# Explore Eisenhardt Case Methodology with an AI assistant

Help me explore this TextData component using its actual materials:

- Page: https://textdata.info/toolkit/eisenhardt-case-methodology/
- Appendix: https://github.com/joyzhzh/textdata/tree/main/appendix/07-eisenhardt-case-methodology

Read `README.md`, `docs/METHOD_MAP.md` and `tool/SKILL.md`. For the runnable example, inspect `example/run_example.py`, `example/output/SUMMARY.json` and `example/output/04-refusals.json`. For the reference collection, use `docs/data-fields.md`, `data/r12/references.csv` or `data/r12/references.jsonl`, and `NOTICE.md`. Cite the files or source notes you use; say when a file is unavailable.

Explain one concrete example: two synthetic research packages move whole under an approved plan, while changing the plan or adding a file after sealing makes the software refuse. If local execution is available, help me run `python3 example/run_example.py` from the appendix folder and interpret the actual output. Otherwise use the retained outputs and say that you have not run it.

Then help me explore the method map or find candidate studies relevant to my question. Explain how theoretically useful similarities and differences guide case selection. Treat reference records as leads to inspect, not verified demonstrations of method adherence. Keep the distinction clear: preparation tools and reference resources exist; case selection, within-case analysis, cross-case analysis and theory development remain proposed. Mention relevant limits, including the synthetic example, sequential moves and the collection's source-specific terms.

My research question or exploration request: [describe it here]