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science-planning
Build unified science condition plans from validated prepare/code evidence
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。
メニュー
Build unified science condition plans from validated prepare/code evidence
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
Discover the concrete benchmark surfaces and entrypoints required for evaluation
Context-efficient file and terminal usage for experiment agents
Enable all required experiment conditions with runnable commands
Build an ordered executable code plan for controlled experiment enablement
Normalize idea components into explicit component-disable requirements
Create and verify the experiment runtime environment
| name | science-planning |
| description | Build unified science condition plans from validated prepare/code evidence |
| argument-hint | |
| allowed-tools | Bash(*), Read, Edit, Write, Glob, Grep, Agent |
| license | MIT |
Translate validated handoff artifacts into one ordered science condition plan. A condition is fully defined by enabled_components and disabled_components; the all-components condition is the reference case where disabled_components is empty.
science.plan under agent_reports/science/plan/latest.json.dataset_candidate/ data.results/science/<condition_id>/.1 + len(idea.json.components) conditions.idea.json.components enabled, disabled_components: [], and no reference_condition_id.agent_reports/science/.result, metric, value, confidence, analysis, and method_context for disabled-component conditions so final materialization can write ablation_results.json.enabled_components plus disabled_components must cover idea.json.components exactly for every condition.idea.json.components exactly.python project/....ablation_results.json; it is runtime-owned.