菜单
Use when reviewing completed J-stock custom stock-pool out-of-sample evaluations, comparing selected pools with current and full-pool controls, and auditing leakage or survivorship-bias risks.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when: generating worker-ready JSON manifests for J-stock evaluation, pos-evaluation, walk-forward-evaluate, or replay-evaluation batch execution. Resolve WebUI-equivalent defaults, split jobs by worker during generation, and stop before execution.
Use when: orchestrating an end-to-end J-stock batch evaluation workflow, or when the user explicitly names evaluation-batch-runner. Schedule evaluation-batch-generator, evaluation-batch-executor, and evaluation-result-summary in order, preserving the manifest approval boundary and allowing subagent delegation only during approved execution.
Use when: aggregating completed J-stock evaluation output directories into markdown summary reports under G:/My Drive/AI-Stock-Sync/summary without rerunning backtests, including global MDD-Win re-scoring via tools/score_evaluation_outputs.py when worker-local PRS is not comparable.
Use when generating training-window entry-signal-analysis datasets for a J-stock stock-pool experiment, especially scanning a broad/full universe with a fixed entry strategy before ticker-level pool selection.
Use when orchestrating a J-stock custom stock-pool experiment where a fixed entry condition is applied to a broad/full universe during a training window, good tickers are selected into one or more test universes, and those universes are evaluated out-of-sample against current and full-pool controls.
Use when generating approval-ready J-stock out-of-sample evaluation commands or batch manifests that compare trained selected stock pools against current production and broad/full universe controls.
| name | stock-pool-oos-review |
| description | Use when reviewing completed J-stock custom stock-pool out-of-sample evaluations, comparing selected pools with current and full-pool controls, and auditing leakage or survivorship-bias risks. |
Summarize completed OOS stock-pool experiments from machine-readable artifacts.
Prefer:
summary.json under tmp/parallel_eval/<run>/....G:/My Drive/AI-Stock-Sync/strategy_evaluation.Do not rely on terminal-rendered markdown for numeric comparisons when encoding is unreliable.
Compare each universe control on:
return_pct.topix_return_pct and alpha.sharpe_ratio.max_drawdown_pct.num_trades.win_rate_pct.Report both fold-level results and pooled OOS averages. Keep training metrics separate from OOS metrics.
Check and state:
train_end is strictly before OOS test_start.train_end.Treat a selected pool as promising only if:
Produce a concise markdown report, preferably under G:/My Drive/AI-Stock-Sync/summary, with: