neon
Assists with building applications using Neon serverless PostgreSQL. Use when setting up serverless databases, creating branches for development workflows, integrating with edge runtimes, or configuring Prisma/Drizzle with Neon. Trigger words: neon, serverless postgres, database branching, neon serverless driver, pgvector, neon database.
What this skill does
# Neon ## Overview Neon provides serverless PostgreSQL with scale-to-zero compute, database branching, and an HTTP-based driver for edge runtimes. It offers full PostgreSQL compatibility including extensions like pgvector, with features like copy-on-write branching for development workflows and automatic preview deployment databases. ## Instructions - When setting up connections, use the pooled connection string (`-pooler` suffix) for serverless functions and the direct connection string for long-running Node.js servers. - When creating development workflows, use database branching (`neon branches create`) to give each feature or PR its own isolated database that inherits schema from the parent branch. - When deploying to edge runtimes (Cloudflare Workers, Vercel Edge), use `@neondatabase/serverless` which communicates over HTTP instead of TCP sockets. - When integrating with ORMs, use `@prisma/adapter-neon` for Prisma or `drizzle-orm/neon-serverless` for Drizzle, and reserve raw SQL for complex queries and migrations. - When enabling AI features, install the `pgvector` extension at project creation for vector embeddings and ANN search. - When configuring scaling, set `autosuspend_delay` to 300s for development and 0 (never suspend) for production, and configure compute autoscaling between 0.25 and 8 CU. - When running migrations, apply them on the main branch so feature branches inherit schema changes automatically. ## Examples ### Example 1: Set up Neon with Drizzle ORM for a Next.js app **User request:** "Connect my Next.js project to Neon using Drizzle ORM" **Actions:** 1. Create a Neon project and obtain the pooled connection string 2. Install `@neondatabase/serverless` and `drizzle-orm` 3. Configure Drizzle with the Neon serverless adapter 4. Define schema and run initial migration on the main branch **Output:** A type-safe database layer using Drizzle with Neon that works in both server components and edge functions. ### Example 2: Set up branch-per-PR workflow **User request:** "Automatically create a database branch for each pull request" **Actions:** 1. Configure Neon-Vercel integration for automatic branch creation 2. Set up CI script using `neonctl branches create --name pr-$PR_NUMBER` 3. Pass branch connection string as environment variable to preview deployment 4. Add cleanup step to delete branch when PR is closed **Output:** An isolated database branch for each PR with automatic lifecycle management. ## Guidelines - Always use the pooled connection string (`-pooler` suffix) for serverless functions to avoid exhausting connection limits. - Use `@neondatabase/serverless` for edge runtimes; use standard `pg` for long-running Node.js servers. - Create database branches for each feature or PR; never develop against the main branch. - Enable `pgvector` extension at project creation if vector search will be needed. - Set `autosuspend_delay` to 300s for dev and 0 for production. - Use Drizzle or Prisma with the Neon adapter; use raw SQL only for complex queries and migrations. - Run migrations on the main branch; feature branches inherit schema automatically.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.