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/thoreinstein/obsidian-rag/researchnpx skills add thoreinstein/obsidian-rag --skill researchgit clone --depth 1 https://github.com/thoreinstein/obsidian-ragWrote 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/thoreinstein/obsidian-rag/research)<a href="https://agentmods.dev/skills/thoreinstein/obsidian-rag/research"><img src="https://agentmods.dev/badge/skills/thoreinstein/obsidian-rag/research.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 | $0.00061 | $0.00320 |
| Opus 5 | $0.00030 | $0.00160 |
| Sonnet 5 | $0.00012 | $0.00064 |
| Haiku 4.5 | $0.00006 | $0.00032 |
Grade A, and why
research 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 4d 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.
This is a copy
100% identical to research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Deep Vault Research
Research a topic across the user's Obsidian vault using a multi-pass approach.
Workflow
-
Semantic Discovery — Call
obsidian_rag_querywith the user's question (limit 5-10). This surfaces the most relevant chunks across the vault. -
Targeted Reading — For each high-relevance result, call
obsidian_read_noteto get the full context. Skim for the most pertinent sections. -
Link Traversal — Check
obsidian_get_linkson the most relevant notes to find connected knowledge. Read promising linked notes. -
Synthesis — Combine findings into a clear answer with citations:
- Reference specific notes by name:
[[Note Name]] - Quote relevant passages when helpful
- Note any gaps or contradictions in the vault's knowledge
- Reference specific notes by name:
Arguments
The user's query follows the skill invocation. Example: /research what do I know about authentication patterns
Tips
- If RAG returns no results, suggest running
/indexfirst - For broad topics, do multiple RAG queries with different phrasings
- Always cite which notes your answer draws from
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.
- 4d ago First seen · 36 lines · 61 tokens per session scan A 12d8c3fe372e
research is a skill published in the GitHub repository thoreinstein/obsidian-rag (2 stars, last pushed 1mo ago), licensed MIT. It adds 61 tokens to every session and 320 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to research, differing in 0 lines, and is treated as a copy.
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