Ravi Kiran Pagidi

What an agent can do with the great-generator corpus

GreatDataLabs/great-generator: Generate realistic Synthetic enterprise data for Spark, Pandas, testing, demos, benchmarking, learning, and research

• Look up how great-generator implements benchmarking, data-engineering, data-generation, with answers cited to file and line.
• Reuse a worked example: the commit “Add workflow agent skills and validated Databricks examples” (18 files · Python).
• Answer from great-generator's real Python source instead of memory (API design and specification).

• Where does great-generator handle “Add workflow agent skills and validated Databricks examples”?
• How does great-generator implement benchmarking?
• What does skills do in great-generator?

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.

corpus greatdatalabs--great-generator · API design and specification
Related corpora: distilabel, ann-benchmarks, lakehq/sail, MuteJester/GenAIRR
Search an OpenAPI spec from an agent · Case study: reference coding for agents