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Stage 3. Emit a LaTeX table of the regression coefficients. Triggers when state.stage == "regressed".
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Basé sur la classification professionnelle SOC
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.
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.
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 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 | build-table |
| description | Stage 3. Emit a LaTeX table of the regression coefficients. Triggers when state.stage == "regressed". |
Read state/state.json. Confirm state.stage == "regressed" and
state.artifacts.regression is populated.
Ensure the output directory exists (mkdir -p tables), then emit a
booktabs-style LaTeX table to tables/main.tex:
\begin{tabular}{lcc}
\toprule
& Estimate & t-stat \\
\midrule
$\alpha$ (monthly) & {alpha} & ({t_alpha}) \\
$\beta_{MKT}$ & {beta} & ({t_beta}) \\
\midrule
$N$ & \multicolumn{2}{c}{ {n_obs} } \\
$R^2$ & \multicolumn{2}{c}{ {r2} } \\
\bottomrule
\end{tabular}
Round alpha and beta to 4 decimals, t-stats to 2.
\bottomrulepdflatex in a minimal scaffold (use scripts/check_table.tex)If verification fails: halt.
state.artifacts.table_path = "tables/main.tex"
state.stage = "tabulated"
Run python scripts/verify_state.py.