senior-data-engineer

Looking at your senior data engineer skill—I'm curious how you're thinking about the progression from spec to actual implementation, since there's a gap there that's affecting the score.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

The TL;DR

You're at 43/100, which puts you in F territory. This is based on Anthropic's best practices for agentic skills. Your strongest area is Spec Compliance (12/15)—the YAML frontmatter and metadata are solid. But Progressive Disclosure Architecture (8/30) is where you're losing the most points. The reference files are basically empty templates, and the main file is bloated with marketing language instead of concrete guidance.

What's Working Well

• Metadata is clean. Your frontmatter validates, name follows conventions, and the description has solid trigger terms (designing data architectures, building pipelines, etc.)
• Spec compliance is tight. You nailed the required fields and file structure—no friction there.
• Breadth of scope. Covering ML, LLM, ETL, and streaming shows you're thinking about the full data engineering landscape.

The Big One: Empty Reference Files

This is your biggest issue. You've got three reference files (data_modeling_patterns.md, data_pipeline_architecture.md, dataops_best_practices.md) that are basically identical boilerplate placeholders. They say things like "World-class data pipeline architecture" and "Core Principles" but provide zero actual content—no schema patterns, no architecture diagrams, no CI/CD strategies, nothing.

Why it matters: References are supposed to be your PDA leverage. They let you keep SKILL.md lean while providing depth on demand. Right now they're just adding noise.

Concrete fix: Replace the boilerpl...