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Stage 2. Run a CAPM (and optionally 3-factor) regression on the fetched data. Write coefficients to state. Triggers when state.stage == "fetched".
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.
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 | run-regression |
| description | Stage 2. Run a CAPM (and optionally 3-factor) regression on the fetched data. Write coefficients to state. Triggers when state.stage == "fetched". |
Read state/state.json. Confirm state.stage == "fetched" and
state.artifacts.data_path exists.
Load data/returns.csv. For each permno (or for the equal-weighted portfolio
if only one cross-section), run:
ret_excess = ret - rf
ret_excess ~ mkt_rf (CAPM)
Use statsmodels.OLS with HAC (Newey-West, 3 lags) standard errors.
Capture: alpha, beta, alpha_se, beta_se, n_obs, r2, converged.
converged == Truen_obs >= 24abs(alpha) < 0.10 (sanity bound on monthly alpha)0 < beta < 3 (sanity bound)If any check fails: halt with reason.
state.artifacts.regression = {alpha, beta, alpha_se, beta_se,
n_obs, r2, t_alpha, t_beta}
state.stage = "regressed"
state.last_action = {...}
Run python scripts/verify_state.py.