markitdown

I took a look at your markitdown skill and wanted to share some thoughts.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

The TL;DR

You're at 80/100, solidly in B territory. This evaluation is based on Anthropic's Claude Skills best practices across five pillars. Your strongest area is Progressive Disclosure Architecture (26/30) — you've nailed the layered structure with a clean SKILL.md overview and five focused reference files. The weakest area is Spec Compliance (12/15) and Writing Style (7/10), where some smaller refinements would push you higher.

What's Working Well

• Progressive disclosure is chef's kiss — Your five reference files (structured_data, web_content, document_conversion, media_processing, advanced_integrations) sit exactly one level deep from SKILL.md. That's the sweet spot for token economy and discoverability.
• Practical utility — You're solving a real problem: converting 20+ file formats to Markdown for LLM processing. The input/output examples and batch processing templates show you understand actual workflows.
• Modular design — Trigger phrases cover the common cases (convert, extract, transcribe, OCR, batch). The "When to Use" section helps developers understand scope without reading everything.
• Rich examples — Both CLI and Python code examples; good error handling patterns scattered through the references.

The Big One

Your writing voice is inconsistent, and it's costing you points. The spec wants imperative/instructional voice throughout, but your references slip into second-person statements like "Use high-resolution images for better accuracy" in media_processing.md. This violates the voice requirements and pulls down y...