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science-planning
Build unified science condition plans from validated prepare/code evidence
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Build unified science condition plans from validated prepare/code evidence
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
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.