Backend

Use when writing API routes, workers, or backend services. Layers on TOP of your base guideline. Covers validation, error handling, async patterns, and architecture enforcement.

Analyze codebases to generate optimal Claude Code Sandbox configurations. Use this skill when users need to set up sandbox security settings for their projects. This skill should be triggered when users ask about sandbox configuration, security settings, or when setting up Claude Code for a new project. It analyzes the codebase stack (Node.js, Python, Rust, Go, PHP, etc.), detects dependencies, and generates appropriate sandbox and permission settings through an interactive Q&A process.

Design and implement REST API integrations with proper error handling, authentication, rate limiting, and testing. Use when building API clients, integrating third-party services, or when users mention API, REST, webhooks, HTTP requests, or service integration.

Expert code review for Python, microservices, security, and production best practices. Automatically activated when code review is needed to identify bugs, security issues, and quality problems.

Expert skill for implementing authentication in Docusaurus static sites. Handles FastAPI backend setup for authentication, JWT token management, and secure API communication. Includes setup for static site generation, client-side authentication, and user data protection. Use when adding authentication to Docusaurus static sites, implementing FastAPI backend for authentication services, or securing API routes with JWT tokens in static site context.

Validates type consistency across Rust, TypeScript, PostgreSQL boundaries. Use when reviewing code, debugging type mismatches, or validating API contracts. Triggers on: check consistency, validate types, find mismatches, cross-language.

Senior SQL developer for query optimization and complex patterns. Use for PostgreSQL, MySQL, SQL Server.

Intelligent documentation system for AI Elements component library. Activate automatically when working with AI-native applications or when AI Elements component names are mentioned (Message, Conversation, Reasoning, Canvas, etc.). Provides context-aware documentation retrieval - fetches examples for implementation queries, component references for API lookups, and smart multi-page fetching for complex tasks.

Builds AI agents using OpenAI Agents SDK with async/await patterns and multi-agent orchestration.Use when creating tutoring agents, building agent handoffs, implementing tool-calling agents, or orchestrating multiple specialists.Covers Agent class, Runner patterns, function tools, guardrails, and streaming responses.NOT when using raw OpenAI API without SDK or other agent frameworks like LangChain.

Patterns for building AI-powered chatbot backends using OpenAI Agents SDK with MCP (Model Context Protocol) server integration in FastAPI applications. Supports both standalone MCP servers and function tools.

GoDaddy tech stack - React, TypeScript, Node.js, GraphQL

Verifica que no haya credenciales, secrets o archivos sensibles antes de pushear a GitHub. Busca API keys hardcodeadas, .env con valores reales, y valida .gitignore.

Expert in implementing Microsoft 365 Copilot agents with TypeSpec code (use AFTER project is created). Use when (1) Writing or editing .tsp files in an existing project, (2) Implementing agent instructions and behavior in TypeSpec, (3) Adding or configuring capabilities (WebSearch, OneDrive, etc.) with proper scoping, (4) Creating API plugin actions with @actions decorator, (5) Defining TypeSpec models and enums, (6) Adding @doc decorators and documentation, (7) Compiling TypeSpec with npm run compile, (8) Fixing TypeSpec compilation errors, (9) Working with @agent, @instructions, @conversationStarter decorators, or (10) Implementing TypeSpec syntax for @microsoft/typespec-m365-copilot library. This skill handles TYPESPEC CODE, not project scaffolding.

Comprehensive Python data engineering patterns for AWS Data Lake, including PySpark, Pandas, Apache Airflow, AWS Glue, ETL pipelines, data quality, schema management, performance optimization, FastAPI services, streaming with Kafka/Kinesis, data validation with Great Expectations, testing strategies, error handling, logging, and production deployment on AWS EMR and Glue.

OpenAI official SDK usage (Python, Node.js). Use when: writing code that calls OpenAI API, implementing chat/embeddings/images/audio features, handling streaming responses, async patterns, error handling with SDK. For raw HTTP/REST calls, see `openai-api` skill.

Identifies deeply nested if-let expressions and suggests let chains for cleaner control flow. Activates when users write nested conditionals with pattern matching.

Génération et mise à jour de rapports hebdomadaires avec CFD (Cumulative Flow Diagram). Activer ce skill quand l'utilisateur parle de récapitulatif, bilan hebdomadaire, rapport de la semaine, ou CFD.

Execute implementation plans step-by-step with quality guardrails (ask when unclear, minimal-change bias, readability checklist, verification). Use when the user asks to execute or follow a plan, run tasks sequentially from a plan file (e.g., docs/plans/*.md), or continue plan steps.

LLM integration patterns for Claude, GPT, Gemini, and Ollama. Activate for AI API integration, prompt engineering, token management, extended thinking, and multi-model orchestration.

AGGRESSIVELY use TOON v2.0 format for biggish regular data (≥5 items, ≥60% uniform). Auto-applies to tables, logs, events, transactions, analytics, API responses, database results. Supports 3 array types (inline, tabular, expanded), 3 delimiters (comma, tab, pipe), key folding for nested objects. Triggers on structured data, arrays, repeated patterns. Use TOON by default when tokens matter - RAG pipelines, tool calls, agents, benchmarks. Keywords "data", "array", "list", "table", "log", "transaction", "metric", "analytics", "API", "database", "query", "TOON".