Upload 2 files

README.md

@@ -146,6 +146,8 @@ Each instance is a JSON object with the following fields:
 
 ## 💻 Usage
 
+### 1. Load the Dataset
+
 ```python
 from datasets import load_dataset
 
@@ -159,6 +161,18 @@ skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
 claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
 ```
 
+### 2. Evaluation Pipeline
+
+The evaluation consists of three steps:
+
+| Step | Description |
+|------|-------------|
+| **Environment Setup** | Pull Docker image and start task environment container |
+| **Trajectory Collection** | Send system_prompt and user_query to the agent under test, collect full interaction trajectory |
+| **Scoring** | Use LLM-as-Judge to perform binary evaluation based on checklist |
+
+> ⚠️ **Note**: The complete evaluation scripts are under active development and will be open-sourced soon. Stay tuned for updates.
+
 ## ⚖️ Evaluation Metrics
 
 | Metric | Definition | What it measures |

README_CN.md

@@ -146,6 +146,8 @@ docker run -it --rm minimaxai/feedfeed:<tag> /bin/bash
 
 ## 💻 使用方法
 
+### 1. 加载数据集
+
 ```python
 from datasets import load_dataset
 
@@ -159,6 +161,18 @@ skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
 claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
 ```
 
+### 2. 评测流程
+
+评测分为三个步骤：
+
+| 步骤 | 说明 |
+|------|------|
+| **环境准备** | 拉取 Docker 镜像，启动任务环境容器 |
+| **轨迹收集** | 将 system_prompt 和 user_query 发送给待测智能体，收集完整交互轨迹 |
+| **评分判定** | 基于 checklist 使用 LLM-as-Judge 对轨迹进行二元判定 |
+
+> ⚠️ **注意**：完整的评测脚本正在完善中，即将开源。敬请关注本仓库更新。
+
 ## ⚖️ 评估指标
 
 | 指标 | 定义 | 衡量内容 |