ai-chat_skill

This skill helps you build and manage a full-stack AI chat app with persistence, chat list features, and automatic title generation.
  • TypeScript

8

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill andrelandgraf/fullstackrecipes --skill ai-chat

  • SKILL.md3.4 KB

Overview

This skill builds a complete AI chat application with persistent storage, chat list management, and automatic title generation. It combines a TypeScript full-stack pattern with Neon Postgres, Drizzle ORM, Shadcn UI, and background workflows to deliver a production-ready chat experience. The recipe collection provides step-by-step guidance and integrations for deployment on Vercel.

How this skill works

Conversations are persisted to Neon Postgres using Drizzle and UUID v7 for chronologically-sortable IDs. The chat UI uses Shadcn components and nuqs-driven URL state for shareable filters and deep links. Background workflows generate chat titles with a small LLM after responses are stored, and logging plus auth guard access and observability.

When to use it

  • You need a full-stack AI chat app with reliable persistence and scalable serverless deployment.
  • You want URL-synced chat lists and deep-linkable modals for collaboration or support flows.
  • You require consistent, production-ready patterns (auth, logging, environment validation) before adding AI features.
  • You want automatic, non-blocking chat title generation to improve organization and UX.

Best practices

  • Validate environment variables with a type-safe schema before runtime to avoid leaking secrets to the client.
  • Persist streaming AI messages and tool outputs so partial results can be resumed and audited.
  • Run title generation as an asynchronous background step to keep user-facing latency low.
  • Use UUID v7 or another time-sortable ID for predictable chronological ordering in lists.
  • Configure Pino structured logs for development-friendly output and production JSON ingestion.

Example use cases

  • Customer support chat where each conversation is stored and searchable, and titles summarize ticket topics.
  • Internal team assistant that keeps a history of question/answer threads with rename and delete controls.
  • SaaS product chat widget that logs AI recommendations and streams responses while preserving state across sessions.
  • Knowledge base builder that converts initial queries into titled threads for later curation.

FAQ

Yes—authentication using Better Auth and deployment steps for Next.js on Vercel are included as prerequisite recipes.

How are chat titles generated without slowing responses?

Titles are produced by a fast LLM in a background workflow step that runs after the main response is saved, preventing added latency for users.

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ai-chat skill by andrelandgraf/fullstackrecipes | VeilStrat