Obsidian Paper Vault
Converts a folder of research PDFs into a two-layer Obsidian vault: literature notes (one per paper, templated) and atomic concept notes (synthesized across papers).
Relationship to /lit-sync
Both skills write literature and concept notes into the same vault folders. They enter from opposite ends and must not overwrite each other.
/lit-sync | obsidian-paper-vault | |
|---|---|---|
| Input | Zotero collection / refs.bib | a folder of PDFs |
| Note key | citekey | short descriptive title |
| Owns | manuscript/_src/refs.bib | extracted-text cache |
Never overwrite an existing note. If a note for the paper already exists (by title, DOI,
or citekey), report it and skip. When both skills are in play, /lit-sync notes are the
bibliographic spine; this skill's notes are the read-through summaries.
Step 0: Resolve the vault layout — ask, do not assume
Establish three paths before writing anything:
- Vault root — from the user, or
$OBSIDIAN_VAULT. Never guess a home-directory path. - Literature notes folder — default
Literature/. If the vault already has a folder serving this role (02_research/논문/,Papers/,文献/), honor the existing layout rather than imposing the default. - Concept notes folder — default
Concepts/, same honor-what-exists rule.
For a vault whose structure is Korean, see references/locale/ko/note_templates.md — folder
names and note headings in Korean, opt-in.
Also confirm PyMuPDF is available: python3 -c "import fitz; print(fitz.__version__)"
(install with pip install PyMuPDF). Text extraction caches to
~/.local/cache/paper-vault-texts/ unless the user names another location.
Step 1: Pre-extract PDF text — always, for any batch
bashpython3 "${CLAUDE_SKILL_DIR}/scripts/extract_pdfs.py" <pdf_folder> <text_cache_folder> [max_pages]
Defaults to 12 pages, which covers abstract through discussion for most papers. If notes come
out generic, the text file is abstract-only or its OCR is poor: re-extract with more pages or
check the source PDF. A PDF with no extractable text (an image-only scan) is reported as
FAILED … (needs OCR) and gets no .txt; OCR it or read it interactively, never batch it.
Never hand a PDF path to a subagent. A subagent that cannot open a file does not report
failure — it writes the note from training data, and the result is a plausible note with
invented numbers. Pass the .txt paths instead. Single-paper interactive work may read the
PDF directly (the Read tool handles PDFs); batches may not.
Step 2: Write the notes — subagents in parallel
Split a batch across parallel subagents, a handful of papers each (per-agent note quality drops when one agent carries too many), and group papers thematically per agent so each one can spot recurring concepts.
Give each subagent: its assigned text-file paths with destination filenames, the template
from references/templates.md verbatim, the list of concept notes that already exist, and
the prohibition on inventing anything. references/subagent-prompt.md holds the full prompt.
Every note, batch or single, follows these rules:
- Numbers, authors, and dates come from the extracted text only — never from model knowledge, however familiar the paper, because well-known papers drift between versions and that is exactly where invented values look most plausible.
- What the text does not state, the note does not claim. Write "not stated in the extracted text" instead of filling the gap.
- Preserve the PDF filename exactly in
![[filename.pdf]]: take the text filename and swap.txtfor.pdf, character for character (embeds are sensitive to case, spaces, and punctuation). - Match the frontmatter field names in
references/templates.md. Dataview queries break on a renamed field, and they break by returning an empty table, not an error. - Use the existing tag vocabulary (
references/tag-vocabulary.md) rather than inventing top-level tags. - Name notes with 3–5 keyword concepts, not the PDF's full title and not
paper_001.
See assets/example_paper_note.md when unsure about literature-note formatting.
Gate — before a batch is accepted: spot-check two notes against their text files (one sample size, one effect estimate). If either value is absent from the text, stop the batch and report it rather than continuing. This gate is the user's call to waive, not the skill's.
Step 3: Track progress in a queue file
Keep PAPER_QUEUE.md in the vault with per-paper status (done / pending / skipped / in
progress) so a 200-paper vault survives across sessions. Update it after each batch.
Step 4: Extract concept notes once 10+ literature notes exist
A phrase earns a concept note when it appears in 3+ notes, carries pedagogical value, and is
treated differently by different papers. Model names, datasets, and journals are entities,
not concepts. See references/concept-extraction.md for the full criteria, the frequency
scan, and the seedling/growing/mature lifecycle, and assets/example_concept_note.md for the
format.
Roughly one new concept note per 5–7 literature notes is healthy. Faster than that is concept inflation, and it shows up as dozens of stub notes the user never edits.
Gate — before concept notes are presented as done: concept notes ship as 🌱Seedling with the definition marked as a placeholder, and require user review before they count as the reader's own. Say so explicitly when handing them over.
Read references/workflow.md when the user asks how the layers fit together or why the skill
will not write certain notes.
