Development Claude Skills
8 676 development skills from 759 authors. They write and change code. Half of them fit into 1 830 tokens or less — that is what one costs your context window when the agent loads it. 1 213 ship runnable scripts rather than instructions alone. 42 of them cannot work without an MCP server, most often rube. We also found 1 172 copies of these same skills sitting in other people's repositories — counted once here, not 1 172 times.
8 676 unique 759 authors 5 242 updated this month 1 369 from vendors
Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.
Next.js 16 Cache Components guidance — PPR, use cache directive, cacheLife, cacheTag, updateTag, and migration from unstable_cache. Use when implementing partial prerendering, caching strategies, or migrating from older Next.js cache patterns.
React best-practices reviewer for TSX files. Triggers after editing multiple TSX components to run a condensed quality checklist covering component structure, hooks usage, accessibility, performance, and TypeScript patterns.
Upgrade Next.js to the latest version following official migration guides and codemods. Use when upgrading Next.js versions, running codemods, or migrating between major releases.
Next.js App Router expert guidance. Use when building, debugging, or architecting Next.js applications — routing, Server Components, Server Actions, Cache Components, layouts, middleware/proxy, data fetching, rendering strategies, and deployment on Vercel.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Vercel Routing Middleware guidance — request interception before cache, rewrites, redirects, personalization. Works with any framework. Supports Edge, Node.js, and Bun runtimes. Use when intercepting requests at the platform level.
Vercel Runtime Cache API guidance — ephemeral per-region key-value cache with tag-based invalidation. Shared across Functions, Routing Middleware, and Builds. Use when implementing caching strategies beyond framework-level caching.
Turbopack expert guidance. Use when configuring the Next.js bundler, optimizing HMR, debugging build issues, or understanding the Turbopack vs Webpack differences.
Vercel Agent guidance — AI-powered code review, incident investigation, and SDK installation. Automates PR analysis and anomaly debugging. Use when configuring or understanding Vercel's AI development tools.
Deploy, manage, and develop projects on Vercel from the command line
Vercel CLI expert guidance. Use when deploying, managing environment variables, linking projects, viewing logs, querying metrics, managing domains, or interacting with the Vercel platform from the command line.
Vercel Functions expert guidance — Serverless Functions, Edge Functions, Fluid Compute, streaming, Cron Jobs, and runtime configuration. Use when configuring, debugging, or optimizing server-side code running on Vercel.
Vercel Sandbox guidance — ephemeral Firecracker microVMs for running untrusted code safely. Supports AI agents, code generation, and experimentation. Use when executing user-generated or AI-generated code in isolation.
Full-story verification — infers what the user is building, then verifies the complete flow end-to-end: browser → API → data → response. Triggers on dev server start and 'why isn't this working' signals.
Creates durable, resumable workflows using Vercel's Workflow DevKit. Use when building workflows that need to survive restarts, pause for external events, retry on failure, or coordinate multi-step operations over time. Triggers on mentions of "workflow", "durable functions", "resumable", "workflow devkit", "queue", "event", "push", "subscribe", or step-based orchestration.
Sync skills between local installation and the GitHub source-of-truth repository. Use when asked to install, update, list, or push skills.
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2 by default for state-of-the-art software engineering.
Query, audit, and optimize Google Ads campaigns. Supports two modes: (1) API mode for bulk operations with the google-ads Python SDK, (2) attached-browser mode for users without API access. Use when asked to check ad performance, pause campaigns or keywords, find wasted spend, audit conversion tracking, or optimize Google Ads accounts.
Query and manage Salesforce CRM data via the Salesforce CLI (`sf`). Run SOQL/SOSL queries, inspect object schemas, create/update/delete records, bulk import/export, execute Apex, deploy metadata, and make raw REST API calls.
Autonomous multi-agent task orchestration with dependency analysis, parallel tmux/Codex execution, and self-healing heartbeat monitoring. Use for large projects with multiple issues/tasks that need coordinated parallel execution.
Look up official company data from European public registries across 11 countries/regions (CZ, SK, PL, DE, UK, NL, RO, HR, SE + EU-level + ESG). Covers company registration, ownership, financial filings, VAT status, ESG data. Use when the user asks to "look up a company", "check registry", "find company info", "look up IČO/KRS/LEI/CRN", "company due diligence", "check VAT status", "find ownership structure", or needs official data from European registries. Reads tracked companies from data/companies.json. Some lookups use Python scripts (stdlib), some fall back to Apify actors for scraping-based registries.
Patterns for invoking the Apify CLI (`apify`) from agents. Covers authentication, creating/running/pushing Actors, calling Actors in the cloud, and reading results from datasets and key-value stores.
How to think through work, distilled from a stronger model's demonstrated moves - invoke when STARTING to investigate a confusing behavior or debug a root cause; design or evaluate an approach/proposal; plan a multi-step or long-running task; verify work, review findings, or reconcile conflicting reports; write rules, reports, or docs someone else must act on.
Backend engineering judgment, distilled from a stronger model - invoke when CHOOSING a tech stack, language, database, queue, or architecture; designing a service, API, business logic, or schema; making a system production-ready (observability, failure handling, security); or reviewing server-side code and judging codebase health. Scenario-driven stack tradeoffs, logic-design rules, data discipline, production floors, and a rot catalog (the early signs of unmaintainable code).
Software development knowledge reference covering Python, Go, Rust, TypeScript, Java, C++, and Shell. Use when writing code, debugging, or following language-specific best practices.
Mobile development knowledge reference covering iOS (SwiftUI), Android (Jetpack Compose), React Native, and Flutter. Use when building mobile apps, working with cross-platform frameworks, or implementing native UI patterns.
Code relationship graph and temporal intelligence via abyss CLI. Provides caller tracing, impact analysis, hotspot detection, and file-level context gathering. Agent automatically runs abyss before modifying code to check impact. Works with any agent that has shell access.
Generates README.md and DESIGN.md scaffolds by analyzing module structure. Use when creating documentation templates for new modules. Automatically triggered at module creation.
End-to-end change-shipping closed loop for non-trivial work — research → proposal doc → phased guarded implementation → PR self-review → harden → merge. Use when taking a substantial change from idea to merged PR, when a task needs a written proposal before code, when implementing in reversible phases behind a guarded commit chain (assert + test + verify, all green before commit), or when self-reviewing your own PR. Complements automating-devops (which is the git/CI/release knowledge reference); this skill is the orchestration spine that sequences a change from zero to merged.
Scans directory structure, detects missing documentation, and verifies code-doc synchronization. Use when checking module completeness, README presence, or DESIGN.md alignment. Automatically triggered after creating new modules.
NestJS best practices and architecture patterns for building production-ready applications. This skill should be used when writing, reviewing, or refactoring NestJS code to ensure proper patterns for modules, dependency injection, security, and performance.
Initialize and configure the TestDriver SDK client
Listen to SDK lifecycle events with wildcard support
Initialize and configure the TestDriver SDK client
Reduction pass — cuts content, structure, visuals, and dead code that doesn't answer a user question or drive an action, respecting CRAFT_LEVEL. Use when the UI feels cluttered, has too many CTAs, walls of text, or decorative noise, or when the user says "simplify this" / "it feels too busy". Invoke when the user asks for distill on their UI, or mentions 'distill' alongside design / UI / frontend work.
Front door. Reads the current project (framework, tokens, brief, spec, harness) and reports what ui-craft can do right now, then routes you to the right next step. Run this first if you're new or unsure where to begin. No code changes. Invoke when the user asks for start on their UI, or mentions 'start' alongside design / UI / frontend work.
>- Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.
Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.
Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.
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).
Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.
Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.
Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage AI Gateway rate limits; discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).