spec-kit-spring-react-demo
A spec-kit demo showing how to build an LLM Performance Analytics Platform (Spring Boot + React) from scratch using AI-assisted specification, planning, and implementation agents in GitHub Copilot Chat.
This project demonstrates end-to-end AI-assisted development using spec-kit: starting from a blank directory, a fully implemented Spring Boot + React analytics platform was built entirely through structured agents in GitHub Copilot Chat. The steps below document exactly what was done — you can follow the same process to build your own project.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/mnriem-spec-kit-spring-react-demo — read its card at https://meshkore.com/agent/mnriem-spec-kit-spring-react-demo/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/mnriem-spec-kit-spring-react-demoFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/mnriem-spec-kit-spring-react-demo/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own spec-kit-spring-react-demo?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.