A technical tool for producing consistent model rankings from metric-weighted evaluation inputs and deterministic benchmarking logic.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ml-model-eval-benchmark
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 ml-model-eval-benchmark 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 ML Model Evaluation & Benchmarking?
The ML Model Eval Benchmark skill provides a systematic approach to comparing machine learning model candidates. By utilizing weighted metrics and deterministic ranking outputs, it enables developers and data scientists to make data-driven decisions regarding model promotion and leaderboard placement. This skill simplifies the complex process of model evaluation within the Openclaw Skills ecosystem, ensuring that performance comparisons are both fair and reproducible across different development cycles.
By standardizing the evaluation lifecycle, this skill helps teams avoid inconsistencies in how metrics are interpreted. Whether you are choosing between different LLM architectures or fine-tuned versions of a specific model, this skill ensures that your Openclaw Skills workflow remains transparent, recording all weighting assumptions directly in the output for full auditability.
ML Model Evaluation & Benchmarking Use Cases
- Establishing internal model leaderboards to track performance progress over time.
- Automating model promotion and deployment decisions based on specific performance thresholds.
- Standardizing the comparison of multiple candidate models within Openclaw Skills pipelines.
- Documenting weighting assumptions and evaluation logic for compliance and audit trails.
How ML Model Evaluation & Benchmarking Works
- Define the specific metric weights and acceptable metric ranges for your evaluation criteria.
- Ingest the collected model metrics for each candidate being evaluated.
- Compute a weighted score and objective ranking based on the predefined logic.
- Export the final leaderboard and a formal recommendation for model promotion.
ML Model Evaluation & Benchmarking Setup
To begin using this evaluation tool within your Openclaw Skills environment, follow these steps:
- Navigate to the skill directory and ensure your metric data is prepared.
- Run the benchmarking script to generate model rankings:
python scripts/benchmark_models.py
- Consult the benchmarking guide for details on weighting and tie-break guidance:
cat references/benchmarking-guide.md
ML Model Evaluation & Benchmarking Data Schema & Taxonomy
| Data Type | Description |
|---|---|
| Metric Inputs | Consistent metric names and scales across all model candidates. |
| Weighting Logic | User-defined importance values for each specific metric. |
| Ranking Output | A deterministic leaderboard sorting models by weighted performance. |
| Assumptions | A record of all weighting assumptions stored within the output files. |
ML Model Evaluation & Benchmarking Advanced Features
- Deterministic ranking logic ensuring identical outputs for identical inputs across different runs.
- Seamless integration with automated Openclaw Skills CI/CD pipelines for model promotion.
- Support for custom tie-breaking guidance and complex weighting scenarios.
- Automated generation of promotion recommendations for faster model lifecycle management.
SKILL.md
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