ReddRadar
Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/pinchbench/pinchbench
Metadata
Additional technical details for this skill
- author
- pinchbench
- version
- 1.0.0
- homepage
- https://pinchbench.com
- repository
- https://github.com/pinchbench/skill
SKILL.md
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 |
Inbox triage | |
task_17_email_search |
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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
aiskillstore/marketplace
perigon-backend
Perigon ASP.NET Core + EF Core + Aspire conventions
aiskillstore/marketplace
perigon-agent
Pointers for Copilot/agents to apply Perigon conventions
aiskillstore/marketplace
perigon-angular
Angular 21+ standalone/Material/signal conventions for Perigon WebApp
aiskillstore/marketplace
fastapi-mastery
Comprehensive FastAPI development skill covering REST API creation, routing, request/response handling, validation, authentication, database integration, middleware, and deployment. Use when working with FastAPI projects, building APIs, implementing CRUD operations, setting up authentication/authorization, integrating databases (SQL/NoSQL), adding middleware, handling WebSockets, or deploying FastAPI applications. Triggered by requests involving .py files with FastAPI code, API endpoint creation, Pydantic models, or FastAPI-specific features.
aiskillstore/marketplace
context7-efficient
Token-efficient library documentation fetcher using Context7 MCP with 86.8% token savings through intelligent shell pipeline filtering. Fetches code examples, API references, and best practices for JavaScript, Python, Go, Rust, and other libraries. Use when users ask about library documentation, need code examples, want API usage patterns, are learning a new framework, need syntax reference, or troubleshooting with library-specific information. Triggers include questions like "Show me React hooks", "How do I use Prisma", "What's the Next.js routing syntax", or any request for library/framework documentation.
aiskillstore/marketplace
browser-use
Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction.
Didn't find tool you were looking for?