/obsidian-ingest

description: Ingest a source into the vault - the vault rewrites itself around new knowledge. Every ingest updates entities, rewrites stale claims, synthesizes new concepts, and resolves contradictions.
category: research
triggers_en: ["ingest this source", "add this article", "import this", "absorb this"]
triggers_es: ["haz una ingesta de esta fuente", "añade este artículo", "importa esto", "absorbe esto", "mete esto al vault", "procesa esta fuente"]
triggers_pt: ["ingira esta fonte", "adicione este artigo", "importe isto", "absorva isto"]
triggers_zh: ["把这篇文章纳入知识库", "导入这份资料", "用这个来源更新我的笔记", "消化这份材料"]

Use the obsidian-second-brain skill. Execute `/obsidian-ingest $ARGUMENTS`:

The argument is a URL, file path, or pasted text. If no argument, ask what to ingest.

1. Read `_CLAUDE.md` first if it exists in the vault root

2. Classify the source type before reading the full content:

• **Article/blog post** - extract key claims, people, tools, concepts
• **PDF/document** - extract structure, findings, recommendations
• **Transcript (meeting/podcast)** - extract speakers, decisions, action items, quotes
• **YouTube video** - pull metadata, description, and transcript (see step 3 for method)
• **Audio file** (.m4a, .mp3, .wav, .ogg, .webm) - transcribe, identify speakers, extract decisions/tasks/promises
• **Image/screenshot** (.png, .jpg, .jpeg, .webp) - read/OCR the image, extract text and context
• **Raw text** - classify by content (opinion, technical, narrative) and extract accordingly

3. Read or fetch the full source content:

**For YouTube URLs** - try methods in this order (use the first one that works):

**Method A - `yt-dlp` (best, works in Claude Code / terminal):**

   which yt-dlp || brew install yt-dlp
   yt-dlp --skip-download --print title --print description --print duration_string --print view_count --print like_count --print upload_date --print channel "URL"
   yt-dlp --write-auto-sub --sub-lang en --skip-download -o "/tmp/%(id)s" "URL"

**Method B - YouTube MCP tools (works in Claude Desktop if configured):** Check if YouTube MCP tools are available. If so, use them.

**Method C - oEmbed fallback (works everywhere, limited data):** Fetch `https://www.youtube.com/oembed?url=URL&format=json` - gives title and channel only. Ask user to paste description for full ingest.

**For audio files** (.m4a, .mp3, .wav, .ogg, .webm):

   # Transcribe with Whisper (install if missing)
   which whisper || pip install openai-whisper
   whisper "path/to/audio.m4a" --model base --output_format txt --output_dir /tmp

If `whisper` can't be installed, ask the user to paste the transcript. After transcription: identify speakers if possible, extract decisions, action items, promises, and who said what. Save the transcript to `raw/transcripts/`.

**For images/screenshots** (.png, .jpg, .jpeg, .webp): Claude can read images directly. Analyze the image for:

• Text content (OCR) - extract all readable text
• UI screenshots - describe what's shown, extract data from tables/forms/dashboards
• Whiteboard/diagram photos - describe the structure and extract concepts
• Chat screenshots - extract messages, people, decisions

Save the image description to `raw/articles/` as a markdown summary with context.

**For articles** - use the WebFetch tool to pull the page content **For PDFs** - read the file directly **For pasted text** - use as-is

4. Extract and organize:

• **Entities**: people mentioned, companies, tools, projects
• **Concepts**: key ideas, frameworks, methodologies
• **Claims**: specific assertions with supporting evidence
• **Action items**: anything actionable for the user
• **Quotes**: notable quotes worth preserving

5. Save the raw source to `raw/` (immutable - never modify after saving):

• **Check for a previous ingest of this source first (#218, #239).** Compute `content_hash` over the *canonical* text of the source, never over the raw capture: the same page fetched twice rarely yields the same bytes (a JS shell one time and rendered DOM the next, navigation chrome, a cookie banner, a `+` where the page had `-`), and a hash over those bytes calls an unchanged source "changed" on every run. Canonicalize in this order, then hash: (1) keep the article body only - drop navigation, header, footer, sidebar, cookie and consent banners, share widgets, comment sections, and any frontmatter or metadata block the page itself embeds; (2) convert CRLF to LF and drop a leading BOM; (3) normalize list markers `*` and `+` to `-`; (4) collapse every run of whitespace, newlines included, to one space and trim. `content_hash` is the first 16 hex characters of the SHA-256 of that string (`printf '%s' "$CANONICAL" | shasum -a 256 | cut -c1-16`). The raw note body stays the verbatim capture; only the hash is computed over the canonical form. Then search `raw/` frontmatter for that `content_hash`, and for the same `source_url` (normalized: strip the scheme, `www.`, trailing slash and tracking parameters such as `utm_*`). Use Grep, not memory.
• Same hash found: the source is already in the vault. Do not write a second raw note. Skip to step 6 and treat this run as a re-read: build the proposals from the existing raw note, and say in the report that the source was already ingested on the date in its frontmatter. A re-read is bound by the confirmation rule in step 6 like any other run: it may not rewrite an existing note without the user's yes, unless the vault's `rewrite_policy` is `unattended` (step 6), which binds a re-read exactly as it binds a first ingest.
• Same URL, different hash: diff the new canonical text against the canonical form of the stored raw note before deciding. If the delta is capture noise the canonicalization missed (chrome, whitespace, list markers), treat it as the same-hash case: no second raw note, nothing superseded, and the stored note