Development Claude Skills
8 673 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 1 239 copies of these same skills sitting in other people's repositories — counted once here, not 1 239 times.
8 673 unique 759 authors 10 updated this month 1 368 from vendors
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).
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.
React DevTools CLI for AI agents. Use when the user asks you to debug a React or React Native app at runtime, inspect component props/state/hooks, diagnose render performance, profile re-renders, find slow components, or understand why something re-renders. Triggers include "why does this re-render", "inspect the component", "what props does X have", "profile the app", "find slow components", "debug the UI", "check component state", "the app feels slow", or any React runtime debugging task.
Generate comprehensive API documentation including endpoint descriptions, request/response examples, authentication guides, error codes, and SDKs. Creates OpenAPI/Swagger specs, REST API docs, and developer-friendly reference materials. Use when users need to document APIs, create technical references, or write developer documentation.
Load tests API endpoints with progressive concurrency. Measures response times, error rates, throughput, and identifies breaking points. Generates a detailed report with latency percentiles, throughput curves, bottleneck analysis, and optimization recommendations.
Generate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
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.
Audit npm dependencies for security vulnerabilities, outdated packages, and unused dependencies. Use when checking for security issues, updating packages, or cleaning up dependencies.
Structured deal assessment using MEDDIC, BANT. Risk scoring, required evidence by stage, red flag detection, coaching points.
Create error boundaries, error handling, and fallback UIs for React applications. Use when implementing error handling, creating fallback components, or setting up error reporting.
Uses 1M context window to ingest an entire codebase and output a file-by-file migration plan. Supports JS to TS, React class to hooks, framework migrations, and more. Generates migration-plan.md with file inventory, dependency graph, migration order, file-by-file changes, estimated effort, and risk assessment.
Uses Managed Agents' 14.5-hour runtime to audit an entire codebase overnight. Security, performance, accessibility, dependency issues. You wake up to a full report.
Aggregate prospect intelligence from multiple sources including news, social media, company websites, and financial data.
Generate React components with TypeScript, proper props, hooks, and accessibility. Use when creating new React components, UI elements, or refactoring existing components.
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
>- This skill should be used when the user asks to "manage UniFi devices", "configure UniFi networks", "create a VLAN", "provision an SSID", "create firewall rules", "reorder firewall policies", "create a NAT rule", "set up port forwarding", "configure masquerade NAT", "add DNS records", "manage traffic matching lists", "create DHCP reservations", "list DHCP reservations", "block a client", "kick a client", "find a client by IP or name", "adopt a device", "restart a UniFi device", "cycle a PoE port", "upgrade device firmware", "run a speed test", "stream UniFi events", "watch real-time events", "query UniFi stats", "analyze DPI traffic", "enable DPI", "generate hotspot vouchers", "show network topology", "audit firewall policies", "create a backup", "call the raw UniFi API", "check network health", or any task involving UniFi network infrastructure management via the unifly CLI. Also triggers on mentions of unifly, UniFi, Ubiquiti, UDM, UCG, USG, USW, UAP, UXG, UNVR, U6, U7, or UniFi controller operations.
Create a new Langfuse integration page in the langfuse-docs repo. Use this skill whenever the user wants to add, create, draft, or scaffold an integration page, cookbook, or docs page for a new tool/framework/model-provider/gateway in Langfuse — triggers include "new integration", "integration page", "docs page for <X>", "cookbook for <X>", "add <X> to langfuse docs", or any request that results in a new `cookbook/integration_*.ipynb`. Also use when the user pastes working integration code, a link to a partner's docs, or rough notes and wants them turned into the standard Langfuse integration notebook. The skill produces a correctly formatted Jupyter notebook, updates `cookbook/_routes.json`, and tries to fetch the partner logo into `public/images/integrations/`.
Agent-applied GRACE 3 to GRACE 4 migration workflow. CLI validates the result but does not convert or delete files.
Operate the GRACE 4 CLI for .grace linting, status, module navigation, verification navigation, and file-local semantic markup.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
Analyze and fix GitHub issues in the current repository, including issue research, scoped implementation, and testing. Use when the user asks to fix, investigate, or work on a GitHub issue by number or URL. Create branches, commits, pushes, or pull requests only when the user explicitly requests those delivery actions.
Review GitHub pull requests with evidence-backed, multi-perspective analysis and false-positive filtering. Use when the user asks to review, inspect, or check a GitHub pull request by number or URL. Default to reporting findings locally; publish comments, submit reviews, or approve only when the user explicitly authorizes that GitHub mutation. Do not use for local uncommitted changes.
Generate N analysis scripts from a single methodology template × multiple exposure/outcome combinations. The "80-person team" pattern — same validated method, swap variables only. Produces batch R/Python code + summary matrix.
> Interactive sample size calculator for medical research. Decision-tree guided test selection, reproducible R/Python code, effect size interpretation, and IRB-ready justification text. Supports diagnostic accuracy, agreement, proportions, continuous outcomes, survival, ANOVA, logistic regression, and non-inferiority/equivalence designs.
> Offer your local changes back to the project — a journal profile you added, a checklist item you fixed, a skill you adapted to your department — as a pull request or an issue, without ever typing a git command. Detects what you changed against the installed version, scans it for patient data and identifiers, shows you every line, and sends nothing until you confirm.
> De-identify clinical research data before LLM-assisted analysis. Standalone Python CLI detects PHI via regex + heuristics with 10 country locale packs (kr, us, jp, cn, de, uk, fr, ca, au, in). Interactive terminal review. No LLM touches raw data — the script runs locally without any network or AI calls.
> Intake and normalize a new radiology research project. Classifies project type, summarizes current state, identifies missing inputs, recommends next steps, and scaffolds lightweight project memory files.
Research project management for medical manuscripts. Scaffold project structure, track writing progress across phases, maintain project memory files, generate submission checklists and backwards timelines. Commands: init, status, sync-memory, checklist, timeline.
Diagnostic checklist for the MedSci Skills runtime. Verifies Python, R, Node, Claude Code, Git, Zotero, and configured MCP servers, and prints a pass/fail table with links to the right setup doc for any missing component. Read-only — does not install anything.
Use when work should be delegated to Claude Code CLI, especially headless `claude -p` runs, automation scripts, CI jobs, resumable sessions, or requests to use Claude/Claude Code for a task.
> Runs a Greptile CLI review for the current local branch, installing or authenticating the CLI when needed, then summarizes JSON findings for the user. Use when the user wants Greptile feedback before opening a PR, outside a hosted PR review flow, or directly from a local checkout.
Use when verifying SDK event payloads in the Android SDK sandbox, debugging what events are emitted, or validating request bodies via logcat with the sample Kotlin app.
Applies React/TypeScript type safety, component design, and state management rules. Use when implementing React components.
Designs frontend tests using the repository's configured React test and browser harnesses, including RTL, MSW, Vitest, and Playwright when present. Use when adding or reviewing component, loading/error-state, integration, or frontend E2E tests.
Defines React environment, component architecture, state/data flow, build verification, and frontend non-functional criteria from repository evidence. Use when configuring or designing a React frontend, its build, or its runtime boundaries.
Applies type safety and error handling rules. Enforces no-any policy and type guards. Use when implementing TypeScript or reviewing types.
リポジトリの根拠に基づき、Reactの環境、コンポーネントアーキテクチャ、状態・データフロー、ビルド検証、フロントエンドの非機能基準を定義。Reactフロントエンド、そのビルド、ランタイム境界の設定・設計時に使用。