README.md

metadata

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 | 中文

🌟 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:

{
  "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

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

@misc{octocodingbench2026,
  title={OctoCodingBench: Instruction-Following Benchmark for Coding Agents},
  author={MiniMax},
  year={2026},
  publisher={Hugging Face}
}