How do I install the Pinchbench skill?

Run npx skillmds@latest add modbender/pinchbench in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Pinchbench safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Pinchbench?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pinchbench free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Pinchbench?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pinchbench by modbender — Agent Skill

Name: Pinchbench
Rating: 4.2 (5 reviews)
Author: modbender

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.

modbender Updated Sep 9, 2026 12 repo stars

File contents

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_01_calendar,task_02_stock

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

Available Tasks (23)

Task	Category	Description
`task_00_sanity`	Basic	Verify agent works
`task_01_calendar`	Productivity	Calendar event creation
`task_02_stock`	Research	Stock price lookup
`task_03_blog`	Writing	Blog post creation
`task_04_weather`	Coding	Weather script
`task_05_summary`	Analysis	Document summarization
`task_06_events`	Research	Conference research
`task_07_email`	Writing	Email drafting
`task_08_memory`	Memory	Context retrieval
`task_09_files`	Files	File structure creation
`task_10_workflow`	Integration	Multi-step API workflow
`task_11_clawdhub`	Skills	ClawHub interaction
`task_12_skill_search`	Skills	Skill discovery
`task_13_image_gen`	Creative	Image generation
`task_14_humanizer`	Writing	Text humanization
`task_15_daily_summary`	Productivity	Daily digest
`task_16_email_triage`	Email	Inbox triage
`task_17_email_search`	Email	Email search
`task_18_market_research`	Research	Market analysis
`task_19_spreadsheet_summary`	Analysis	Spreadsheet analysis
`task_20_eli5_pdf_summary`	Analysis	PDF simplification
`task_21_openclaw_comprehension`	Knowledge	OpenClaw docs comprehension
`task_22_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

---name: pinchbenchdescription: 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.--- # 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 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_01_calendar,task_02_stock # Skip uploading results uv run benchmark.py --model anthropic/claude-sonnet-4 --no-upload ``` ## Available Tasks (23) | Task | Category | Description | |------|----------|-------------| | `task_00_sanity` | Basic | Verify agent works | | `task_01_calendar` | Productivity | Calendar event creation | | `task_02_stock` | Research | Stock price lookup | | `task_03_blog` | Writing | Blog post creation | | `task_04_weather` | Coding | Weather script | | `task_05_summary` | Analysis | Document summarization | | `task_06_events` | Research | Conference research | | `task_07_email` | Writing | Email drafting | | `task_08_memory` | Memory | Context retrieval | | `task_09_files` | Files | File structure creation | | `task_10_workflow` | Integration | Multi-step API workflow | | `task_11_clawdhub` | Skills | ClawHub interaction | | `task_12_skill_search` | Skills | Skill discovery | | `task_13_image_gen` | Creative | Image generation | | `task_14_humanizer` | Writing | Text humanization | | `task_15_daily_summary` | Productivity | Daily digest | | `task_16_email_triage` | Email | Inbox triage | | `task_17_email_search` | Email | Email search | | `task_18_market_research` | Research | Market analysis | | `task_19_spreadsheet_summary` | Analysis | Spreadsheet analysis | | `task_20_eli5_pdf_summary` | Analysis | PDF simplification | | `task_21_openclaw_comprehension` | Knowledge | OpenClaw docs comprehension | | `task_22_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: ```bash # 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: ```bash # 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](https://pinchbench.com). The leaderboard shows: - Model rankings by overall score - Per-task breakdowns - Historical performance trends

Frequently asked questions