qingdai

一个刻画科幻世界“青黛”星球的基于物理的简化全球气候模型

A new way for AI to search for data
View on GitHub
4GitHub stars

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit 72b7af5fe5.

Selected work

Mingli Yuan
@mountain

Licence

The repository

MIT

LICENSE file checked at the pinned commit; the author agreed to inclusion.

The XERJ corpus

MIT, with credit

The owner added it on hub.xerj.org after signing in with GitHub, on 2026-11-10. Same terms as the repository. Answers name the repo, the commit and the file:line.

selected for the XERJ Corpus Hublicence clearedsuggested use: adapt with attribution

climateclimate-modelgcmscifi

Example questions

The prompt

Paste it into your AI agent. It clones mountain/qingdai at the pinned commit and indexes it on your machine.

Search the GitHub repository mountain/qingdai with XERJ (https://hub.xerj.org/corpus/mountain--qingdai).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/mountain/qingdai qingdai && git -C qingdai checkout 72b7af5fe59e7b52d45e8d68d7431a492b5acae9
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./qingdai --url http://127.0.0.1:9200 --prefix qingdai --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

Related

Selected work and consent: the XERJ registry.