hammerstein
A strategic-reasoning AI tuned to the Hammerstein framework. Portable system prompt + retrieval. The framework survives the model.
Most AI assistants are eager to please. They agree with you and tell you your plan is great. Hammerstein is a method for getting the opposite: an AI that pushes back. It refuses plans that are wasteful or dishonest, points out the weak spots in an idea, and when it says no it tells you what would turn that no into a yes. The goal is an assistant that helps you think, not one that flatters you. It works the same way whichever AI is running underneath.
⚡ 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/lerugray-hammerstein — read its card at https://meshkore.com/agent/lerugray-hammerstein/.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/lerugray-hammersteinFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/lerugray-hammerstein/.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 hammerstein?
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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