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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 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.
| name | stock-pool-oos-evaluation-plan |
| description | 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. |
Prepare OOS evaluation without executing it.
Require:
Resolve production defaults from G:/My Drive/AI-Stock-Sync/config.json and production.strategy_groups when the user asks for production parity.
Use evaluation-batch-generator when jobs are long, multi-fold, or need worker planning. Preserve its approval boundary:
tmp/evaluationtmp.json.For a small direct command plan, use evaluate with explicit universe files:
uv run python main.py evaluate `
--mode annual `
--years <TEST_YEARS> `
--entry-strategies <ENTRY_STRATEGY> `
--exit-strategies <EXIT_STRATEGY> `
--ranking-strategies <RANKING_STRATEGY> `
--ranking-mode prs_train `
--entry-filter-mode <ENTRY_FILTER_MODE> `
--universe-file <CURRENT_POOL> <FULL_POOL> <SELECTED_POOL> `
--output-dir "G:/My Drive/AI-Stock-Sync/strategy_evaluation"
Use explicit --custom-periods or start/end capable tooling if the OOS fold is a partial year and annual --years would include unwanted dates.
Return:
Do not run the manifest or evaluation command in this skill.