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Produce a publication-ready summary-statistics table from a CSV or in-memory dataframe. Triggers when the user asks for "summary stats", "describe data", or uploads a *.csv with numeric columns and asks for a table.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Produce a publication-ready correlation heatmap (PDF+PNG) from a CSV or in-memory dataframe. Triggers when the user asks for "correlation heatmap", "corr matrix figure", or uploads a *.csv with numeric columns and asks for a heatmap.
Read a corpus of the user's published papers (PDFs) and produce a VOICE.md style guide capturing recurring phrasing, sentence rhythm, hedging style, structural moves, and a ban list. Triggers when the user asks to "extract my voice", "build a voice file from these papers", or runs /voice-extractor.
Produce a multi-perspective referee report on a working paper PDF. Spawns three reviewer subagents (empirical, theory, methods), then consolidates their memos into a single editor-facing report. Triggers on commands like /referee-report or when the user uploads a PDF and asks for a "referee" / "review".
Create a rigorous, dependency-mapped revision master plan from peer review reports with computational DAG validation using NetworkX. Use when academic authors receive reviewer comments (R&R, revise-and-resubmit) and need a coherent revision strategy with validated dependency graphs, parallel execution schedules, critical path analysis, and bottleneck identification. Triggers include requests to analyze reviewer comments with DAG validation, plan a revision with dependency checking, create a computationally validated revision roadmap, validate revision dependencies, or identify critical paths in a revision plan. Designed for empirical research papers in accounting, finance, economics, management, and information systems.
Stage 3. Emit a LaTeX table of the regression coefficients. Triggers when state.stage == "regressed".
Stage 1 of the pipeline. Fetch a small CRSP return panel from WRDS and write it to data/returns.csv. Update state.json. Verify schema. Triggers when state.stage == "init".
| name | edhec-summary-stats |
| description | Produce a publication-ready summary-statistics table from a CSV or in-memory dataframe. Triggers when the user asks for "summary stats", "describe data", or uploads a *.csv with numeric columns and asks for a table. |
| args | {"input":"path to CSV or name of in-memory dataframe","output":"optional path for the LaTeX table (default tables/sumstats.tex)","cluster":"optional clustering for SE (e.g. \"permno,ym\")"} |
A four-phase skill. Each phase ends with a checkpoint the user can interrupt.
Read the input. Identify numeric columns. For each column report:
Stop and surface anything with > 5% missing or with values outside plausible bounds (returns > 1 in absolute value, ratios outside [-100, 100], etc).
Print the column list you intend to include and the statistics you intend to report:
cluster arg is set and the data is panel-shaped, add clustered SE on the meanIf the proposed table would have > 20 rows, stop and ask for confirmation.
Compute the table. Use:
pandas.DataFrame.describe() for the basic momentsstatsmodels for clustered SEs if requestedEmit a LaTeX table:
booktabs rules\multicolumn{1}{c}{...}output path (default tables/sumstats.tex)