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AI Skill Report Card

Discovering Document Transformation Notebooks

B+78·Oct 3, 2026·Source: Web

Quick Start13 / 15

Deploy the discovery prompt below against a web-enabled AI agent or code search tool. It systematically audits public repos for two categories of notebooks: (1) Cross-Format & ODF-First Repurposing, and (2) Novel Derived Asset Generation.

Minimum viable search operator combinations:

filetype:ipynb "Docling" OR "Unstructured.io"
filetype:ipynb "ODT" OR "ODF" "Pandoc"
filetype:ipynb "pdf to marp" OR "pdf to newsletter"
filetype:ipynb "text to anki" OR "flashcard generator"
filetype:ipynb "python-uno" LibreOffice headless

Recommendation▾

Add a real concrete example showing actual discovered repo names/links rather than only describing what an entry would contain, to strengthen input/output pair quality

Workflow14 / 15

Progress:

Step 1: Define target scope (confirm which categories/tools are in-scope; tune if user wants only specific stacks like LlamaIndex, Marp, Docling)
Step 2: Execute searches across GitHub, Hugging Face Hub, Kaggle, Colab, PyPI docs, dev blogs
Step 3: Filter for functional, runnable .ipynb files (not just tutorials or dead repos)
Step 4: Classify each find into Category 1 (Cross-Format/ODF) or Category 2 (Derived Asset Generation)
Step 5: Extract tech stack, I/O spec, and workflow mechanics for each
Step 6: Build the target matrix (Category / Engine / Input / Output / Value)
Step 7: Compile minimum 8-10 entries into markdown report with Core Value Matrix justification for each

Recommendation▾

Trim the master prompt duplication with the workflow section — some redundancy between Quick Start operators and the embedded prompt's search operators could be consolidated

The Master Discovery Prompt

Use this verbatim when deploying to a web-browsing or repo-indexing AI agent:

[SYSTEM DIRECTIVE: TECHNICAL ASSET DISCOVERY AGENT]

ROLE:
You are an expert Open-Source Asset Auditor and Python Workflow Architect.
Discover, evaluate, and detail existing open-source Jupyter Notebooks (.ipynb),
Python automation repositories, and developer scripts enabling document
transformations from legacy/rich layout formats (PDF, ODF, ODT, ODS, ODP, EPUB)
into modular multi-format assets.

TARGET SCOPE: Search GitHub, Hugging Face Hub, Kaggle Notebooks, Google Colab
repos, PyPI docs, and developer blogs for two categories:

CATEGORY 1 - Cross-Format & ODF-First Repurposing:
- Pandoc/UNO bridges (ODT/ODS -> Markdown/HTML5/EPUB/DOCX)
- Unstructured.io, Docling, Marker, PyMuPDF/pdfplumber pipelines
  (multi-column PDF -> structured JSON/clean Markdown)
- LibreOffice headless (python-uno) automation
- Direct XML/ZIP parsing of .odt/.docx for media/style/text isolation

CATEGORY 2 - Novel Derived Asset Generation:
- Document-to-Newsletter engines (HTML/MD email editions)
- Executive/Slide Deck builders (python-pptx, Reveal.js, Marp)
- Social content/micro-asset generators (quotes, stats, carousels)
- Educational systems (quizzes, Anki flashcards, study guides)
- Vector KB & wiki builders (Chroma, LanceDB, FAISS)
- Multilingual/multi-modal parallel repurposing

SEARCH OPERATORS:
"filetype:ipynb" OR "extension:ipynb"
"ODF" OR "ODT" OR "Pandoc" OR "Docling" OR "Unstructured.io"
"magazine repurposing" OR "pdf to newsletter" OR "document to slides"
"pdf to marp" OR "text to anki notebook" OR "kb generator ipynb"

OUTPUT FORMAT (per entry):
1. Notebook Name & Repository Link
2. Primary Technology Stack
3. Input -> Output Specification
4. Workflow Mechanics (3-4 sentences)
5. Core Value Matrix (why superior to standard converters)

Provide minimum 8-10 distinct notebooks/pipeline blueprints across both
categories. Deliver as clean markdown report.

Examples14 / 20

Example 1: Input: "Find notebooks that convert ODT files to EPUB preserving footnotes" Output: Entry citing a Pandoc-based .ipynb using pandoc --from=odt --to=epub3, noting it preserves footnote anchors and embedded images via Pandoc's native ODF reader, contrasted against LibreOffice's lossy "Save As" GUI export.

Example 2: Input: "Find pipelines that turn long PDFs into Anki flashcard decks" Output: Entry describing a notebook using PyMuPDF for text extraction + an LLM prompt chain to generate Q&A pairs + genanki Python library to compile .apkg output, with CSV export fallback.

Recommendation▾

Include a brief note on how to handle rate-limiting/search API constraints when deploying against GitHub code search, since that's a common real-world pitfall

Output Deliverable Template

Always fill the target matrix with these columns:

Category	Primary Engine	Input Format	Output Format	Functional Value

Minimum rows: 8-10. Mix across both categories — don't cluster all results in one.

Best Practices

Prioritize runnable notebooks over conceptual blog posts — verify the repo has an actual .ipynb with executed cells or clear install/run instructions.
Cross-check tool names against current versions (Docling, Marker, Unstructured.io evolve fast) — flag if a notebook uses a deprecated API.
When exact notebooks aren't found, provide runnable pipeline blueprints (named, stack-specified, with pseudocode) rather than leaving gaps — label these clearly as "blueprint" vs. "discovered."
Always distinguish ODF-native tooling (Pandoc, python-uno) from PDF-only tooling (Docling, Marker, pdfplumber) — they solve different structural problems.
Favor notebooks that preserve structural fidelity (tables, footnotes, embedded media) over naive text-dump converters.

Common Pitfalls

Don't list plain format-converter scripts (e.g., bare pandoc file.odt -o file.md one-liners) as if they were full pipelines — the value-add is in multi-stage transformation (extraction → restructuring → asset generation).
Don't conflate Category 1 (format fidelity preservation) with Category 2 (new asset creation) — keep the matrix classification clean.
Don't present dead or archived repos without flagging staleness (check last commit date).
Don't skip the "Core Value Matrix" justification — a bare link list is not a deliverable.