Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ssube/conclave/obsidian-importnpx skills add ssube/conclave --skill obsidian-importgit clone --depth 1 https://github.com/ssube/conclaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ssube/conclave/obsidian-import)<a href="https://agentmods.dev/skills/ssube/conclave/obsidian-import"><img src="https://agentmods.dev/badge/skills/ssube/conclave/obsidian-import.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00066 | $0.02013 |
| Opus 5 | $0.00033 | $0.01007 |
| Sonnet 5 | $0.00013 | $0.00403 |
| Haiku 4.5 | $0.00007 | $0.00201 |
Grade A, and why
obsidian-import scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Obsidian Import Skill
Import an Obsidian vault into ChromaDB for semantic search. Parses markdown structure (frontmatter, headings, tags, links, tables) and chunks content by heading hierarchy so each vector represents a coherent topic.
Requirements
pip install chromadb pyyaml
Quick Start
# Scan a vault (dry run — no writes)
python3 {baseDir}/obsidian_import.py scan --vault ~/my-vault
# Import everything into ChromaDB
python3 {baseDir}/obsidian_import.py import --vault ~/my-vault
# Import only files in a specific folder
python3 {baseDir}/obsidian_import.py import --vault ~/my-vault --folder "Projects/Active"
# Import only files with a specific tag
python3 {baseDir}/obsidian_import.py import --vault ~/my-vault --tag "important"
# Incremental import (skip unchanged files)
python3 {baseDir}/obsidian_import.py import --vault ~/my-vault --incremental
Commands
scan — Preview what would be imported
python3 {baseDir}/obsidian_import.py scan --vault ./my-vault [--folder PATH] [--tag TAG] [--glob PATTERN]
Shows file count, section count, total chunks, and per-file breakdown. No writes — safe to run anytime.
import — Import vault content into ChromaDB
python3 {baseDir}/obsidian_import.py import --vault ./my-vault \
[--collection vault] \
[--folder PATH] \
[--tag TAG] \
[--glob PATTERN] \
[--incremental] \
[--chunk-size 1500] \
[--chunk-overlap 150]
Parses every matching .md file, splits by heading sections, chunks long
sections, and upserts into ChromaDB with rich metadata.
clear — Remove all items from a collection
python3 {baseDir}/obsidian_import.py clear --collection vault
Deletes the collection entirely. Use before a full re-import if needed.
stats — Show collection statistics
python3 {baseDir}/obsidian_import.py stats [--collection vault]
How It Works
1. File Discovery
Recursively finds all .md files in the vault, excluding:
.obsidian/(config directory).trash/(Obsidian trash)- Files matching
--globexclusion patterns - Dot-prefixed directories
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 261 lines · 66 tokens per session scan A b40ea079d11c
obsidian-import is a skill published in the GitHub repository ssube/conclave (2 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 2,013 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
vector-backend-operations
Select and connect the right vector-store backend for the vector-mcp MCP server — chromadb, postgres/pgvector, qdrant, couchbase, or mongodb — and supply the correct dbtype/connection parameters that every collection and search call needs. Use when the agent must decide which engine to target, wire up…
vector-collection-management
Create, populate, list, and delete vector-store collections through the vector-mcp MCP server's vectorcollectionmanagement tool. Use when the agent must stand up a new RAG collection, ingest documents (from a directory, file paths/URLs, or raw text) into an existing collection, enumerate collections, or drop one …
vector-hybrid-search
Retrieve knowledge from a vector-store collection via the vector-mcp MCP server's vectorsearch tool — semantic (ANN) search, lexical BM25 search, or a hybrid of the two fused with Reciprocal Rank Fusion. Use when the agent must answer a question from an indexed corpus, pull top-k relevant chunks for RAG context, or…
vector-mcp-operations
Operate vector-mcp through its governed MCP and GraphOS capabilities. Use for collection lifecycle, root-confined document ingestion, semantic or lexical retrieval, hybrid search, backend readiness, troubleshooting, and sanitized verification evidence.
vector-databases
Vector database operations — embed, store, search, and build RAG pipelines.
rag-expert
Expert Retrieval-Augmented Generation: chunking, embeddings, vector/hybrid search, reranking, and grounded answers. Trigger keywords: RAG, retrieval, embeddings, vector database, chunking, reranking, hybrid search, BM25, grounding, citations, hallucination, context window, recall. Use to build or debug RAG pipelines…