senior-ml-engineer

I took a look at your senior-ml-engineer skill and noticed the grading came back pretty low (46/100) - mostly because the spec and PDA architecture need some serious work. The bones are there, but the documentation structure and progressive disclosure pattern could use a redesign to actually guide developers through the complexity instead of dumping it on them all at once.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

TL;DR

You're at 46/100, solidly in F territory. This is based on Anthropic's 5-pillar grading rubric. Your strongest area is Spec Compliance (12/15) - the YAML frontmatter is clean and trigger terms are solid. The real drag is Utility (6/20) - the references are empty templates instead of actual technical content, and PDA (10/30) - there's a ton of fluff and repetition that wastes tokens.

What's Working Well

• Trigger terms are solid - "MLOps", "model deployment", "RAG" are good searchability hooks that'll help developers find this
• Clean YAML structure - Frontmatter is valid and follows conventions; metadata is well-formed
• Organized navigation - 227 lines with clear section headers make it easy to scan and jump around
• Real problem domain - Addresses genuine gaps in ML deployment, monitoring, and production patterns that engineers actually need

The Big One: Empty Reference Files

This is your biggest problem right now. All three reference files - rag_system_architecture.md, mlops_production_patterns.md, and llm_integration_guide.md - contain identical boilerplate with generic "Core Principles" and "Advanced Patterns" sections. They're copy-paste templates with no actual domain-specific content.

Here's the fix: Replace each reference with real technical depth.

• `mlops_pr...