How do I install the Obsidian Search skill?

Run npx skillmds@latest add rami-maalouf/obsidian-search in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Obsidian Search safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Obsidian Search?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Obsidian Search free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Obsidian Search?

rami-maalouf (@rami-maalouf) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Obsidian Search by rami-maalouf — Agent Skill

Name: Obsidian Search
Author: rami-maalouf

Obsidian Search

Find and retrieve notes from Rami's Obsidian vault by topic or theme using semantic search against Smart Connections embeddings. Use when asked to find notes about a specific subject, retrieve relevant vault content, or surface what Rami has written about a topic.

rami-maalouf Updated Sep 17, 2026

File contents

Obsidian Vault Search

You help Rami find relevant notes in his Obsidian vault using semantic search. This is NOT the same as letterly-automation (which imports notes) — this skill is purely for finding existing notes by topic.

Vault location

/Users/rami/Library/Mobile Documents/iCloud~md~obsidian/Documents/Obsidian

Also accessible via symlink at: /Users/rami/Documents/life-os/Obsidian

Use the symlink path when working inside the life-os repo.

Smart Connections embeddings

The vault uses the Smart Connections Obsidian plugin. Embeddings live in:

<vault>/.smart-env/multi/

Each .ajson file corresponds to one note. The filename is the note path with /, , and . replaced by _, then .ajson appended.

Example: My Notes/Foo bar.md → My_Notes_Foo_bar_md.ajson

The shared utility for reading these is at:

/Users/rami/Documents/life-os/ai-agents-config/skills/utils/smart_connections.py

Key functions:

get_vault_root() — finds the vault by looking for .smart-env/
load_note_embeddings() — loads all note-level vectors
load_block_embeddings() — loads all heading-level vectors

Search approach

Option A — Semantic search via Smart Connections (preferred when embeddings are available)

Run a semantic search using the existing utility:

cd /Users/rami/Documents/life-os
.venv/bin/python ai-agents-config/skills/obsidian-similar-notes/scripts/find_similar.py \
  --query "TOPIC HERE" \
  --mode note \
  --threshold 0.4 \
  --top 10

If the venv isn't set up, fall back to Option B.

Option B — Keyword search with grep (fallback)

grep -r -l --include="*.md" -i "KEYWORD" "/Users/rami/Library/Mobile Documents/iCloud~md~obsidian/Documents/Obsidian" 2>/dev/null | head -20

Then read the top matches.

Option C — Direct folder browsing

If the user specifies a folder (e.g., "check my Essays folder"), use Glob to list files in that subtree, then read the most relevant ones.

After finding matches

List the matching notes with their path and a 1-sentence summary of what each is about.
Ask the user which ones they want to read in full, or read the top 3 automatically if they asked you to "fetch" or "retrieve" them.
If the user wants to build on the content (write an article, brainstorm, etc.), read the full note text and present it clearly before proceeding.

Key vault structure (for context)

unprocessed/ — inbox, newly imported notes not yet organized
My Outputs/Transcriptions/ — processed voice memos and video transcripts
My Outputs/My Essays/ — published or draft essays
My Resources/ — Maps of Content (MOCs) only, no raw notes
Hidden/ — personal/private notes (gitignored, may have access)

Notes on the embeddings model

Model: TaylorAI/bge-micro-v2 (384-dim vectors) Similarity threshold for "related": 0.45 (use 0.4 for broader search) Block-level search uses heading chains: path#H1#H2

---name: obsidian-searchdescription: Find and retrieve notes from Rami's Obsidian vault by topic or theme using semantic search against Smart Connections embeddings. Use when asked to find notes about a specific subject, retrieve relevant vault content, or surface what Rami has written about a topic.---# Obsidian Vault SearchYou help Rami find relevant notes in his Obsidian vault using semantic search. This is NOT the same as letterly-automation (which imports notes) — this skill is purely for finding existing notes by topic.## Vault location```/Users/rami/Library/Mobile Documents/iCloud~md~obsidian/Documents/Obsidian```Also accessible via symlink at: `/Users/rami/Documents/life-os/Obsidian`Use the symlink path when working inside the life-os repo.## Smart Connections embeddingsThe vault uses the Smart Connections Obsidian plugin. Embeddings live in:```<vault>/.smart-env/multi/```Each `.ajson` file corresponds to one note. The filename is the note path with `/`, ` `, and `.` replaced by `_`, then `.ajson` appended.Example: `My Notes/Foo bar.md` → `My_Notes_Foo_bar_md.ajson`The shared utility for reading these is at:```/Users/rami/Documents/life-os/ai-agents-config/skills/utils/smart_connections.py```Key functions:- `get_vault_root()` — finds the vault by looking for `.smart-env/`- `load_note_embeddings()` — loads all note-level vectors- `load_block_embeddings()` — loads all heading-level vectors## Search approach### Option A — Semantic search via Smart Connections (preferred when embeddings are available)Run a semantic search using the existing utility:```bashcd /Users/rami/Documents/life-os.venv/bin/python ai-agents-config/skills/obsidian-similar-notes/scripts/find_similar.py \ --query "TOPIC HERE" \ --mode note \ --threshold 0.4 \ --top 10```If the venv isn't set up, fall back to Option B.### Option B — Keyword search with grep (fallback)```bashgrep -r -l --include="*.md" -i "KEYWORD" "/Users/rami/Library/Mobile Documents/iCloud~md~obsidian/Documents/Obsidian" 2>/dev/null | head -20```Then read the top matches.### Option C — Direct folder browsingIf the user specifies a folder (e.g., "check my Essays folder"), use Glob to list files in that subtree, then read the most relevant ones.## After finding matches1. List the matching notes with their path and a 1-sentence summary of what each is about.2. Ask the user which ones they want to read in full, or read the top 3 automatically if they asked you to "fetch" or "retrieve" them.3. If the user wants to build on the content (write an article, brainstorm, etc.), read the full note text and present it clearly before proceeding.## Key vault structure (for context)- `unprocessed/` — inbox, newly imported notes not yet organized- `My Outputs/Transcriptions/` — processed voice memos and video transcripts- `My Outputs/My Essays/` — published or draft essays- `My Resources/` — Maps of Content (MOCs) only, no raw notes- `Hidden/` — personal/private notes (gitignored, may have access)## Notes on the embeddings modelModel: `TaylorAI/bge-micro-v2` (384-dim vectors)Similarity threshold for "related": 0.45 (use 0.4 for broader search)Block-level search uses heading chains: `path#H1#H2`

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