external/antfu-skills/nitro/SKILL.md
Nitro is the framework-agnostic server toolkit (powering Nuxt) for building and deploying web servers anywhere. Use when working with nitro.config, server routes/event handlers, route rules, caching, storage, tasks, websockets, or deploying to Node/Bun/Deno/Cloudflare/Vercel.
npx skillsauth add seikaikyo/dash-skills nitroInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The skill is based on Nitro v3 (beta), generated at 2026-06-22.
Nitro is a framework-agnostic, deployment-agnostic server toolkit powered by H3 v2, unstorage, and Vite/Rolldown/Rollup. It powers Nuxt and works standalone. From one codebase it builds optimized output for Node.js, Bun, Deno, Cloudflare, Vercel, Netlify, and more.
Key capabilities:
NITRO_* env vars.Nitro v3 renamed the package
nitropack→nitroand adopts H3 v2 (web-standardRequest/Response). If unsure about v2-vs-v3 APIs, read advanced-migration first.
| Topic | Description | Reference |
|-------|-------------|-----------|
| Routing | File-based routes, defineHandler, params, middleware, route rules, errors | core-routing |
| Configuration | nitro.config.ts, defineConfig, key options, runtime config | core-configuration |
| Storage | unstorage KV, mount points, drivers, dynamic mounts | core-storage |
| Cache | defineCachedHandler, defineCachedFunction, SWR, invalidation | core-cache |
| Assets | Public assets, compression, server assets via storage | core-assets |
| Rendering | Renderer (HTML/SSR), server entry, framework integration | core-rendering |
| Topic | Description | Reference |
|-------|-------------|-----------|
| Plugins & Hooks | definePlugin, runtime lifecycle hooks, error capture | features-plugins |
| Tasks | On-demand & scheduled (cron) tasks, runTask | features-tasks |
| WebSocket & SSE | defineWebSocketHandler, pub/sub, namespaces, event streams | features-websocket |
| Database | Built-in SQL layer via db0, useDatabase, connectors | features-database |
| OpenAPI | Auto spec from defineRouteMeta, Scalar/Swagger UIs | features-openapi |
| Topic | Description | Reference |
|-------|-------------|-----------|
| Deployment Presets | Runtimes & providers, compatibility dates, platform integration | deploy-presets |
| v2 → v3 Migration | Package rename, nitro/* imports, H3 v2 API, preset changes | advanced-migration |
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.