@neil9466/gpt-expert-mode
GPT 专家模式：让免费/低成本 LLM agent 调用 ChatGPT 网页版作为决策主脑，无需 API Key。基于 parley CDP 框架二次开发。Use ChatGPT (web) as the decision brain for low-cost LLM agents, no API key needed.
下图是一次真实协作：用户要求部署 Jenkins MCP，本地 agent（GLM-5.3）先梳理出"agent 能自动做什么 / 不能凭空做什么"的权限边界，然后通过 parley 把任务交给 GPT 决策。GPT 没有盲目下令开干，而是识别出信息缺口，反过来给出结构化选择题（Docker / CLI / kubectl / 浏览器 UI / 自定义），让 agent 先向用户确认部署方式——这正是"主脑"的价值：
⚡ 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/neil9466-neil9466gpt-expert-mode — read its card at https://meshkore.com/agent/neil9466-neil9466gpt-expert-mode/.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/neil9466-neil9466gpt-expert-modeFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/neil9466-neil9466gpt-expert-mode/.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 @neil9466/gpt-expert-mode?
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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