pinchbench
Run PinchBench benchmarks to evaluate OpenClaw agent performance across real-world tasks. Use when testing model capabilities, comparing models, submitting benchmark results to the leaderboard, or checking how well your OpenClaw setup handles calendar, email, research, coding, and multi-step workflows.
Profile →npx skills add dixiyao/FoT --skill pinchbench --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PinchBench Benchmark Skill PinchBench measures how well LLM models perform as the brain of an OpenClaw agent. Results are collected on a public leaderboard at [pinchbench.com](https://pinchbench.com). ## Prerequisites - Python 3.10+ - [uv](https://docs.astral.sh/uv/) package manager - OpenClaw instance (this agent) ## Quick Start ```bash cd <skill_directory> # Run benchmark with a specific model
What does the pinchbench skill do?
Run PinchBench benchmarks to evaluate OpenClaw agent performance across real-world tasks. Use when testing model capabilities, comparing models, submitting benchmark results to the leaderboard, or checking how well your OpenClaw setup handles calendar, email, research, coding, and multi-step workflows.
How do I install it?
Run `npx skills add dixiyao/FoT --skill pinchbench --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From dixiyao/FoT, a repository with 110 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.