Identify and categorize assumptions, limitations, and weaknesses in model outputs into a JSON schema.
npx skills add https://github.com/gtylee/CodexGAS --skill alw
Please help me install this Agent Skill. Skill: alw Source: https://github.com/gtylee/CodexGAS/tree/main/modelgas/skills/alw Command: npx skills add https://github.com/gtylee/CodexGAS --skill alw
This skill formalizes and surfaces implicit assumptions, limitations, and weaknesses embedded in a model, turning tacit risk into explicit, auditable items for governance and safe deployment.
Run the ALW assessment on your model outputs to extract explicit assumptions, limitations, and weaknesses. Review the generated JSON against your risk governance checklist and iterate with additional evidence as needed. Save the artifact to your project risk folder to support audits and decision-making.
High-intent search queries and answers about installing and using this skill.
To identify and document model assumptions for risk governance, run an assessment on model outputs to surface data, methodology, and market dynamics assumptions. This process links each extracted assumption to traceable evidence, turning tacit risks into explicit, auditable items for safe deployment.
The best way to surface limitations and weaknesses in machine learning models is to perform a structured risk analysis that catalogs scope, realism, and applicability boundaries. This identifies potential failure modes and ranks them by priority with actionable mitigations for governance.
Evidence tracking for model risk analysis works by linking each identified assumption, limitation, and weakness directly to supporting evidence within a structured JSON schema. This creates an auditable artifact that feeds risk registers and supports safe deployment reviews.
Yes, you can generate a structured JSON risk report from model outputs by running an assessment that categorizes assumptions, limitations, and weaknesses. The schema-compliant output can be saved to your project risk folder to support audits and decision-making.
You need to formalize implicit assumptions for model governance when preparing for safe deployment and audits. This process turns tacit risks into explicit, auditable items by linking data, methodology, and market dynamics assumptions to traceable evidence.
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