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README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - code
+  - agent
+  - benchmark
+  - evaluation
+pretty_name: OctoCodingBench
+size_categories:
+  - n<1K
+---
+
+# OctoCodingBench: Instruction-Following Benchmark for Coding Agents
+
+[English](README.md) | [中文](README_CN.md)
+
+## 🌟 Overview
+
+**OctoCodingBench** is a comprehensive benchmark for evaluating how well AI coding agents follow instructions from multiple sources. Unlike existing benchmarks that focus solely on task completion, OctoCodingBench systematically tests whether agents respect constraints from:
+
+- **System Prompts (SP)** — Role definitions, output formats, workflow rules
+- **System Reminders** — Behavior correction, tool usage reminders, information confidentiality
+- **User Queries** — Task requirements, multi-turn instruction changes  
+- **Project Documentation (Agents.md)** — Coding conventions from `CLAUDE.md`, `AGENTS.md`
+- **Skills** — Skill invocation workflows and protocols
+- **Memory** — User preferences and project context continuation
+- **Tool Schema** — Parameter correctness, call sequence, no hallucinated results
+
+## 🚀 Key Features
+
+- **Multi-Source Instruction Evaluation**: Tests agent compliance across 7 distinct instruction categories
+- **Checklist-Based Scoring**: Each instance includes a structured checklist with binary-decidable checks
+- **Real-World Scenarios**: Tasks derived from actual development workflows
+- **Multi-Scaffold Support**: Evaluated across Claude Code, Kilo, and Droid environments
+
+## 📦 Dataset Contents
+
+This release contains **72 curated instances** with:
+
+- Natural language task specifications
+- System prompts with behavioral constraints
+- Structured evaluation checklists (2,422 total check items)
+- Category and scaffold metadata
+
+## 📊 Dataset Statistics
+
+| Category | Instances |
+|----------|-----------|
+| Skill | 17 |
+| Claude.md | 15 |
+| AGENTS.md | 13 |
+| Memory | 12 |
+| System Prompt | 11 |
+| User Query | 4 |
+| **Total** | **72** |
+
+| Scaffold | Instances |
+|----------|-----------|
+| Claude Code | 54 |
+| Kilo | 11 |
+| Droid | 7 |
+
+| Metric | Value |
+|--------|-------|
+| Total check items | 2,422 |
+| Avg checks per instance | 33.6 |
+
+## 📝 Data Format
+
+Each instance is a JSON object with the following fields:
+
+```json
+{
+  "instance_id": "md-course-builder-conventional-commits",
+  "user_query": ["Implement the feature as specified..."],
+  "system_prompt": "You are a CLI assistant...",
+  "category": "Claude.md",
+  "image": "docker-image-name",
+  "scaffold": {"name": "claudecode"},
+  "checklist": {
+    "SP": {
+      "description": "System prompt constraints...",
+      "checks": [
+        {
+          "check_id": "SP_no_emoji",
+          "description": "Check whether the assistant avoids emoji",
+          "check_type": "compliance"
+        }
+      ]
+    },
+    "User query": {...}
+  }
+}
+```
+
+| Field | Description |
+|-------|-------------|
+| `instance_id` | Unique task identifier |
+| `user_query` | List of user messages (supports multi-turn) |
+| `system_prompt` | System-level behavioral constraints |
+| `category` | Primary instruction source being tested |
+| `image` | Docker image for task environment |
+| `scaffold` | Agent scaffold configuration |
+| `checklist` | Structured evaluation criteria |
+
+## 💻 Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("MiniMaxAI/OctoCodingBench")
+
+# Filter by category
+skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
+
+# Filter by scaffold
+claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
+```
+
+## ⚖️ Evaluation Metrics
+
+- **ISR (Instance Success Rate)**: 1 if all checks pass, 0 otherwise
+- **CSR (Checklist Success Rate)**: Proportion of passed checks
+
+## 📜 Citation
+
+```bibtex
+@misc{octocodingbench2026,
+  title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents},
+  author={MiniMax},
+  year={2026},
+  publisher={Hugging Face}
+}
+```

README_CN.md

@@ -0,0 +1,140 @@
+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - zh
+tags:
+  - code
+  - agent
+  - benchmark
+  - evaluation
+pretty_name: OctoCodingBench
+size_categories:
+  - n<1K
+---
+
+# OctoCodingBench: 编程智能体指令遵循基准
+
+[English](README.md) | [中文](README_CN.md)
+
+## 🌟 概览
+
+**OctoCodingBench**（智能体指令遵循基准）是一个全面评估 AI 编程智能体指令遵循能力的基准测试。与现有仅关注任务完成度的基准不同，OctoCodingBench 系统性地测试智能体是否遵循来自多个来源的约束：
+
+- **系统提示 (System Prompt)** — 角色定义、输出格式、工作流规则
+- **系统提醒 (System Reminder)** — 行为纠正、工具使用提醒、信息保密
+- **用户查询 (User Query)** — 任务需求、多轮指令变更
+- **项目文档 (Agents.md)** — 来自 `CLAUDE.md`、`AGENTS.md` 的编码规范
+- **技能 (Skill)** — 技能调用流程和协议
+- **记忆 (Memory)** — 用户偏好和项目上下文延续
+- **工具模式 (Tool Schema)** — 参数正确性、调用顺序、无幻觉结果
+
+## 🚀 核心特性
+
+- **多源指令评估**：测试智能体对 7 种不同指令类别的遵循程度
+- **检查清单评分**：每个实例包含结构化的二元判定检查项
+- **真实场景**：任务源自实际开发工作流
+- **多脚手架支持**：在 Claude Code、Kilo、Droid 环境中评估
+
+## 📦 数据集内容
+
+本次发布包含 **72 个精选实例**：
+
+- 自然语言任务规范
+- 带有行为约束的系统提示
+- 结构化评估检查清单（共 2,422 个检查项）
+- 类别和脚手架元数据
+
+## 📊 数据集统计
+
+| 类别 | 实例数 |
+|------|--------|
+| Skill| 17 |
+| Claude.md | 15 |
+| AGENTS.md | 13 |
+| Memory | 12 |
+| System Prompt | 11 |
+| User Query | 4 |
+| **总计** | **72** |
+
+| 脚手架 | 实例数 |
+|--------|--------|
+| Claude Code | 54 |
+| Kilo | 11 |
+| Droid | 7 |
+
+| 指标 | 数值 |
+|------|------|
+| 总检查项数 | 2,422 |
+| 平均每实例检查项 | 33.6 |
+
+## 📝 数据格式
+
+每个实例是一个 JSON 对象，包含以下字段：
+
+```json
+{
+  "instance_id": "md-course-builder-conventional-commits",
+  "user_query": ["Implement the feature as specified..."],
+  "system_prompt": "You are a CLI assistant...",
+  "category": "Claude.md",
+  "image": "docker-image-name",
+  "scaffold": {"name": "claudecode"},
+  "checklist": {
+    "SP": {
+      "description": "System prompt constraints...",
+      "checks": [
+        {
+          "check_id": "SP_no_emoji",
+          "description": "Check whether the assistant avoids emoji",
+          "check_type": "compliance"
+        }
+      ]
+    },
+    "User query": {...}
+  }
+}
+```
+
+| 字段 | 描述 |
+|------|------|
+| `instance_id` | 唯一任务标识符 |
+| `user_query` | 用户消息列表（支持多轮） |
+| `system_prompt` | 系统级行为约束 |
+| `category` | 被测试的主要指令来源 |
+| `image` | 任务环境 Docker 镜像 |
+| `scaffold` | 智能体脚手架配置 |
+| `checklist` | 结构化评估标准 |
+
+## 💻 使用方法
+
+```python
+from datasets import load_dataset
+
+# 加载数据集
+dataset = load_dataset("MiniMaxAI/OctoCodingBench")
+
+# 按类别筛选
+skill_tasks = [d for d in dataset["train"] if d["category"] == "Skill"]
+
+# 按脚手架筛选
+claudecode_tasks = [d for d in dataset["train"] if d["scaffold"]["name"] == "claudecode"]
+```
+
+## ⚖️ 评估指标
+
+- **ISR（实例成功率）**：所有检查项通过为 1，否则为 0
+- **CSR（检查清单成功率）**：通过的检查项占比
+
+## 📜 引用
+
+```bibtex
+@misc{octocodingbench2026,
+  title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents},
+  author={MiniMax},
+  year={2026},
+  publisher={Hugging Face}
+}
+```
+