run-train

作成者： lllllllama

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

npx skills add https://github.com/lllllllama/rigorpilot-skills --skill run-train

ZIPダウンロード GitHub

run-train

Use this as the Rigor Train skill. The installed slug remains run-train for compatibility.

Use the shared operating principles in ../../references/agent-operating-principles.md; this skill should keep training evidence bounded while leaving repository-specific monitoring details to the model.

When to apply

When the training command has already been selected and should be executed conservatively.
When the researcher wants startup verification, short-run verification, full training kickoff, or resume handling.
When the run needs structured training status, checkpoint, and metric reporting.

When not to apply

When the main task is environment setup or asset download.
When the researcher wants inference-only or evaluation-only execution.
When the task is speculative exploration, multi-variant sweeps, or autonomous idea implementation.
When the user still needs repository intake or paper gap resolution.

Clear boundaries

This skill executes a selected training command and normalizes the resulting evidence.
It does not choose the overall research goal on its own.
It does not own exploratory branching or speculative code adaptation.
It should record partial, blocked, resumed, and kicked-off states clearly.
It should preserve reproducibility context such as configs, seeds, checkpoints, logs, metrics, and runtime assumptions when available.

Input expectations

selected training goal
runnable training command
environment and asset assumptions
run mode such as startup verification, short-run verification, full kickoff, or resume

Output expectations

train_outputs/SUMMARY.md
train_outputs/COMMANDS.md
train_outputs/LOG.md
train_outputs/SCIENTIFIC_CHANGELOG.md
train_outputs/COMPARABILITY_REPORT.md
train_outputs/status.json

Notes

Use references/training-policy.md, ../../references/deep-learning-experiment-principles.md, scripts/run_training.py, and scripts/write_outputs.py.

lllllllamaのその他のスキル

ai-research-explore

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding,...

researchdata-analysisapi

analyze-project

Rigor Analyze / Rigor Auditの読み取り専用スキル。深層学習研究リポジトリ向け。ユーザーがリポジトリを読み解き、モデル構造や学習・推論のエントリポイントを調査し、設定や挿入箇所を確認し、コードを変更したり重いジョブを実行せずに不審な実装パターンを指摘したい場合に使用する。アクティブなコマンド実行、大規模なリファクタリング、推測的なコード適応、自動バグ修正には使用しない。

developmentcode-reviewresearch

ai-research-reproduction

RigorPilotの再現モードオーケストレーター。READMEファーストの深層学習リポジトリ再現用。ユーザーがエンドツーエンドで最小限の信頼性のあるフローを求める場合に使用。リポジトリを最初に読み、最小の文書化された推論または評価ターゲットを選択し、取り込み、セットアップ、信頼できる実行、オプションの信頼できるトレーニング、オプションのリポジトリ分析、オプションの論文ギャップ解決を調整し、保守的なパッチルールを適用し、証拠の前提の逸脱と人間の判断ポイントを記録する。

researchdevelopmentdocument

Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted...

developmentresearchcode-review

minimal-run-and-audit

Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

developmenttestingcode-review

env-and-assets-bootstrap

README優先の深層学習リポジトリ再現のためのRigor Setupスキル。READMEにドキュメント化されたリポジトリで実行前に、保守的なconda優先環境、チェックポイントとデータセットのパス前提、キャッシュ場所のヒント、セットアップノートを準備するタスクに使用する。リポジトリスキャン、完全なオーケストレーション、論文解釈、最終実行レポート、特定の再現対象に紐づかない汎用的な環境セットアップには使用しない。

developmentdevops

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline...

researchdevelopmentdata-analysis

ディープラーニング研究作業のためのRigor Debug / Rigor Auditスキル。ユーザーがトレースバック、ターミナルエラー、CUDA OOM、チェックポイント読み込み失敗、形状不一致、NaN損失症状、またはトレーニング失敗を貼り付け、パッチ適用前に保守的な診断を希望し、デバッグ修正が研究貢献と明確に分離されている場合に使用します。広範なリファクタリング、推測的な適応、自動探索的パッチ適用、または一般的なリポジトリの習熟には使用しないでください。

developmenttestingcode-review