pinchbench

Agent Building

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.

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How to use this skill

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Source SKILL.md: https://github.com/dixiyao/FoT/blob/HEAD/experiment/pinchbench/SKILL.md

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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`	Knowledge	OpenClaw docs comprehension
`task_second_brain`	Memory	Knowledge management

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