Imported from akillness/jeo-skills (.agent-skills/chatbot-template/SKILL.md). Install upstream with npx skills add akillness/jeo-skills --skill chatbot-template. Copyright stays with the author.

chatbot-template

chatbot-template is shadcn/ui's minimal, opinionated Next.js starter for an AI chatbot: streaming markdown responses (react-markdown + shadcn/typeset), typed tool calling, provider-native web search, and a human-in-the-loop ask_user questionnaire the model can trigger to ask clarifying questions. It deploys to Vercel with zero configuration — the AI Gateway authenticates automatically via OIDC — or runs locally against a gateway API key.

When to use this skill

• Scaffolding a new Next.js AI chatbot instead of hand-wiring useChat, streaming, and markdown rendering from scratch
• Adding a custom tool (server-executed or UI-answered) with a typed part component that renders its input-streaming / input-available / output-available states
• Restricting or reordering the model list, or wiring web search per model
• Hardening the public /api/chat route (rate limiting, spend caps, auth) before putting it in front of real traffic
• Understanding how assistant message "parts" (text, tool-*, source-url) map to components so you can extend the chat UI safely

When not to use this skill

• The user needs persistent chat history, multi-user auth, or a database — this template has none of that; build it on top rather than expecting it out of the box
• The target stack isn't Next.js/React — this is a Next.js App Router template tied to useChat and RSC-style API routes
• The goal is running an LLM entirely offline/bare-metal with no cloud gateway → not this skill; see the nightrun skill for a no-OS boot-to-LLM appliance instead
• The ask is about the AI SDK or shadcn/ui in the abstract, unrelated to this specific starter template → use general AI SDK / shadcn/ui docs instead

Instructions

Step 1: Get the code

git clone --depth 1 https://github.com/shadcn-ui/chatbot-template.git
cd chatbot-template

Or click "Deploy with Vercel" on the README for a zero-config deploy — no env vars needed, since Vercel deployments authenticate to the AI Gateway via OIDC automatically and billing runs on the team's AI Gateway credits.

Step 2: Install and give the app a gateway credential

pnpm install

Then either pull an OIDC token from a linked Vercel project:

vercel link
vercel env pull

or create an API key in the Vercel dashboard (AI Gateway → API Keys) and set it locally:

cp .env.example .env.local
# then set AI_GATEWAY_API_KEY=... in .env.local

Step 3: Run the dev server

pnpm dev

Step 4: Configure the model list

Edit lib/models.ts — the first entry is the default model, and isModelAllowed() restricts /api/chat to only the listed model IDs (see Vercel AI Gateway models for valid IDs).

Step 5: Add a custom tool

1. Create tools/<name>.ts — the filename is the model-facing tool name. Export a tool() with a description, an inputSchema, and an execute function; omit execute for tools the user answers in the UI (like ask_user).
2. Register it in tools/index.ts (add it to baseTools, or conditionally like web_search).
3. Add a part component in components/parts/ and a matching case "tool-<name>" in components/chat-message.tsx.

Message types are inferred from tool definitions via InferUITools, so part.input/part.output are typed — renaming a tool field becomes a build error instead of a silent undefined. See references/commands.md for the full tool-part table and file map.

Step 6: Add shadcn/ui components as needed

npx shadcn@latest add button

Step 7: Harden before public traffic

/api/chat is public and unauthenticated by default — every request spends AI Gateway credits. Before real traffic:

• Rate limit it (Vercel Firewall/WAF, or @upstash/ratelimit) to prevent a denial-of-wallet from a single client
• Set an AI Gateway spend limit as a backstop
• Add auth if the chatbot isn't meant to be public

The route already validates the request body, restricts models to lib/models.ts, caps output tokens/step count, and aborts generation on client disconnect — those bound a single request, not overall volume.

Step 8: Verify before handing off

pnpm lint
pnpm typecheck
pnpm build

Best practices

1. Treat /api/chat as public by default — it has no auth or rate limiting out of the box; do not tell a user it's production-ready without step 7's hardening.
2. Register new tools in both places — tools/index.ts and a case "tool-<name>" in chat-message.tsx. A tool with no part component renders nothing for its output.
3. Model order matters — MODELS[0] in lib/models.ts is the default; don't assume alphabetical or arbitrary ordering is safe to change.
4. Use pnpm, not npm/yarn — the repo ships pnpm-lock.yaml and pnpm-workspace.yaml; a different package manager will produce a divergent lockfile.
5. Prefer npx shadcn@latest add <component> over hand-copying UI code — it keeps components in sync with the project's components.json config.
6. Local dev needs a gateway credential; Vercel deploys don't — don't add AI_GATEWAY_API_KEY handling for the Vercel-deployed path, OIDC already covers it.

References

• references/commands.md — curated command reference, key files, and tool-part extension map
• chatbot-template GitHub repository
• AI SDK docs
• shadcn/ui docs
• Vercel AI Gateway docs
• Project standards: .agent-skills/skill-standardization/SKILL.md

Examples

Example 1: Scaffold and run locally

git clone --depth 1 https://github.com/shadcn-ui/chatbot-template.git
cd chatbot-template
pnpm install
cp .env.example .env.local
# set AI_GATEWAY_API_KEY=... in .env.local
pnpm dev

Example 2: Add a custom tool with a UI part

# 1. tools/weather.ts — export tool({ description, inputSchema, execute })
# 2. tools/index.ts   — add weather: weatherTool to baseTools
# 3. components/parts/weather-part.tsx — render input-streaming/available/output states
# 4. components/chat-message.tsx — add case "tool-weather": <WeatherPart part={part} />
pnpm typecheck
pnpm dev