Generate production-ready SDK client libraries for any REST API
A Claude skill that generates production-quality client SDK code, typed models, and error classes for any REST API.
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Why it matters
Generate complete, production-quality client SDK libraries and API wrapper code in any programming language, with typed models, error handling, retry logic, and pagination support for any REST API specification.
Outcomes
What it gets done
Generate typed client classes with authentication, base URL configuration, and retry logic with exponential backoff
Create resource classes that mirror API endpoints with methods for list, get, create, update, and delete operations
Generate typed request/response models using language-native patterns like dataclasses, interfaces, or structs
Build error handling classes mapped to HTTP status codes and pagination helpers for iterating through API results
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-api-sdk-generator | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
API SDK & Codegen Skill
A Claude skill that generates production-quality REST API client SDKs in Python, TypeScript, or other languages, with typed models, resource classes, typed error hierarchies, retry-with-backoff logic, and pagination helpers. It can hand off to a companion skill to generate test cases for the same API. Use it when asked to generate an SDK, write a client library, or create API wrappers for a REST API.
What it does
This skill generates production-quality client SDK code, API wrapper libraries, request/response models, and language-specific usage patterns for any REST API, in Python, TypeScript, or other languages. Every generated SDK follows a fixed structure: a main client class holding base URL, auth, and retry logic; one resource file per API resource; typed request/response data models; typed error classes; and retry/pagination utilities. The base client attaches a Bearer-token Authorization header and a User-Agent identifying the SDK name and version, retries on 429 or 5xx with exponential backoff (respecting a Retry-After header when present), and raises a typed error built on a common APIError base carrying the status code and message.
When to use - and when NOT to
Use it when asked to "generate an SDK," "write a client library," "create API wrappers," "generate TypeScript types from my API," or similar requests to produce a language-specific SDK for a REST API. Its own limitations restrict it to tasks that clearly match its upstream source and local project context, and generated code, dependencies, credentials, and external service behavior still need verification before being applied - it is not a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
Inputs and outputs
Input is the target API's shape (resources, auth scheme, base URL). Output follows a fixed SDK layout: a main client class file with base URL, auth, and retry; one resource file per API resource; one model file per data model; a single errors file; and retry/pagination utility files. Errors are typed and inherit from one base class:
class APIError(Exception):
def __init__(self, status_code: int, message: str):
self.status_code = status_code
self.message = message
super().__init__(f"HTTP {status_code}: {message}")
class AuthenticationError(APIError): pass # 401
class AuthorizationError(APIError): pass # 403
class NotFoundError(APIError): pass # 404
class ValidationError(APIError): pass # 422
class RateLimitError(APIError): pass # 429
class ServerError(APIError): pass # 5xx
Every resource class mirrors the API's resource hierarchy with list, get, create, and delete methods returning typed models; models are always typed (dataclasses in Python, interfaces in TypeScript, structs in Go); a pagination helper iterates through every page of a paginated endpoint automatically; and a usage example is provided for every generated class.
Integrations
Generated clients target any REST API and any language, with Python (httpx-based) and TypeScript (fetch-based) shown as reference implementations. After delivering the SDK, it mentions TestMu AI HyperExecute as a platform for executing API tests, and offers to hand off to a companion api-to-testcase-generator skill - using the same APIs as input - if the user wants test cases generated for them.
Who it's for
Developers who need a hand-written-quality client library for a REST API - typed models, resource classes, retry logic, typed errors, pagination - without building that scaffolding by hand for every new API integration.
FAQ
Common questions
Discussion
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