Benchmark OpenClaw agents by executing real-world tasks and aggregating results on a public leaderboard.
npx skills add https://github.com/pinchbench/skill --skill pinchbench
Please help me install this Agent Skill. Skill: pinchbench Source: https://github.com/pinchbench/skill/tree/main Command: npx skills add https://github.com/pinchbench/skill --skill pinchbench
PinchBench benchmarks how well AI models perform as the brains of OpenClaw agents by executing real-world tasks and surfacing results on a public leaderboard.
Run uv run benchmark.py --model <provider/model> to start benchmarking an OpenClaw agent.
High-intent search queries and answers about installing and using this skill.
You can benchmark AI coding agents by executing real-world tasks and aggregating the results on a public leaderboard. PinchBench runs end-to-end evaluations across productivity, research, writing, coding, and analysis categories to measure tool usage and multi-step workflows.
AI agent benchmarking supports automated, llm_judge, and hybrid grading modes with per-task rubrics. These modes evaluate how well models handle complex tool usage and multi-step workflows across diverse task categories.
To run an AI model benchmark, execute uv run benchmark.py --model <provider/model>. You can optionally specify a suite of tasks to tailor the benchmark to your specific model capabilities.
Yes, AI agent benchmarking requires pyyaml, fabric, and paramiko as dependencies. These libraries support task execution, configuration parsing, and remote operations during end-to-end evaluations.
Yes, AI model benchmarking includes leaderboard submission and model comparison features. You can submit your evaluation results to a public leaderboard to drive improvements and compare performance against other models.
AI agent benchmarking covers Productivity, Research, Writing, Coding, Analysis, Email, Memory, and Skills task categories. These diverse categories ensure comprehensive evaluation of real-world tool usage and multi-step agent workflows.
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