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Stage 4 (final). Write a 1-paragraph natural-language summary of the regression results. Triggers when state.stage == "tabulated".
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Basado en la clasificación ocupacional 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 3. Emit a LaTeX table of the regression coefficients. Triggers when state.stage == "regressed".
| name | write-summary |
| description | Stage 4 (final). Write a 1-paragraph natural-language summary of the regression results. Triggers when state.stage == "tabulated". |
Read state/state.json. Confirm state.stage == "tabulated" and
state.artifacts.regression is populated.
Ensure the output directory exists (mkdir -p summary), then generate a
1-paragraph summary at summary/summary.md. Format:
Over {n_obs} monthly observations, the portfolio earned a CAPM alpha of {alpha:.4f} ({t_alpha:.2f}) per month and a market beta of {beta:.4f} ({t_beta:.2f}). The R-squared was {r2:.3f}. Alpha [is / is not] statistically significant at the 5% level.
The "[is / is not]" decision must be made from the actual t-stat (|t| >= 1.96). Do NOT hedge. Do NOT add extra interpretation beyond what the numbers say.
Spawn the verifier subagent with the prompt: "Read summary/summary.md and state/state.json. Confirm every number in the summary matches state.artifacts.regression. Confirm the significance call matches the t-stat. Reply OK or list discrepancies."
If verifier returns anything other than OK: halt with the discrepancy as halt_reason.
state.artifacts.summary_path = "summary/summary.md"
state.stage = "done"
state.last_action = {...}
Run python scripts/verify_state.py. We are done.