A data-driven decision framework to benchmark and rank AI models based on your specific prompts, domain requirements, and usage patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install benchmark-model-provider
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install benchmark-model-provider using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Benchmark Model Provider?
This skill provides a systematic approach to evaluating AI providers and models, moving beyond generic leaderboards to create personalized performance metrics. By executing a domain-specific prompt suite across multiple endpoints, it generates objective rankings based on quality, depth, cost, and speed.
It is designed for developers and researchers who need to validate which model—whether local or service-based—truly fits their daily operational needs within the Openclaw Skills ecosystem. The process ensures that decisions are based on repeatable data rather than vague preferences or marketing claims.
Benchmark Model Provider Use Cases
- Comparing the cost-efficiency of different LLMs for daily research tasks.
- Benchmarking local models against hosted services for privacy-sensitive coding workflows.
- Generating auditable performance reports to justify model selection in production environments.
- Periodically reranking models as new updates or pricing structures are released using Openclaw Skills.
How Benchmark Model Provider Works
- Define the benchmark context including purpose, domain, and specific usage frequency.
- Construct a benchmark specification containing a curated set of 5 to 10 domain-specific questions.
- Identify and verify target providers or models within the local environment to ensure compatibility.
- Execute prompts across all selected models independently, capturing raw outputs and performance metrics.
- Apply a standardized scoring rubric to evaluate quality, depth, cost, and latency.
- Generate comprehensive reports in Markdown, HTML, and PDF formats for review, sharing, and publication.
Benchmark Model Provider Setup
To get started with this skill, ensure you have Python 3 installed and your environment variables configured. Use the following commands to initialize the environment for Openclaw Skills:
# Set your primary benchmark API key
export BENCHMARK_API_KEY="your_api_key_here"
# Run the benchmark specification builder
python3 scripts/build_benchmark_spec.py
# Execute the benchmark run
python3 scripts/run_benchmark.py
Refer to the environment variables documentation for specific provider configurations.
Benchmark Model Provider Data Schema & Taxonomy
The skill organizes data into a structured hierarchy to ensure auditability and repeatability. Key artifacts include:
| Artifact | Description | Format |
|---|---|---|
| Benchmark Spec | The versioned configuration of prompts and models | YAML/JSON |
| Raw Answers | Direct, unaltered responses from each tested model | TXT/MD |
| Metrics JSON | Quantitative data on tokens, latency, and costs | JSON |
| Score Breakdown | Final calculated rankings based on the scoring rubric | JSON |
| Reports | Human-readable summaries and landing pages | MD/HTML/PDF |
All metadata is preserved to allow for reranking without rerunning model executions.
Benchmark Model Provider Advanced Features
- Support for agent_context mode to test models within specific shared system prompt frameworks.
- Reranking capabilities that allow users to update scoring weights or formulas without re-executing expensive model calls.
- Sub-agent orchestration for bounded parallel execution to accelerate large-scale benchmarks.
- Unicode-capable PDF generation for accurate multilingual reporting in Vietnamese, Chinese, and more.
- Normalized model identification to resolve naming mismatches across different provider catalogs within Openclaw Skills.
SKILL.md
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