Assess AI agent systems across 10 AWAF v1.0 pillars and generate scored reports.
npx skills add https://github.com/YogirajA/awaf-skill --skill awaf
Please help me install this Agent Skill. Skill: awaf Source: https://github.com/YogirajA/awaf-skill/tree/main Command: npx skills add https://github.com/YogirajA/awaf-skill --skill awaf
This Skill provides a structured AWAF v1.0 architectural assessment for AI agent systems by evaluating production-readiness across 10 pillars in 3 tiers, using diverse evidence (code, configs, runbooks, reports, diagrams) to generate a scored report with findings and actionable recommendations.
Provide evidence (code, configs, runbooks, reports, and diagrams) to begin an AWAF assessment and receive a structured, actionable report.
High-intent search queries and answers about installing and using this skill.
AI agent production-readiness assessment evaluates your system across 10 pillars using evidence like IAM policies, architecture diagrams, and code to generate a scored report with actionable findings and recommendations.
The AWAF v1.0 standard is a structured architectural assessment framework that scores AI agent systems across 10 pillars in 3 tiers using verifiable evidence to produce per-pillar readiness ratings and prioritized recommendations.
Yes, agent production-readiness evaluation accepts runbooks, observability exports, code, IAM policies, architecture diagrams, and verbal descriptions as evidence sources to drive confidence in the scored assessment.
Generate a comparative readiness report by providing evidence sources for each AI agent, which are evaluated pillar-by-pillar to produce consistent scores, findings, and actionable recommendations across systems.
To start an architecture assessment, provide any available evidence such as code, configurations, IAM policies, runbooks, observability exports, architecture diagrams, or verbal descriptions of your AI agent system.
Yes, the agent readiness evaluation enforces frontmatter presence and pillar-by-pillar scoring to ensure structured, actionable outputs that enable consistent comparisons across different AI agent systems.
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