CRUX-Compress
Compress verbose LLM rules while retaining self-contained meaning and intent.
AI coding assistants like Cursor rely on context windows to understand your project. When you add natural language markdown rules to guide agent behavior, those rules consume valuable context tokens—often thousands of tokens per rule file. As your rule library grows, context window usage balloons, leaving less room for actual code and conversation.
⚡ 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/zotoio-crux-compress — read its card at https://meshkore.com/agent/zotoio-crux-compress/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/zotoio-crux-compressFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/zotoio-crux-compress/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own CRUX-Compress?
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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