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Found 1,833 Skills
Eloquent model patterns and database layer. Use when working with models, database entities, Eloquent ORM, or when user mentions models, eloquent, relationships, casts, observers, database entities.
Implements efficient API pagination using offset, cursor, and keyset strategies for large datasets. Use when building paginated endpoints, implementing infinite scroll, or optimizing database queries for collections.
PostgreSQL relational database. Covers SQL queries, indexes, constraints, and performance. Use when working with PostgreSQL. USE WHEN: user mentions "postgres", "postgresql", "pg_", asks about "JSONB queries", "window functions", "recursive CTE", "row level security", "full text search", "partitioning", "pgBouncer", "replication" DO NOT USE FOR: MySQL syntax - use `mysql` instead, MongoDB - use `mongodb` instead, Oracle PL/SQL - use `plsql` instead, SQL Server T-SQL - use `tsql` instead
Create and manage Codex app automations stored in the local SQLite database (~/.codex/sqlite/codex-dev.db). Use when you need to add, list, update, enable/disable, delete, or run-now automations; edit automation names, prompts, RRULE schedules, or cwd scopes; or inspect automation records while troubleshooting app automation behavior.
Conducts comprehensive backend design reviews covering API design quality, database architecture validation, microservices patterns assessment, integration strategies evaluation, security design review, and scalability analysis. Evaluates API specifications (REST, GraphQL, gRPC), database schemas, service boundaries, authentication/authorization flows, caching strategies, message queues, and deployment architectures. Identifies design flaws, security vulnerabilities, performance bottlenecks, and scalability issues. Produces detailed design review reports with severity-rated findings, architecture diagrams, and implementation recommendations. Use when reviewing backend system designs, validating API specifications, assessing database schemas, evaluating microservices architectures, reviewing integration patterns, or when users mention backend design review, API design validation, database design review, microservices assessment, or backend architecture evaluation.
Use when optimizing application performance, reducing load times, improving database queries, meeting performance budgets, or diagnosing bottlenecks in web applications or APIs. Triggers: slow page loads, poor Web Vitals, database timeouts, large bundle size, user-reported sluggishness, scaling preparation.
Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
Create a new sequentially numbered database migration with up/down SQL files
Diagnoses and optimises slow SQL queries using EXPLAIN ANALYZE. Covers identifying bottlenecks (sequential scans, bad estimates, heap fetches), index strategy, query rewrites, and verification. Invoked when the user asks to optimize a query, fix a slow database query, or improve database performance.
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
Audits database schemas for naming conventions, type consistency, nullability patterns, and missing constraints. Provides violations report with recommended fixes. Use for "schema validation", "database linting", "schema standards", or "consistency checks".