SDKs, REST APIs, Playwright, Puppeteer, Stagehand, Browser Use, credentials, profiles, files, extensions, embeds, proxies, or CAPTCHA APIs. Do not use for live web browsing performed by the agent; use steel-browser. Route failed-session diagnosis to steel-session-debugging and reliability mitigation to steel-reliability.
Apifox Automated Test Execution, Suites & CI: test-suite, scheduled-task, runner, apifox run, execution parameters, iteration data, report upload and CI regression. For complex test-scenario step modeling, please use apifox-test-scenario.
Use for the CODE half of a managed Intelligence Channel with Slack or Microsoft Teams: customising the Channel a CLI-scaffolded project already ships, or — for a project the CLI did not generate — writing the Channel declaration, the long-running host, and the awaited activation call. Teams provider setup is in scope, because the CLI or dashboard wizard performs it. Creating a Slack app for the first time is not: if no Slack app exists yet, use setup-slack-channel for the provider half and return here for the code.
Use when a developer wants to build their first CopilotKit Channels agent and get it answering in Slack or Microsoft Teams — "set up a channel", "connect my agent to Slack", "get my agent into Teams", or starting from nothing and wanting a working channel end to end. Covers the whole path: inspecting or scaffolding the project, building the AG-UI agent, creating and reconciling the managed Channel with the public CopilotKit CLI, running the long-running host, and proving a real provider mention gets a reply. The workflow is not in this file — it is fetched from https://copilotkit.ai/channels-guide.md at run time, so it cannot go stale against the CLI.
Create and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.
Build, deploy, and secure Model Context Protocol (MCP) servers on Netlify. Use whenever the task involves creating an MCP server, exposing an app or API to AI agents as MCP tools, letting Claude / Cursor / Claude Code call a custom remote server, or adding MCP tools to an existing Netlify site. Covers the MCP SDK + Streamable HTTP transport on a Netlify Function, authentication (single shared secret vs per-user API keys with Netlify Identity), read/write safety, file uploads, and connecting clients. Use even when the user just says "MCP", "tool server for an agent", or "let an AI use my API".
Use 1000+ external apps via Composio - either directly through the CLI or by building AI agents and apps with the SDK
Use when building, modifying, or reviewing a Stripe App — or when the user describes something that implies one (e.g. "add a panel to the customer page", "customize my Stripe Dashboard", "react to Stripe events from my app", "connect my service to Stripe without sharing API keys"). Covers the full app development workflow (scaffold, preview, upload, versioning), UI extension architecture (sandboxed iframe, Stripe UI toolkit, viewports), extension types (UI extensions, backend-only, extension interfaces, embedded apps), authentication (platform keys, OAuth, restricted API keys), stripe-app.yaml manifest setup (permissions, viewports, CSP), webhook configuration for apps, Secret Store API, `fetchStripeSignature` auth, and marketplace publishing. Use when the user mentions Stripe Apps, UI extensions, @stripe/ui-extension-sdk, stripe-app.yaml, Dashboard extensions, or customizing the Stripe Dashboard.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
Spawn a full Claude session in a tmux window to work a task, or hand this conversation to one.
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns