qingdai3

A virtual planet in a binary star system

A new way for AI to search for data

View on GitHub

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What XERJ indexed

Snapshot

Whole repository

The whole repository at commit baf2d3533a.

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

The prompt

Paste it into your AI agent. It clones mountain/qingdai3 at the pinned commit and indexes it on your machine.

Search the GitHub repository mountain/qingdai3 with XERJ (https://hub.xerj.org/corpus/mountain--qingdai3).
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/qingdai3 qingdai3 && git -C qingdai3 checkout baf2d3533af984214799945c136fd632cc7e0412
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./qingdai3 --url http://127.0.0.1:9200 --prefix qingdai3 --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.

Mingli Yuan API design and specification corpora

Selected work and consent: the XERJ registry.