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Apollo Connectors

Organization
apollographql
apollo-connectors

Guide for integrating REST APIs into GraphQL supergraphs using Apollo Connectors with @source and @connect directives. Use this skill when the user: (1) mentions "connectors", "Apollo Connectors", or "REST Connector", (2) wants to integrate a REST API into GraphQL, (3) references @source or @connect directives, (4) works with files containing "# Note to AI Friends: This is an Apollo Connectors schema".

Overview

Publisherapollographql
Repositoryskills
Skill nameapollo-connectors
Stars
112
Forks
12
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by apollographql on GitHub. Read the source before you install it.

Installation

Install the Apollo Connectors AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/apollographql/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/apollo-connectors .claude/skills/apollo-connectors
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apollo Connectors in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Apollo Connectors on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Apollo Connectors is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Apollo Connectors Schema Assistant

MCP Tools

If GraphOS MCP Tools are available, use them:

  • connectors-spec: Fetch the complete Connectors specification before starting any connector work
  • apollo_docs_search: Search for relevant documentation
  • apollo_docs_read: Read specific documentation pages by slug

Documentation paths by topic:

  • Requests: /graphos/connectors/requests/url, /headers, /body, /batching
  • Responses: /graphos/connectors/responses/fields, /error-handling
  • Mapping: /graphos/connectors/mapping, /arrays, /enums, /literals
  • Entities: /graphos/connectors/entities, /patterns

Process

Follow this 5-step process. DO NOT skip any steps.

Step 1: Research

  • Understand the API being called and the structure of responses
  • Ask the user for example API responses if not provided
  • Fetch relevant documentation from MCP tools or reference files
  • DO NOT write any code until research is complete

Step 2: Implement

  • Create the schema using the template below
  • Follow the grammar, methods, and variables in the reference files
  • Ask clarifying questions if unsure about requirements

Step 3: Validate (Compose)

  • Run rover supergraph compose --config ./supergraph.yaml
  • Fix any composition errors before proceeding

Step 4: Execute

  • Run rover connector run --schema <file> -c "<Type.field>" -v "{}"
  • Verify the connector executes correctly

Step 5: Test

  • Create or update test files under /tests/
  • Run rover connector test
  • Ensure full test coverage for each connector

Schema Template

graphql
# Note to AI Friends: This is an Apollo Connectors schema. You should use your connectors-spec MCP tool if you have it available before editing this file.

extend schema
  @link(url: "https://specs.apollo.dev/federation/v2.12")
  @link(url: "https://specs.apollo.dev/connect/v0.3", import: ["@source", "@connect"])

@source(name: "api_name", http: { baseURL: "https://api.example.com" })

type Query {
  example(id: ID!): Example
    @connect(
      source: "api_name"
      http: { GET: "/example/{$args.id}" }
      selection: """
      id
      name
      """
    )
}

type Example {
  id: ID!
  name: String
}

Version Requirements: Always use federation/v2.12 and connect/v0.3 unless specified otherwise.

Reference Files

Before implementing connectors, read the relevant reference files:

Key Rules

Selection Mapping

  • Prefer sub-selections over ->map for cleaner mappings
  • Do NOT use $ when selecting fields directly from root
  • Field aliasing: newName: originalField (only when renaming)
  • Sub-selection: fieldName { ... } (to map nested content)
# DO - Direct sub-selection for arrays
$.results {
  firstName: name.first
  lastName: name.last
}

# DO NOT - Unnecessary root $
$ {
  id
  name
}

# DO - Direct field selection
id
name

Entities

  • Add @connect on a type to make it an entity (no @key needed)
  • Create entity stubs in parent selections: user: { id: userId }
  • When you see an ID field (e.g., productId), create an entity relationship
  • Each entity should have ONE authoritative subgraph with @connect

Literal Values

Use $() wrapper for literal values in mappings:

$(1)              # number
$(true)           # boolean
$("hello")        # string
$({"a": "b"})     # object

# In body
body: "$({ a: $args.a })"  # CORRECT
body: "{ a: $args.a }"     # WRONG - will not compose

Headers

graphql
http: {
  GET: "/api"
  headers: [
    { name: "Authorization", value: "Bearer {$env.API_KEY}" },
    { name: "X-Forwarded", from: "x-client" }
  ]
}

Batching

Convert N+1 patterns using $batch:

graphql
type Product @connect(
  source: "api"
  http: {
    POST: "/batch"
    body: "ids: $batch.id"
  }
  selection: "id name"
) {
  id: ID!
  name: String
}

Ground Rules

  • NEVER make up syntax or directive values not in this specification
  • NEVER use --elv2-license accept (for humans only)
  • ALWAYS ask for example API responses before writing code
  • ALWAYS validate with rover supergraph compose after changes
  • ALWAYS create entity relationships when you see ID fields
  • Prefer $env over $config for environment variables
  • Use rover dev for running Apollo Router locally

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Apollo Connectors AI skill do?

Guide for integrating REST APIs into GraphQL supergraphs using Apollo Connectors with @source and @connect directives. Use this skill when the user: (1) mentions "connectors", "Apollo Connectors", or "REST Connector", (2) wants to integrate a REST API into GraphQL, (3) references @source or @connect directives, (4) works with files containing "# Note to AI Friends: This is an Apollo Connectors schema".

Why use Apollo Connectors on TypingMind?

Because you install it once and use it with any model. Apollo Connectors is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Apollo Connectors in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/apollographql/skills/tree/main/skills/apollo-connectors. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Apollo Connectors?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Apollo Connectors?

As many as you like. As long as a model supports skills, you can use Apollo Connectors with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Apollo Connectors AI skill free?

Yes. It is published on GitHub by apollographql under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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