PDF Markdown Knowledge Organizer
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About this skill
Problem
PDF notes often contain headers, page numbers, tables, diagrams, and ambiguous headings, which can become fragmented after a plain Markdown conversion. This skill is aimed at engineers turning study materials, manuals, or course notes into maintainable knowledge documents.
How It Works
The core step is to reorganize after understanding, not mechanically copy text: it extracts and quality-checks text/pages, builds a strict # → ## → ### hierarchy, removes non-knowledge noise, merges related points, and converts tables into Markdown tables. For images, it identifies diagrams, UI screenshots, table screenshots, and other knowledge-bearing visuals, keeps relative ./images/ references, and adds descriptions. When content is ambiguous, classification is unclear, or granularity is uncertain, it asks instead of guessing. In batch mode, each PDF produces a _knowledge.md file, then a Wiki.md index is generated afterward.
Boundaries
It treats only the current PDF as the source of truth and forbids filling gaps from other generated files. OCR output is less reliable, so unclear parts should be marked [To Verify] or [Extraction Failed]. It works best for clean text-heavy or diagram-heavy PDFs; scanned, formula-dense, or low-quality files still need manual review.
Use Cases
- Turn course PDF handouts into hierarchical Markdown notes covering chapters, definitions, and examples.
- Extract configuration steps, UI screenshots, and flow diagrams from PDF manuals with in-place image references.
- Batch-convert multiple PDFs in one folder into separate knowledge documents and generate a Wiki index.
- Check low-quality PDFs for garbled text, missing pages, and broken tables, labeling extraction failures explicitly.
Best For
- Students organizing course handouts who want PDF chapters, definitions, and examples turned into reviewable Markdown notes.
- Engineers maintaining technical docs who need configuration steps, screenshots, and diagrams from PDF manuals converted into structured references.
- Knowledge-management leads who want to batch-process multiple PDFs and generate a unified Wiki index.
- Reviewers checking curation quality who expect uncertain or failed extraction items to be labeled rather than auto-filled.
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