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Project directory organization and script naming conventions for research
Passed 24 of 26 test cases with the skill, and 13 of 26 without it.
and 15 more in the full report
11 fixed13 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
What three specific subdirectories should exist inside the Data/ directory to separate data stages from raw files to final datasets?
| Checked | Without skill | With skill |
|---|---|---|
| Names the unprocessed data folder Raw | Passed | Passed |
| Names the partially processed data folder Intermediate | Missed | Passed |
| Names the analysis-ready data folder Clean | Missed | Passed |
The instructions your model reads when the skill loads.
This document outlines the standardized structure for research project code organization. Use this as a template for organizing future research projects.
Project Root/
├── Code/ # All analysis scripts
│ ├── [Numbered]_[Descriptive].py # Data processing scripts (Python)
│ ├── AN_[Number]_[Descriptive].py # Analysis scripts (Python)
│ ├── AN_[Number]_[Descriptive].do # Analysis scripts (Stata)
│ ├── LogFiles/ # Stata log files
│ └── README.md # Project documentation
├── Data/ # All data files
│ ├── Raw/ # Original, unprocessed data
│ ├── Intermediate/ # Partially processed data
│ └── Clean/ # Final, analysis-ready datasets
└── Results/ # Analysis outputs
├── Tables/ # Regression tables, summary stats
└── Figures/ # Plots, charts, visualizations
Format: [Number]_[DescriptiveName].py
Examples:
0_ExtractCreditAgreements.py1a_CleanCompAnnualCRSP.py1b_ProcessDealscanCompustat.py2_MergeAgreementsCompCRSP.py3a_ExtractLoanOfficerNames.py4a1_ExtractLoanTerms.pytest cases passed, vs 13 / 26 without
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