Nitro Agent Skill

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.

14k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

5703

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/antfu/skills --skill nitro

The instruction itself

3 sections, as written by the author

> 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:

Filesystem routing with H3 v2 event handlers, dynamic params, and method suffixes.
Route rules for declarative caching, headers, redirects, proxying, and auth.
Caching layer (cached handlers/functions, SWR) on top of unstorage KV.
Runtime config overridable via NITRO_* env vars.
Tasks (on-demand + scheduled/cron), WebSockets/SSE, a SQL database layer, and OpenAPI auto-docs.
Plugins & lifecycle hooks, custom renderer/server entry, and portable deployment presets.

> Nitro v3 renamed the package nitropack → nitro and adopts H3 v2 (web-standard Request/Response). If unsure about v2-vs-v3 APIs, read advanced-migration first.

Core

| 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 |

Features

| 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 |

Advanced / Deployment

| 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 |

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take antfu/nitro from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.