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).

The rules below are not style preferences. Each one is here because its absence produced a specific, silent failure — a fabricated patient count, a broken PDF link, an empty Dataview table — in a vault of 100+ 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:

1. Vault root — from the user, or $OBSIDIAN_VAULT. Never guess a home-directory path.
2. 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.
3. 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

python3 scripts/extract_pdfs.py <pdf_folder> <text_cache_folder> [max_pages]

Defaults to 12 pages, which covers abstract through discussion for most papers.

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: Launch subagents in parallel

Five subagents × 5–6 papers is the working batch size: enough parallelism to clear 25 papers in one pass, small enough that per-agent quality holds. 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.

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.

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.

Anti-Hallucination rules (non-negotiable)

A note that is fluent, correctly formatted, and wrong in its numbers is worse than no note: the user cites it. These three rules exist to make that failure impossible rather than unlikely.

1. Numbers, authors, and dates come from the extracted text only — never from model knowledge, however familiar the paper. Well-known papers drift between versions, and that is exactly where invented values look most plausible.
2. Subagents receive .txt paths, never PDF paths. A subagent that cannot open a file does not report the failure; it writes from training data. The text indirection is the only reliable guard.
3. What the text does not state, the note does not claim. Write "not stated in the extracted text" instead of filling the gap.

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.

Structural rules

4. Preserve the PDF filename exactly in ![[filename.pdf]]. Obsidian embeds are sensitive to case, spaces, and punctuation — take the text filename and swap .txt for .pdf, character for character.
5. 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.
6. Use the existing tag vocabulary (references/tag-vocabulary.md) rather than inventing top-level tags.
7. Name notes with 3–5 keyword concepts, not the PDF's full title and not paper_001.
8. Never overwrite an existing note — see the /lit-sync boundary above.

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.

Reference files

File	Read it when
references/templates.md	writing any literature or concept note
references/subagent-prompt.md	launching a batch
references/concept-extraction.md	extracting concepts across notes
references/tag-vocabulary.md	choosing tags
references/workflow.md	the user asks how the layers fit together
references/locale/ko/note_templates.md	the vault is Korean-structured
assets/example_paper_note.md	unsure about literature-note formatting
assets/example_concept_note.md	unsure about concept-note formatting

Troubleshooting

Symptom	Cause	Fix
Subagent says it could not read the PDF	it was given a PDF path	run extract_pdfs.py, pass .txt paths
Note reads plausibly but numbers are wrong	subagent wrote from training data	re-run against the text file; verify n, CI, p-values
Dataview table is empty	frontmatter field renamed	match references/templates.md exactly
PDF embed shows a broken tile	filename mismatch	compare character by character, including case
Note content is generic	text file is abstract-only or OCR is poor	re-extract with more pages, or check the source PDF