Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
PinchBench
Run PinchBench benchmarks to evaluate OpenClaw agent performance across real-world tasks. Use when testing model capabilities, comparing models, submitting b...
skill openclawclawhub Free
Run PinchBench benchmarks to evaluate OpenClaw agent performance across real-world tasks. Use when testing model capabilities, comparing models, submitting b...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, assets/ai_blog.txt, assets/quarterly_sales.csv, crab.txt, pyproject.toml
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- olearycrew
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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)
TaskCategoryDescriptiontask_00_sanityBasicVerify agent workstask_01_calendarProductivityCalendar event creationtask_02_stockResearchStock price lookuptask_03_blogWritingBlog post creationtask_04_weatherCodingWeather scripttask_05_summaryAnalysisDocument summarizationtask_06_eventsResearchConference researchtask_07_emailWritingEmail draftingtask_08_memoryMemoryContext retrievaltask_09_filesFilesFile structure creationtask_10_workflowIntegrationMulti-step API workflowtask_11_clawdhubSkillsClawHub interactiontask_12_skill_searchSkillsSkill discoverytask_13_image_genCreativeImage generationtask_14_humanizerWritingText humanizationtask_15_daily_summaryProductivityDaily digesttask_16_email_triageEmailInbox triagetask_17_email_searchEmailEmail searchtask_18_market_researchResearchMarket analysistask_19_spreadsheet_summaryAnalysisSpreadsheet analysistask_20_eli5_pdf_summaryAnalysisPDF simplificationtask_21_openclaw_comprehensionKnowledgeOpenClaw docs comprehensiontask_22_second_brainMemoryKnowledge management
Command Line Options
OptionDescription--modelModel identifier (e.g., anthropic/claude-sonnet-4)--suiteall, automated-only, or comma-separated task IDs--output-dirResults directory (default: results/)--timeout-multiplierScale task timeouts for slower models--runsNumber of runs per task for averaging--no-uploadSkip uploading to leaderboard--registerRequest new API token for submissions--upload FILEUpload 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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Assets2 Files
- SKILL.md
- README.md
- assets/ai_blog.txt
- assets/quarterly_sales.csv
- crab.txt
- pyproject.toml