Claude Skill

pinchbench/skill

PinchBench 是一个用于评估 LLM 模型作为 OpenClaw 编码代理的基准测试系统,由 kilo.ai 团队构建。

概览

Stars1,260
Forks144
语言Python
最后更新2026-07-02
最近同步2026-07-03
前往 GitHub

仓库信息

拥有者pinchbench
仓库skill
完整名称pinchbench/skill
Repo ID1,154,980,232

安装这个 Skill

git clone https://github.com/pinchbench/skill.git

Registry 信息

类型openclaw_skill
质量分80/100
验证状态readme_parsed
最近验证2026-06-03
平台
ClaudeOpenClaw
能力
pdfmemorysearchvideoterminal
识别文件
README.mdSKILL.mdpyproject.tomltests
配置键
PINCHBENCH_OFFICIAL_KEYKEYOPENROUTER_API_KEYKILO_API_KEYANTHROPIC_API_KEYOPENAI_API_KEY

项目简介

PinchBench 是一个用于评估 LLM 模型作为 OpenClaw 编码代理的基准测试系统,由 kilo.ai 团队开发。

英文描述

PinchBench is a benchmarking system for evaluating LLM models as OpenClaw coding agents. Made with 🦀 by the humans at https://kilo.ai

要点

  • 评估 LLM 模型作为编码代理
  • 基于 OpenClaw 的基准测试框架
  • 由 kilo.ai 团队构建
  • 基于 Python 的实现

使用场景

  • 基准测试 LLM 编码代理性能
  • 比较不同 LLM 模型在编码任务中的表现
  • 基于代理的代码生成研究

README 摘要

# 🦀 PinchBench **Real-world benchmarks for AI coding agents** [![Leaderboard](https://img.shields.io/badge/leaderboard-pinchbench.com-blue)](https://pinchbench.com) [![License](https://img.shields.io/badge/license-MIT-green)](LICENSE) <!-- task-count-badge -->![Tasks](https://img.shields.io/badge/tasks-53-orange)<!-- /task-count-badge --> > **Note:** This repository contains the benchmark skill/tasks. It is NOT the source of official leaderboard results. To add models to the official results, modify [pinchbench/scripts/default-models.yml](https://github.com/pinchbench/scripts/blob/main/default-models.yml). PinchBench measures how well LLM models perform as the brain of an [OpenClaw](https://github.com/openclaw/openclaw) agent. Instead of synthetic tests, we throw real tasks at agents: scheduling meetings, writing code, triaging email, researching topics, and managing files. Results are collected on a public leaderboard at **[pinchbench.com](https://pinchbench.com)**. ![PinchBench](pinchbench.png) ## Why PinchBench? Most LLM benchmarks test isolated capabilities. PinchBench tests what actually matters for coding agents: - **Tool usage** — Can the model call the right tools with the right parameters? - **Multi-step reasoning** — Can it chain together actions to complete complex tasks? - **Real-world messiness** — Can it handle ambiguous instructions and incomplete information? - **Practical outcomes** — Did it actually create the file, send the email, or schedule the meeting? ## Quick Start ```bash # Clone the skill git clone https://github.com/pinchbench/skill.git cd skill # Run benchmarks with your model of choice ./scripts/run.sh --model openrouter/anthropic/claude-sonnet-4 # Or run specific tasks ./scripts/run.sh --model openrouter/openai/gpt-4o --suite t

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数据来自 GitHub,同步时间:2026-07-03