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 your Spec Compliance and Writing Style scores.

Fix: Rewrite passive instructions as imperative declarations. Instead of "Use high-resolution images...", say "High-resolution images improve OCR accuracy." This is one pass through all references (especially media_processing.md:72-77, advanced_integrations.md, and document_conversion.md). Impact: +2 points.

Other Things Worth Fixing

1. Strip verbose code comments — Lines like # Handles UTF-8, special characters, quotes, etc. state the obvious. Let the code be self-documenting. This cuts unnecessary tokens and cleans up your token economy. Impact: +1 point.

2. Add trigger phrases to your description — You've got solid ones (convert, extract, transcribe), but your SKILL.md description only lists 1-2. Expand that list so developers find you faster through search. Impact: +1 point.

3. Add TOCs to long references — Files over 200 lines (document_conversion.md, advanced_integrations.md) need a table of contents for quick navigation. Add a ## Contents section at the top with anchor links. Impact: +1 point.

4. Consolidate repetitive imports — Every code block re-imports MarkItDown and OpenAI. Show imports once per section, then use abbreviated examples. Saves tokens. Impact: +1 point.

Quick Wins

• Fix voice consistency (second-person → imperative) across references — biggest bang for buck
• Strip obvious code comments
• Add 3-4 trigger phrases to SKILL.md description
• Add TOCs to references over 200 lines
• Deduplicate imports in code examples

These changes push you from 80 → 86-88 range with minimal effort.

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