القائمة
Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Scaffold a Quarto analysis notebook (generic or method-specific — DiD, IV, RDD, LASSO, Panel FE), register it in _quarto.yml, apply publication-quality figure styling, and generate dataset codebooks. Use when starting a new analysis notebook.
Manage the project's literature workflow — ideate research questions, run structured literature reviews, add citations to references.bib, write annotation notes in references/, and audit citation integrity. Use for any bibliography or literature task.
Audit a Quarto manuscript or a single notebook — freeze freshness, data-path existence, citation integrity, figure/table export presence, placeholder and anonymization checks. Produces a scored review report under notes/. Read-only — never modifies notebooks, data, or the manuscript.
Build publication-quality regression and robustness tables from notebook estimation output, exported to tables/ as CSV + Markdown + LaTeX. Use when creating a results table.
Draft and revise manuscript prose for index.qmd — sections, the abstract, regression-result interpretation, and referee responses. Use when writing or revising the manuscript.
Reads the manuscript and notebooks to generate a structured abstract. Use when writing or updating the abstract.
| name | codebook |
| description | Auto-generates a Markdown codebook from a dataset (CSV, DTA, Excel, Parquet) with types and summary statistics. Use when documenting variables. |
| argument-hint | <path to dataset> |
| allowed-tools | Bash, Read, Write, Glob, Grep |
| version | 1.0.0 |
| workflow_stage | data |
| tags | ["data","documentation","codebook"] |
Auto-generate a Markdown codebook documenting all variables in a dataset.
$ARGUMENTS — path to a dataset file (e.g., data/rawData/sample_data.csv, data/panel.dta)Determine the file format from the extension:
.csv — read with pandas read_csv.dta — read with pandas read_stata.xlsx / .xls — read with pandas read_excel.parquet — read with pandas read_parquetLoad the dataset using uv run python and extract metadata for each variable:
Generate a Markdown codebook with:
[FILL: description] placeholder for the user to add a human-readable descriptionDerive the output filename from the dataset name:
data/rawData/sample_data.csv → references/sample-data-codebook.mdSave to references/<dataset-name>-codebook.md
Report the file path and the number of variables documented.