ai-infrastructure-hugg…
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation,…
Netlify deployment platform — serverless functions, edge functions, redirects, forms, Blobs, build plugins
$ npx -y skills add agents-inc/skills --skill infra-platform-netlify --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/infra-platform-netlifyContext preview
The summary Claude sees to decide when to auto-load this skill.
Netlify deployment platform — serverless functions, edge functions, redirects, forms, Blobs, build plugins
name: infra-platform-netlify description: Netlify deployment platform — serverless functions, edge functions, redirects, forms, Blobs, build plugins
> **Quick Guide:** Netlify deploys sites from Git with automatic builds, CDN distribution, and serverless compute. Use `netlify.toml` for all configuration (redirects, headers, build settings, function schedules, plugins). Serverless functions live in `netlify/functions/` and use the standard `(req: Request, context: Context) => Response` signature. Edge functions run on Deno at the network edge for geo-personalization and request transformation. Use `Netlify.env.get()` for environment variables in functions — never `process.env`. Use Netlify Blobs for key-value storage accessible from functions and edge functions.
---
<critical_requirements>
> **All code must follow project conventions in CLAUDE.md** (kebab-case, named exports, import ordering, `import type`, named constants)
**(You MUST use `Netlify.env.get()` to access environment variables in functions — NOT `process.env` which is unavailable in the modern functions runtime)**
**(You MUST use the `.mts` file extension for serverless functions to get ES module support — `.ts` defaults to CommonJS unless `"type": "module"` is in package.json)**
**(You MUST use `context.waitUntil()` for post-response background work — work not passed to waitUntil may be cancelled when the response is sent)**
**(You MUST keep redirects and headers in `netlify.toml` — they are global and NOT scoped to deploy contexts)**
</critical_requirements>
---
---
**Auto-detection:** Netlify, netlify.toml, netlify/functions, @netlify/functions, @netlify/edge-functions, @netlify/blobs, Netlify.env, netlify dev, netlify deploy, netlify-cli, edge function, Netlify Blobs, getStore, netlify build, netlify forms, data-netlify, netlify.app, deploy-preview, branch-deploy, Netlify Identity
**When to use:**
**When NOT to use:**
**Key patterns covered:**
---
<philosophy>
Netlify is a Git-centric platform: push to a branch, Netlify builds and deploys automatically. Configuration lives in `netlify.toml` alongside your code. The platform provides three compute primitives:
1. **Serverless Functions** — Node.js-based, up to 60 seconds execution, 1 GB memory. For API endpoints, webhooks, form handlers, and scheduled tasks. 2. **Edge Functions** — Deno-based, 50ms CPU limit, run at the nearest edge node. For request/response transformation, geo-personalization, A/B testing, and authentication. 3. **Background Functions** — Same as serverless but async (client gets 202 immediately), up to 15 minutes. For long-running tasks like data processing and batch operations.
**Key architectural decisions:**
**When to use Netlify:**
**When NOT to use Netlify:**
</philosophy>
---
<patterns>
The official skills marketplace for Agents Inc. 150+ skills covering everything from React and Prisma to Redis, ElevenLabs, and infrastructure tooling. Pick the skills that match your stack and install them via Claude Code. Need more control?
Repo: agents-inc/skills
Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation,…
LiteLLM proxy server setup, TypeScript client patterns via OpenAI SDK, model routing, fallbacks, load balancing, spend tracking, virtual keys, and production…
Serverless GPU compute platform for AI model deployment — web endpoints, GPU functions, model serving, and TypeScript client patterns
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and…
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation,…