Generate comprehensive API documentation including endpoint descriptions, request/response examples, authentication guides, error codes, and SDKs. Creates OpenAPI/Swagger specs, REST API docs, and developer-friendly reference materials. Use when users need to document APIs, create technical references, or write developer documentation.
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See how to installReview the instructions the agent will receive before you install.
Generate comprehensive, developer-friendly API documentation.
references/documentation-structure.md — every section to cover (overview, auth, endpoints, errors, rate limits, SDKs, webhooks, GraphQL)references/output-template.md — canonical REST Markdown template with worked examplesreferences/best-practices.md — best practices, developer-experience tips, and the output quality checklistGather API information. Determine the API type (REST, GraphQL, WebSocket, gRPC), authentication method (API key, OAuth, JWT), base URL and versioning strategy, available endpoints and their purposes, request/response formats, and any rate limiting or usage restrictions.
Build the documentation structure. Cover every section in references/documentation-structure.md, ordering the most common operations first.
Generate the output. Follow references/output-template.md for REST APIs; adapt to schema, query, mutation, and subscription examples for GraphQL. Replace all placeholders with realistic example data and show both request and response.
Document errors and rate limits. Include the standard error response format, common error codes, troubleshooting guidance, limits, headers to check, and how to handle 429 responses.
Provide code samples in multiple languages (curl, JavaScript, Python) and link SDKs, Postman collections, or OpenAPI specs where available.
Verify quality against the checklist in references/best-practices.md before delivering.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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