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Found 530 Skills
This skill provides comprehensive knowledge for integrating Neon serverless Postgres and Vercel Postgres (which is built on Neon infrastructure) into web applications. It should be used when setting up serverless Postgres databases, configuring connection pooling for edge and serverless environments, implementing database branching workflows, or troubleshooting Postgres connection issues in Cloudflare Workers, Vercel Edge Functions, or Node.js serverless functions. Use this skill when: - Setting up Neon Postgres for Cloudflare Workers, Vercel Edge, or serverless environments - Configuring Vercel Postgres for Next.js applications - Implementing database branching workflows (git-like database branches) - Integrating Drizzle ORM or Prisma with Neon/Vercel Postgres - Debugging connection pool errors, transaction timeouts, or SSL configuration issues - Migrating from D1/SQLite to Postgres or from traditional Postgres to serverless Postgres - Setting up point-in-time restore (PITR) or database backups - Encountering errors like "connection pool exhausted", "TCP connections not supported in serverless", or "sslmode required" Keywords: neon postgres, @neondatabase/serverless, @vercel/postgres, serverless postgres, postgres edge, neon branching, vercel database, http postgres, websocket postgres, pooled connection, drizzle neon, prisma neon, postgres cloudflare, postgres vercel edge, sql template tag, neonctl, database branches, point in time restore, postgres migrations, serverless sql, edge database, neon api, vercel sql
Advanced pytest patterns including custom markers, plugins, hooks, parallel execution, and pytest-xdist. Use when implementing custom test infrastructure, optimizing test execution, or building reusable test utilities.
Build trading systems in the style of D.E. Shaw, the pioneering computational finance firm. Emphasizes systematic strategies, rigorous quantitative research, and world-class technology infrastructure. Use when building research platforms, systematic trading strategies, or quantitative finance infrastructure.
Build requirements specification through structured discovery interview. Use when defining scope, gathering requirements, or specifying WHAT work should accomplish - features, bugs, refactors, infrastructure, migrations, performance, documentation, or any other work type. Triggers: spec, requirements, define scope, what to build.
This skill should be used after productive sessions to extract learnings and route them to appropriate Reusable Intelligence Infrastructure (RII) components. Use when corrections were made, format drift was fixed, new patterns emerged, or the user explicitly asks to "harvest learnings" or "capture session intelligence". Transforms one-time fixes into permanent organizational knowledge by implementing updates across multiple files.
Build and deploy AI agents using VM0's agent-native infrastructure. This skill guides you through the complete agent creation workflow - from understanding requirements to deployment and scheduling.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.
Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Expert guide for documenting infrastructure including architecture diagrams, runbooks, system documentation, and operational procedures. Use when creating technical documentation for systems and deployments.
Performance testing specialist for load testing, stress testing, and performance optimization across applications and infrastructure