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Stage 4 (final). Write a 1-paragraph natural-language summary of the regression results. Triggers when state.stage == "tabulated".
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
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.