By tonone-ai
Design a mock API server with tooling selection, response fixtures, and error scenario coverage for frontend, mobile, or test development
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin mock-designDeveloper Experience Team — Mock: API mocking — mock server design, contract testing, API simulation for development
Create mock API servers from OpenAPI specs for testing
API testing automation, request mocking, OpenAPI documentation generation, observability setup, and monitoring
Use this agent when you need to design and implement internal API architecture, developer experience, and API infrastructure for B2B applications. This agent specializes in REST API design, GraphQL implementation, API documentation, SDK development, and developer portal creation. Handles API performance optimization, versioning strategies, and internal service communication. Examples:
Connector - Production-ready API integration specialist. Orchestrates 10 agents to generate typed clients, auth flows, error handling, rate limiting, and mock servers from OpenAPI/GraphQL specs. Delivers enterprise-grade API integrations, not basic fetch wrappers.
API design, documentation, and testing with OpenAPI spec generation
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".