| id | obsidian-dataweave |
| name | ObsidianDataWeave: NotebookLM, Atomization, and LLM Wiki |
| summary | KISA's skill: turns Claude Code / Codex into a NotebookLM control panel + .docx import with Zettelkasten atomization + a compiled LLM Wiki layer + FTS5 memory across the whole Obsidian vault. All programmatic, all into your vault, with no external search dependencies. |
| type | skill |
| author | kisa |
| recommended | false |
| added | "2026-07-04T00:00:00.000Z" |
| tags | ["obsidian","notebooklm","zettelkasten","llm-wiki","fts5","memory","notes"] |
| source | https://github.com/howdeploy/ObsidianDataWeave |
| description | Use when the user wants to control NotebookLM programmatically, import .docx into an Obsidian vault as atomic Zettelkasten notes, build a compiled LLM Wiki layer, or full-text-search the whole vault. Claude Code and Codex both supported. |
| license | MIT |
ObsidianDataWeave
Turns Claude Code and Codex into a full control panel for NotebookLM and your Obsidian
vault. You run deep research, manage sources, and pull notes out of notebooks — all with a
single natural-language command. In parallel, it imports .docx from Google Drive and
atomizes them into Zettelkasten notes with a MOC, tags, and wikilinks. On top — an isolated
LLM Wiki layer (a compiled base in the Karpathy style), and FTS5 memory — a full-text
index of the entire vault without a single external dependency. Public: howdeploy/ObsidianDataWeave, MIT.
Both clients are supported: Claude Code (skill) and Codex (AGENTS.md).
Four Layers
- NotebookLM → Obsidian. Programmatic control of NotebookLM through
notebooklm-py as
a library (not the CLI — strictly one-shot, no retry duplication). Deep/fast research, source
management, dedup, note extraction, and atomization into the vault.
- .docx → Obsidian. Import from Google Drive (rclone) → parse → atomize → write.
- LLM Wiki. A compiled wiki on top of the atomic notes: it accumulates through an explicit
merge (not RAG, not a recompute on every query), linked with
[[wikilinks]].
- FTS5 memory. A local full-text index of the whole vault on stdlib SQLite, bm25 +
snippets, auto-updates after every write. Search for agents.
Installation
git clone https://github.com/howdeploy/ObsidianDataWeave.git
cd ObsidianDataWeave
bash install.sh --vault-path "/path/to/your/vault"
The installer checks for Python 3.10+, installs the dependencies (python-docx, pyyaml), creates
a config.toml with the vault path, registers the skill globally in ~/.claude/skills/obsidian-dataweave/,
and appends a block to ~/.claude/CLAUDE.md. After installation the skill works from any directory.
Modes: --mode claude (default), --mode codex (checks AGENTS.md), --mode local
(dependencies + config only). Updating is idempotent: git pull && bash install.sh.
How to Use
After installation, just tell the agent in natural language:
| What to say | What happens |
|---|
process MyDocument.docx | Download → parse → atomize → write into the vault |
process note "Title" | Enrich or atomize an existing note |
process contacts "Contacts" | A note with contacts → personal cards + Networking MOC |
run research in notebook "<id>" "<query>" | Deep research in NotebookLM via the API |
clean duplicates in notebook "<id>" | Dedup sources in the notebook |
create wiki "<slug>" | Skeleton of a new LLM Wiki space (project/corpus, RU/EN) |
build wiki "<slug>" | Compile the raw material into pages (guard on [[wikilinks]]) |
search notes "<query>" | FTS5 search across the whole vault (bm25 + snippets) |
Processing modes: Enrich (short note → tags/links/expansion, 1→1), Atomize
(long → atomic notes + MOC, 1→N), Contacts (contacts → cards + Networking MOC).
LLM Wiki
The third knowledge layer is a compiled wiki in the Karpathy style. It lives in the isolated folder
<vault>/LLM Wiki/<slug>/; atomic notes don't go there. It accumulates through an explicit
merge: existing [[wikilinks]] must be preserved, otherwise compile fails with
WIKI_LINKS_LOST. Two modes: project (fixed core pages: overview,
architecture, components, workflows, glossary…) and corpus (only entities/concepts grow).
python3 scripts/wiki_init.py demo --mode project --title "Demo Project"
python3 scripts/wiki_ingest.py demo path/to/article.md --kind articles
python3 scripts/wiki_compile.py demo --since-last-compile
python3 scripts/wiki_lint.py demo --strict
All scripts write through a single writer, vault_writer.py — the atomic pipelines and the wiki
share one write point.
NotebookLM: First Login
The Google session is saved in ~/.notebooklm/storage_state.json — login is one-time.
python3 -m venv .venv
.venv/bin/python scripts/notebooklm_setup.py --skip-login
.venv/bin/notebooklm login
Then: process_notebook.py <notebook_id> (id is the last segment of the notebook URL),
research_notebook.py run "<id>" "<query>" --mode deep.
Requirements
- Python 3.10+ (3.11+ recommended), rclone with access to Google Drive
(for
.docx), Claude Code or Codex.
- SQLite with FTS5 (stdlib
sqlite3) — no separate DBMS needed; doctor.py checks this.
- Obsidian plugins: Local REST API
(HTTP interface to the vault) + MCP Obsidian
(MCP bridge Claude Code ↔ Obsidian).
Related: NotebookLM++ — a Chrome extension
for bulk-importing sources into NotebookLM (web, YouTube, playlists, PDF snapshots).
