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.
git clone --depth 1 https://github.com/tboome33/obsidian-mcp-routerWrote 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/commands/tboome33/obsidian-mcp-router/read-search-smart)<a href="https://agentmods.dev/commands/tboome33/obsidian-mcp-router/read-search-smart"><img src="https://agentmods.dev/badge/commands/tboome33/obsidian-mcp-router/read-search-smart/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/tboome33/obsidian-mcp-router/read-search-smart"><img src="https://agentmods.dev/badge/commands/tboome33/obsidian-mcp-router/read-search-smart.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00045 | $0.00116 |
| Opus 5 | $0.00023 | $0.00058 |
| Sonnet 5 | $0.00009 | $0.00023 |
| Haiku 4.5 | $0.00005 | $0.00012 |
Grade A, and why
read-search-smart 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 11d 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.
What it actually says
Invoke the read-search-smart skill.
Required: query. Optional: vault (default or * for fan-out), folders, excludeFolders, limit (default 10).
Requires the obsidian-mcp-router-bridge and smart-connections plugins on the target vault.
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.
- 11d ago First seen · 10 lines · 45 tokens per session scan A dee432c1eb53
read-search-smart is a command published in the GitHub repository tboome33/obsidian-mcp-router (1 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 116 once invoked, about $0.0002 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 commands, from other repositories
notebooklm
Vault-first source-grounded research via Gemini File Search. One command, no browser. The grounded parallel to /research-deep (which is open-web via Perplexity).
wiki-init
Initialize a new LLM Wiki in the current directory. Creates the full directory structure, config, and template files.
ai
Invoke the AI/LLM Application Engineer for RAG, agents, prompt engineering, evals, tool use, and LLM guardrails.
embeddings
Text embeddings for semantic search and similarity. Use when converting text to vectors, choosing embedding models, implementing chunking, or setting up semantic search. Triggers on embeddings, text-embedding, vector, chunking, cosine similarity, semantic search, embedding model, batch embed.
pgvector-search
Production hybrid search with PGVector and BM25 using Reciprocal Rank Fusion, metadata filtering, and performance tuning for semantic retrieval. Use when building hybrid semantic and keyword search, tuning PGVector performance, or filtering by metadata. Triggers on pgvector, hybrid search, BM25, reciprocal rank…
t00-ai-dev
A set of instructions for building applications that use artificial intelligence, such as chatbots, document search, or text-generation tools. It covers Claude, retrieval-augmented generation (RAG), embeddings, and vector databases, which help find relevant documents for an AI answer.