ablation-plannerlisted

> **Fallback（本整合包补充）**：若 `mcp__codex__codex` 不可用，跳过外部跨模型评审步骤并在产物中注明 "External review skipped"，或改按本包 `cross-model-review` 协议用零上下文��线程替代。本 skill 其余步骤照常执行，不得因缺少评审器而失败。 # Ablation Planner Systematically design ablation studies that answer the questions reviewers will ask. Codex leads the design (reviewer perspective), CC reviews feasibility and implements. ## Context: $ARGUMENTS ## When to Use - Main results pass `/result-to-claim` with claim_supported = yes or partial - User explicitly requests ablation planning - `/auto-review-loop` reviewer identifies missing ablations ## Workflow ### Step 1: Prepare Context CC reads available project files to build the full picture: - Method description and components (from `idea-stage/docs/research_contract.md`, legacy `docs/research_contract.md`, or project CLAUDE.md) - Current experiment results (from EXPERIMENT_LOG.md, EXPERIMENT_TRACKER.md, or W&B) - Confirmed and intended claims (from result-to-claim output or project notes) - Available compute resources (from CLAUDE.md server config, if present) ### Step 2: Codex Designs Ablations ``` mcp__codex__codex: model: gpt-5.6-sol config: {"model_reasoning_effort": "xhigh"} prompt: | You are a rigorous ML reviewer planning ablation studies. Given this method and results, design ablations that: 1. Isolate the contribution of each novel component 2. Answer questions reviewers will definitely ask 3. Test sensitivity to key hyperparameters 4. Compare agains