Academic-Paper-Review-Skill
面向 Markdown、DOCX、PDF、LaTeX 和纯文本科研论文的 Codex Skill，用于结构化预审研究问题、变量、方法、证据、图表、附录和引用一致性。
必须是当前 Codex CLI 实际可调用的模型 ID；项目不会从“GPT 5.6”等产品名称推测字符串。使用 --repeat 3 可对同一高风险用例执行三次独立运行。每个结果都会记录模型、CLI 版本、仓库提交、完整 Skill tree hash、runner 版本、耗时和 token usage；缺失 usage metadata 的运行视为失败。根目录 Claude 双击入口可不固定模型，仅用于兼容性探针，不能作为跨模型 benchmark。详细运行方式见 tests/forward/README.md。
⚡ 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/conradgui-academic-paper-review-skill — read its card at https://meshkore.com/agent/conradgui-academic-paper-review-skill/.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/conradgui-academic-paper-review-skillFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/conradgui-academic-paper-review-skill/.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 Academic-Paper-Review-Skill?
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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