external/neon-skills/neon-serverless/SKILL.md
Configures Neon Serverless Driver for Next.js, Vercel Edge Functions, AWS Lambda, and other serverless environments. Installs @neondatabase/serverless, sets up environment variables, and creates working API route examples with TypeScript types. Use when users need to connect their application to Neon, fetch or query data from a Neon database, integrate Neon with Next.js or serverless frameworks, or set up database access in edge/serverless environments where traditional PostgreSQL clients don't work.
npx skillsauth add seikaikyo/dash-skills neon-serverlessInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Configures the Neon Serverless Driver for optimal performance in serverless and edge computing environments.
Not recommended for: Complex multi-statement transactions (use WebSocket Pool), persistent servers (use native PostgreSQL drivers), or offline-first applications.
When generating TypeScript/JavaScript code:
Primary Resource: See [neon-serverless.mdc](https://raw.githubusercontent.com/neondatabase-labs/ai-rules/main/neon-serverless.mdc) in project root for comprehensive guidelines including:
npm install @neondatabase/serverless
HTTP Client (recommended for edge/serverless):
import { neon } from '@neondatabase/serverless';
const sql = neon(process.env.DATABASE_URL!);
const rows = await sql`SELECT * FROM users WHERE id = ${userId}`;
WebSocket Pool (for Node.js long-lived connections):
import { Pool } from '@neondatabase/serverless';
const pool = new Pool({ connectionString: process.env.DATABASE_URL! });
const result = await pool.query('SELECT * FROM users WHERE id = $1', [userId]);
See templates/ for complete examples:
templates/http-connection.ts - HTTP client setuptemplates/websocket-pool.ts - WebSocket pool configurationUse scripts/validate-connection.ts to test your database connection before deployment.
Want best practices in your project? Run neon-plugin:add-neon-docs with parameter SKILL_NAME="neon-serverless" to add reference links.
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.