great-generator
Generate realistic Synthetic enterprise data for Spark, Pandas, testing, demos, benchmarking, learning, and research
What XERJ indexed
Snapshot
The whole repository at commit 8e41505229 (2026-10-25). main is 5 commits ahead.
Selected work
“Add workflow agent skills and validated Databricks examples”. 18 files, 1,617 lines in skills, tests, scripts.
Licence
The repository
LICENSE file checked at the pinned commit; the author agreed to inclusion.
The XERJ corpus
The author agreed by email on 2026-10-25. 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
benchmarkingdata-engineeringdata-generationdata-generatoretl-testingfakerpandaspyspark
Example questions
- How does great-generator implement benchmarking?
- Where does great-generator handle “Add workflow agent skills and validated Databricks examples”?
- What does skills do in great-generator?
Inside the indexed corpus
- 406 files, 2.2 MB in total, about 5.7 KB per file.
- The selected commit changed 18 of those files (4.4%).
- Languages by size: Python 99.4%, Jupyter Notebook 0.6%, HTML 0%.
- On GitHub since 2026-05-17, last push 2026-10-26, 60 commits on main.
- The pin is 5 commits behind main; the corpus answers from the pinned code, not the newest.
Licence on GitHub and in the corpus
GitHub reported MIT for GreatDataLabs/great-generator on 2026-10-31, and the LICENSE file at commit 8e41505229 was reviewed as MIT. They match, so answers from this corpus carry the same terms as the live repository.
Contributors
Ravi Kiran Pagidi (@ravikiranpagidi) has 1 selected repository on the hub.
Using XERJ with great-generator
- Search Jupyter notebooks (.ipynb) and the .py beside them Jupyter Notebook is 0.6% of the code by size.
- Search an OpenAPI spec so an agent can find endpoints The hub files it under API design and specification.
- ripgrep vs a code index for coding agents: 30 tasks
- Best MCP servers for local code search in 2026, compared
The prompt
Paste it into your AI agent. It clones GreatDataLabs/great-generator at the pinned commit and indexes it on your machine.
Search the GitHub repository GreatDataLabs/great-generator with XERJ (https://hub.xerj.org/corpus/greatdatalabs--great-generator). 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/GreatDataLabs/great-generator great-generator && git -C great-generator checkout 8e41505229a3031266db2c3ab6343e4b0b1ea59f 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./great-generator --url http://127.0.0.1:9200 --prefix great-generator --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
Ravi Kiran PagidiAPI design and specification corporadistilabelann-benchmarksfortiolakehq/sailSoheilGtex/finance-data-pipelinesmatteocavo/Quaero
Stats: GitHub API, fetched 2026-10-31. Snapshot: the git tree at the pinned commit. Selected work and consent: the XERJ registry.