Snaply

Snaply is an open-source, in-browser image toolkit. Every tool runs entirely client-side: no uploads, no accounts, no server round-trips.…

A new way for AI to search for data
View on GitHub
2GitHub stars

TypeScript

What XERJ indexed

Snapshot

Whole repository

The whole repository at commit 782f62285d (2026-09-27).

Selected work

Tanbir
@tanbiralam

“Add favicon images and site manifest for Snaply”. 19 files, 1,587 lines in src/app, src/components, snaply-favicon-package.

Licence

The repository

MIT

Checked by a person at the pinned commit.

The XERJ corpus

MIT, with credit

The author agreed by email on 2026-10-23. Same terms as the repository. Answers name the repo, the commit and the file:line.

in licence reviewnot in the hub yetsuggested use: adapt with attribution

Example questions

The prompt

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

Search the GitHub repository tanbiralam/Snaply with XERJ (https://hub.xerj.org/corpus/tanbiralam--snaply).
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/tanbiralam/Snaply Snaply && git -C Snaply checkout 782f62285d25613006cb440c0cb5a53f3fbfa295
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./Snaply --url http://127.0.0.1:9200 --prefix snaply --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.