Extract model metadata from MOT files and classify openness against MOF criteria.
npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill mot-cogni-ai-ou
Please help me install this Agent Skill. Skill: mot Source: https://github.com/Cogni-AI-OU/cogni-ai-agent-skills/tree/main/mot Command: npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill mot-cogni-ai-ou
It helps you evaluate how open a machine learning model is and extract the model’s key metadata without guessing or relying on outdated third-party sources.
Example use case: comparing multiple candidate models for procurement or governance by ensuring their component-level openness matches your required MOF tier.
Ask the AI to evaluate a specific model using MOF by extracting its metadata from the Model Openness Tool models directory and returning the final MOF classification plus the requested fields.
High-intent search queries and answers about installing and using this skill.
Model openness classification maps a model's components to Model Openness Framework criteria to determine its openness class. It evaluates machine learning models by extracting metadata from official definitions to verify compliance with required tiers.
You extract model metadata by parsing raw JSON or YAML model definitions to pull architecture, origin, producer, type, release date, framework, and components. This produces structured, concise output for quick review or governance workflows.
The Model Openness Framework is a set of criteria for evaluating how open a machine learning model is. You need it for governance, procurement, or auditing to ensure a model's component-level openness matches your required tier.
Yes, you can evaluate multiple candidate models for procurement or governance compliance. The process applies to selecting, auditing, or documenting specific models or lists of models within the MOT models directory.
Model openness classification does not require third-party sources or external dependencies. It evaluates models by extracting metadata directly from official MOT source files to ensure accurate reporting without relying on outdated external data.
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