jobshoplab
This repository provides an OpenAI Gym-compatible environment for production scheduling tasks, designed to benchmark reinforcement learning agents in job shop and flow shop settings.
JobShopLab is a flexible and modular framework designed to advance research and development in job shop scheduling using Reinforcement Learning (RL) techniques. It provides an adaptable Gym environment, enabling users to test and benchmark different scheduling algorithms under realistic constraints found in industrial settings.
⚡ 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/proto-lab-ro-jobshoplab — read its card at https://meshkore.com/agent/proto-lab-ro-jobshoplab/.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/proto-lab-ro-jobshoplabFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/proto-lab-ro-jobshoplab/.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 jobshoplab?
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.
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