ai-core
TanStack AI is a type-safe, provider-agnostic SDK for building AI applications with server-side functions in core packages and client-side hooks in framework-specific packages. Use this skill to understand core concepts, choose between chat streaming, tool calling, media generation, structured outputs, provider configuration, middleware, and custom backend integration patterns.
git clone --depth 1 https://github.com/TanStack/ai /tmp/ai-core && cp -r /tmp/ai-core/packages/ai/skills/ai-core ~/.claude/skills/ai-coreSKILL.md
# TanStack AI — Core Concepts TanStack AI is a type-safe, provider-agnostic AI SDK. Server-side functions live in `@tanstack/ai` and provider adapter packages. Client-side hooks live in framework packages (`@tanstack/ai-react`, `@tanstack/ai-solid`, etc.). Always import from the framework package on the client — never from `@tanstack/ai-client` directly (unless vanilla JS). ## Sub-Skills | Need to... | Read | | ------------------------------------------------- | --------------------------------------------- | | Build a chat UI with streaming | ai-core/chat-experience/SKILL.md | | Survive a browser reload (no extra package) | ai-core/client-persistence/SKILL.md | | Add tool calling (server, client, or both) | ai-core/tool-calling/SKILL.md | | Generate images, video, speech, or transcriptions | ai-core/media-generation/SKILL.md | | Get typed JSON responses from the LLM | ai-core/structured-outputs/SKILL.md | | Choose and configure a provider adapter | ai-core/adapter-configuration/SKILL.md | | Implement AG-UI streaming protocol server-side | ai-core/ag-ui-protocol/SKILL.md | | Add analytics, logging, or lifecycle hooks | ai-core/middleware/SKILL.md | | Coordinate multi-instance work with locks | ai-core/locks/SKILL.md | | Connect to a non-TanStack-AI backend | ai-core/custom-backend-integration/SKILL.md | | Turn on/off debug logging, pipe into pino/winston | ai-core/debug-logging/SKILL.md | | Persist chats server-side (history, runs) | See `@tanstack/ai-persistence` package skills | | Set up Code Mode (LLM code execution) | See `@tanstack/ai-code-mode` package skills | | Give the model a catalog of SKILL.md skills | See `@tanstack/ai-skills` package skills | ## Companion packages Some capabilities live in their own package and ship their own skills. Install the package, then read its skills — do not guess the API from this file. ### `@tanstack/ai-persistence` — durable chat state Makes a conversation survive a reload, a server restart, a second device, or a paused tool approval. It ships the **store contracts** (`MessageStore`, `RunStore`, `InterruptStore`, `MetadataStore`), the `withPersistence` / `withGenerationPersistence` middleware, `reconstructChat` for server-side hydrate, an in-memory reference backend, and a conformance testkit. Multi-instance locks are **not** in this package — `LockStore` / `withLocks` ship in `@tanstack/ai/locks`; see ai-core/locks. The `runs` store contract is typed against run lifecycle types (`RunStatus`, `RunRecord`, `RunStore`, `defineRunStore`, `InMemoryRunStore`), which ship in `@tanstack/ai` itself; see ai-core/middleware. It does **not** ship a backend for your database — you implement the stores against Postgres, SQLite, D1, Mongo, or whatever you run, and the package's skills walk you through it (including Drizzle, Prisma, and Cloudflare recipes). ```bash pnpm add @tanstack/ai-persistence npx @tanstack/intent@latest install ``` The skills ship **inside** the package, so they only exist on disk once it is installed — the second command re-scans `node_modules` and wires them into the agent config. Until then the paths below resolve to nothing. Entry point: `node_modules/@tanstack/ai-persistence/skills/ai-persistence/SKILL.md` | Need to... | Read | | ----------------------------------------------- | --------------------------------------- | | Wire server-side chat history, runs, interrupts | ai-persistence/server/SKILL.md | | Implement the store interfaces for your DB | ai-persistence/stores/SKILL.md | | Write the adapter for the DB your app runs | ai-persistence/build-*-adapter/SKILL.md | Browser-side persistence is **not** in this package — it ships with the framework packages, so read **ai-core/client-persistence** instead. ### `@tanstack/ai-code-mode` — LLM code execution See the `ai-code-mode` skill in that package. ### `@tanstack/ai-skills` — portable Agent Skills at runtime Gives the model a library of `SKILL.md` skills it can load on demand, on any provider, via the `withSkills` middleware and a `load_skill` tool. Skills come from `inlineSkill`, `skillDirectory`, or a build-time bundle. This is the runtime feature for the model **inside your app**, not the coding-assistant skills this file is part of, and not the hosted `codeExecutionTool` / `shellTool` skills (those run in a provider sandbox). ```bash pnpm add @tanstack/ai-skills npx @tanstack/intent@latest install ``` Entry point: `node_modules/@tanstack/ai-skills/skills/ai-skills/SKILL.md` ## Quick Decision Tree - Setting up a chatbot? → ai-core/chat-experience - Adding function calling? → ai-core/tool-calling - Generating media (images, audio, video)? → ai-core/media-generation - Need structured JSON output? → ai-core/structured-outputs - Choosing/configuring a provider? → ai-core/adapter-configuration - Building a server-only AG-UI backend? → ai-core/ag-ui-protocol - Adding analytics or post-stream events? → ai-core/middleware - Surviving reloads / multi-device / durable approvals? → `@tanstack/ai-persistence` skills - Connecting to a custom backend? → ai-core/custom-backend-integration - Turning on debug logging to trace chunks/tools/middleware? → ai-core/debug-logging - Debugging mistakes? → Check Common Mistakes in the relevant sub-skill ## Critical Rules 1. **This is NOT the Vercel AI SDK.** Use `chat()` not `streamText()`. Use `openaiText()` not `createOpenAI()`. Import from `@tanstack/ai`, not `ai`. 2. **Import from framework package on client.** Use `@tanstack/ai-react` (or solid/vue/svelte/preact), not `@tanstack/ai-client`. 3. *
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Triage all open GitHub issues, PRs, and discussions in the current repository by fanning out up to 100 parallel subagents (one per item), then produce a single prioritized report ranking which PRs to review first, which issues to address first, and which discussions need maintainer attention. Use when the user asks to "triage open issues/PRs", "triage discussions", "prioritize the backlog", "what should I review first", "sweep the repo", or any request to bulk-evaluate open GitHub work and recommend an order.
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