pinchbench

name	pinchbench
description	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.
metadata	{"author":"pinchbench","version":"2.0.0-rc1","homepage":"https://pinchbench.com","repository":"https://github.com/pinchbench/skill"}

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.

Prerequisites

• Python 3.10+
• uv package manager
• OpenClaw instance (this agent)

Quick Start

cd <skill_directory>

# Run benchmark with a specific model
uv run benchmark.py --model anthropic/claude-sonnet-4

# Run only automated tasks (faster)
uv run benchmark.py --model anthropic/claude-sonnet-4 --suite automated-only

# Run specific tasks
uv run benchmark.py --model anthropic/claude-sonnet-4 --suite task_calendar,task_stock

# Skip uploading results
uv run benchmark.py --model anthropic/claude-sonnet-4 --no-upload

Available Tasks (23)

Task	Category	Description
task_sanity	Basic	Verify agent works
task_calendar	Productivity	Calendar event creation
task_stock	Research	Stock price lookup
task_blog	Writing	Blog post creation
task_weather	Coding	Weather script
task_summary	Analysis	Document summarization
task_events	Research	Conference research
task_email	Writing	Email drafting
task_memory	Memory	Context retrieval
task_files	Files	File structure creation
task_workflow	Integration	Multi-step API workflow
task_clawdhub	Skills	ClawHub interaction
task_skill_search	Skills	Skill discovery
task_image_gen	Creative	Image generation
task_humanizer	Writing	Text humanization
task_daily_summary	Productivity	Daily digest
task_email_triage	Email	Inbox triage
task_email_search	Email	Email search
task_market_research	Research	Market analysis
task_spreadsheet_summary	Analysis	Spreadsheet analysis
task_eli5_pdf_summary	Analysis	PDF simplification
task_openclaw_comprehension

Command Line Options

Option	Description
--model	Model identifier (e.g., anthropic/claude-sonnet-4)
--suite	all, automated-only, or comma-separated task IDs
--output-dir	Results directory (default: results/)
--timeout-multiplier	Scale task timeouts for slower models
--runs	Number of runs per task for averaging
--no-upload	Skip uploading to leaderboard
--register	Request new API token for submissions
--upload FILE	Upload previous results JSON

Token Registration

To submit results to the leaderboard:

# Register for an API token (one-time)
uv run benchmark.py --register

# Run benchmark (auto-uploads with token)
uv run benchmark.py --model anthropic/claude-sonnet-4

Results

Results are saved as JSON in the output directory:

# View task scores
jq '.tasks[] | {task_id, score: .grading.mean}' results/0001_anthropic-claude-sonnet-4.json

# Show failed tasks
jq '.tasks[] | select(.grading.mean < 0.5)' results/*.json

# Calculate overall score
jq '{average: ([.tasks[].grading.mean] | add / length)}' results/*.json

Adding Custom Tasks

Create a markdown file in tasks/ following TASK_TEMPLATE.md. Each task needs:

• YAML frontmatter (id, name, category, grading_type, timeout)
• Prompt section
• Expected behavior
• Grading criteria
• Automated checks (Python grading function)

Leaderboard

View results at pinchbench.com. The leaderboard shows:

• Model rankings by overall score
• Per-task breakdowns
• Historical performance trends