Developer Experience and Education
Build credible integrations, examples, and reusable agent packages, then turn developer friction into clearer tools and teaching.
#LessonPhaseTimeRequirementOpen
01APIs & KeysEvery AI API works the same way: send a request, get a response. The details change, the pattern doesn't.Phase 0030 minRequired▸ Visual story02The Tool Interface — Why Agents Need Structured I/OA language model produces tokens. A program takes actions. The gap between those two is the tool interface: a contract that lets the model request an action and the host execute it. Every 2026 stack — function calling on OpenAI, Anthropic, and Gemini; MCP's `tools/call`; A2A's task parts — is a different encoding of the same four-step loop. This lesson names the loop and shows the minimum machinery to run it.Phase 1345 minRequired↗ Official lesson03Tool Schema Design — Naming, Descriptions, Parameter ConstraintsA correct tool fails silently when the model cannot tell when to use it. Naming, descriptions, and parameter shapes drive 10 to 20 percentage-point swings in tool-selection accuracy on benchmarks like StableToolBench and MCPToolBench++. This lesson names the design rules that separate a tool a model picks reliably from a tool a model mis-fires.Phase 1345 minRequired↗ Official lesson04MCP Fundamentals: Stateless Requests and JSON-RPCModern MCP has no handshake and no protocol session. Each request must carry enough metadata to be understood, authorized, routed, and retried on its own.Phase 1355 minRequired↗ Official lesson05Building an MCP Server: Stateless Python and TypeScriptA modern MCP server does not remember a handshake. It validates the metadata on every request, runs one handler, and returns one typed result.Phase 1385 minRequired↗ Official lesson06Agent Skills: Portable Contract and Runtime BoundaryA skill is not a long prompt with a better filename. It is a discoverable package of instructions, resources, and executable helpers that enters an agent's context through a runtime contract.Phase 1390 minRequired↗ Official lesson07Skill Discovery and Progressive DisclosureA skill becomes useful before its body is loaded. Its name and description earn a place in the catalog; its deeper files earn context only when the task reaches them.Phase 13105 minRequired↗ Official lesson08Skill Invocation and RoutingInvocation is an authority decision followed by a relevance decision. A good description helps the model choose; a good policy decides whether that choice is allowed.Phase 13105 minRequired↗ Official lesson09Skill Evals, Packaging, and PortabilityA skill is finished when its package survives linting, routes on the right requests, improves a measured task, stays inside policy, and degrades honestly on another host.Phase 13150 minRequired↗ Official lesson10Capstone: Stateless Tool EcosystemA production agent system is a set of boundaries, not a pile of features. This capstone separates a readable in-process simulation from the protocol clients, authorization server, sandbox, and telemetry exporter a real deployment still needs.Phase 13120 minRequired↗ Official lesson11Build a Feedback Ratchet with Ownership and RetirementShipping closes one build loop and opens the learning loop. Evidence must change the system or it becomes telemetry nobody owns.Phase 1475 minRequired↗ Official lesson