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Provider Skills

Provider Skills are hosted, provider-managed capability bundles that the model loads on demand and runs inside the provider's server-side sandbox. You reference them by a skill ID; the provider handles installation and execution.

Not to be confused with @tanstack/ai-code-mode-skills, which are locally-generated TypeScript functions evaluated client-side. Provider Skills run entirely on the provider's infrastructure.

Skills are inert without an execution tool. The execution tool activates the sandbox; skills are additional bundles that run inside it:

  • Anthropic: skills attach to codeExecutionTool (@tanstack/ai-anthropic/tools).
  • OpenAI: skills nest inside shellTool (@tanstack/ai-openai/tools) and require the Responses API.

You already have a chat() call working. By the end of this page you will have attached a hosted skill to the right execution tool, with the provider handling the rest.

Anthropic: skills via codeExecutionTool

1. Install the package

sh
npm install @tanstack/ai-anthropic
npm install @tanstack/ai-anthropic

2. Add the codeExecutionTool with skills

Import codeExecutionTool from @tanstack/ai-anthropic/tools, not from the adapter root. Pass a skills array as the second argument.

typescript
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { anthropicText } from '@tanstack/ai-anthropic'
import { codeExecutionTool } from '@tanstack/ai-anthropic/tools'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: anthropicText('claude-sonnet-4-5'),
    messages,
    tools: [
      codeExecutionTool(
        { type: 'code_execution_20250825', name: 'code_execution' },
        {
          skills: [{ type: 'anthropic', skill_id: 'pptx', version: 'latest' }],
        },
      ),
    ],
  })

  return toServerSentEventsResponse(stream)
}
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { anthropicText } from '@tanstack/ai-anthropic'
import { codeExecutionTool } from '@tanstack/ai-anthropic/tools'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: anthropicText('claude-sonnet-4-5'),
    messages,
    tools: [
      codeExecutionTool(
        { type: 'code_execution_20250825', name: 'code_execution' },
        {
          skills: [{ type: 'anthropic', skill_id: 'pptx', version: 'latest' }],
        },
      ),
    ],
  })

  return toServerSentEventsResponse(stream)
}

The adapter automatically:

  • Lifts your skills into the request's top-level container.skills parameter (the shape Anthropic's API requires).
  • Attaches the code-execution-2025-08-25 beta header, plus the skills-2025-10-02 beta header when skills are present.

You do not set beta headers manually.

Skill shape

Each entry in the skills array is an AnthropicContainerSkill:

FieldTypeRequiredNotes
type'anthropic' | 'custom'yes'anthropic' for Anthropic-hosted skills; 'custom' for your own bundles.
skill_idstringyes1–64 characters.
versionstringnoSpecific version string, or 'latest' (default when omitted).

Up to 8 skills per request. The factory throws at call time if you exceed this or supply an invalid skill_id.

Deprecation notice

Setting skills via modelOptions.container.skills is deprecated. Use codeExecutionTool(config, { skills }) instead — the legacy path bypasses the automatic beta-header wiring.

OpenAI: skills via shellTool (Responses API only)

The OpenAI shellTool accepts an environment object that can carry a skills array. This is Responses API only; the Chat Completions API does not support the shell tool.

1. Install the package

sh
npm install @tanstack/ai-openai
npm install @tanstack/ai-openai

2. Add the shellTool with skills

typescript
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { shellTool } from '@tanstack/ai-openai/tools'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: openaiText('gpt-5.2'),
    messages,
    tools: [
      shellTool({
        environment: {
          type: 'container_auto',
          skills: [
            { type: 'skill_reference', skill_id: 'skill_abc', version: '2' },
          ],
        },
      }),
    ],
  })

  return toServerSentEventsResponse(stream)
}
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { shellTool } from '@tanstack/ai-openai/tools'

export async function POST(request: Request) {
  const { messages } = await request.json()

  const stream = chat({
    adapter: openaiText('gpt-5.2'),
    messages,
    tools: [
      shellTool({
        environment: {
          type: 'container_auto',
          skills: [
            { type: 'skill_reference', skill_id: 'skill_abc', version: '2' },
          ],
        },
      }),
    ],
  })

  return toServerSentEventsResponse(stream)
}

Skill shape

Each entry in the skills array is a SkillReference:

FieldTypeRequiredNotes
type'skill_reference'yesAlways 'skill_reference' for OpenAI.
skill_idstringyesThe skill identifier provided by OpenAI.
versionstringnoA positive integer as a string (e.g. '2') or 'latest'.

Note: version is a string, not a number.

Scope

Only hosted, managed-by-id skills are wired by these factories:

  • Anthropic: type: 'anthropic' or type: 'custom'
  • OpenAI: type: 'skill_reference'

Inline bundles, local-path references, and upload-API skill creation are not handled by codeExecutionTool or shellTool.

Related pages

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Provider Tools
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