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markitdown

Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.

68

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exemplary skill body: fully executable guidance covering the common paths, explicit validation and safe-defaults for batch operations, and clean one-level-deep progressive disclosure with verified reference and script files. The only slack is a small amount of trimmable boilerplate (citation section, release date, testing-provenance commentary).

DimensionReasoningScore

Conciseness

The body is dense and purposeful: routing tables, terse Core Operating Rules, a troubleshooting table, and lean code samples with no basic-concept padding. Not anchor 5 due to minor trimmable material: the boilerplate citation section (~15 lines), the "released September 21, 2026" date, and meta-commentary like "local fixtures and mocked request contracts were tested, without authenticated provider calls".

4 / 5

Actionability

Every section gives copy-paste-ready, complete commands and code: uv venv/pip install lines with pinned versions, CLI invocations with flags, fully-imported Python examples including StreamInfo construction, and script calls with concrete flags (--recursive, --manifest, --create-index). Common cases (local file, stream, stdin bytes, batch, OCR) are all covered.

5 / 5

Workflow Clarity

The path is clearly sequenced (choose route → install → verify → convert → check), with explicit validation checkpoints: installation verification via `markitdown --version` and `scripts/inspect_installation.py`, a numbered Quality Checks checklist for batch/output validation, safe-by-default batch behavior (skip unless --overwrite, opt-in --plugins/--allow-external-services), and a Troubleshooting table providing error-recovery feedback loops.

5 / 5

Progressive Disclosure

SKILL.md is a true overview: a routing table, quick starts, and core rules, with all detail pushed to 7 reference files and 3 scripts that all exist on disk, indexed in a "Reference Files" table with explicit "Read when" triggers. References are one level deep with only sibling cross-links (e.g., file_formats.md → cloud_and_ocr.md), so navigation is easy and nothing is deeply nested.

5 / 5

Total

19

/

20

Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, third-person description that names the tool, its purpose, and a broad range of capabilities with minimal fluff. Its main weakness is the absence of an explicit 'Use when...' trigger clause, leaving the invocation conditions only weakly implied, and natural trigger coverage would benefit from file extensions and format synonyms.

Suggestions

Add an explicit trigger clause such as "Use when converting PDF, Word, Excel, PowerPoint, HTML, or EPUB files (or URLs) to Markdown for search, RAG, or LLM ingestion" to raise completeness.

Include file extensions and everyday synonyms (.pdf, .docx, .pptx, Word, PowerPoint, spreadsheets) so users' natural phrasing matches the description.

Trim the capability enumeration (e.g., merge 'vision OCR, Azure extraction' and 'the official MCP server' into fewer, trigger-oriented phrases) so the description stays scannable once the trigger clause is added.

DimensionReasoningScore

Specificity

"Converts heterogeneous documents and selected URIs to Markdown" plus "Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server" names several concrete capabilities and formats. It falls just short of anchor 5 because most items are capability areas rather than distinct action verbs, and coverage relies on the body for the remaining specifics.

4 / 5

Completeness

The 'what' is clear and specific (convert documents/URIs to Markdown for analysis, search, and LLM/RAG ingestion), but there is no "Use when..." clause or equivalent explicit trigger guidance; the purpose phrase "for text analysis, search, and LLM/RAG ingestion" only weakly implies when to invoke it. Per the judging guideline, a missing 'Use when' clause caps this at 3.

3 / 5

Trigger Term Quality

Natural terms users would say are present: "documents", "Markdown", "convert", "PDF", "Office", "text analysis", "search", "LLM/RAG ingestion". Not anchor 5: it lacks file extensions (.docx, .xlsx, .pdf as extension tokens) and common synonyms like "Word" or "PowerPoint" that users often mention.

4 / 5

Distinctiveness Conflict Risk

Naming "Microsoft MarkItDown" pins the exact tool and a clear niche (document-to-Markdown conversion for ingestion), with distinctive terms like "MarkItDown", "vision OCR", and "MCP server". It is clearly distinguishable from other skills and unlikely to trigger for the wrong one.

5 / 5

Total

16

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/markitdown/SKILL.md

Reviewed

Table of Contents

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