Wendy Yun
Wendy Yun's work was selected for the XERJ Corpus Hub, the open reference memory AI coding agents search before they write code. When an agent meets the same problem, it gets this implementation back as a worked example, with the author and the repository attached.
Totals across every corpus @zyziyun appears in; a commit selected for two corpora counts once. Files and lines count the selected commits; each corpus indexes the whole repository at its pinned commit.
What an agent can do with the reelfold corpus
zyziyun/reelfold: Open-source creator workbench: turn footage or notes into batches of videos, captions and covers for every platform. Local editing on…
- Look up how reelfold implements ai-video, batch-processing, claude-code, with answers cited to file and line.
- Reuse a worked example: the commit “delete a series and its generated files” (6 files · Python, TypeScript).
- Answer from reelfold's real Python source instead of memory (AI, prompts and agents).
- Where does reelfold handle “delete a series and its generated files”?
- How does reelfold implement ai-video?
- What does apps/desk do in reelfold?
Search the GitHub repository zyziyun/reelfold with XERJ (https://hub.xerj.org/corpus/zyziyun--reelfold). 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/zyziyun/reelfold reelfold && git -C reelfold checkout aabcc58065eceb8e1cda8c829e131da9d7bfe7ab 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./reelfold --url http://127.0.0.1:9200 --prefix reelfold --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.
corpus zyziyun--reelfold · AI, prompts and agents
Related corpora: xerj-blogposts, ehzawad/codex-council, HuangYincan/VideoNote-MCP
AI search and retrieval with XERJ · Case study: reference coding for agents
Selected work
zyziyun/reelfold
The corpus is the whole repository at the pinned commit, not only the files of the selected commit.
Not installable yet: this corpus is not in the hub registry until its licence review passes, so xerj corpus add and xerj ask cannot fetch it. It becomes installable when this card says live in the hub.
Sign in with GitHub or Hugging Face to see more: every selected commit in full (commit date, every folder, licence review state, pin) and the pack builds of each corpus.
[](https://hub.xerj.org/contributors/zyziyun)
Included with the author's permission (email, 2026-10-23), under each repository's own licence, with attribution. Want it removed? Reply to the email or open an issue on xerj-org/xerj.