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 skills add Michael-OvO/obsidian-knowledge-agent --skill obsidian-knowledgegit clone --depth 1 https://github.com/Michael-OvO/obsidian-knowledge-agentWrote 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/michael-ovo/obsidian-knowledge-agent/obsidian-knowledge)<a href="https://agentmods.dev/skills/michael-ovo/obsidian-knowledge-agent/obsidian-knowledge"><img src="https://agentmods.dev/badge/skills/michael-ovo/obsidian-knowledge-agent/obsidian-knowledge/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/skills/michael-ovo/obsidian-knowledge-agent/obsidian-knowledge"><img src="https://agentmods.dev/badge/skills/michael-ovo/obsidian-knowledge-agent/obsidian-knowledge.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 | $0.00146 | $0.00895 |
| Opus 5 | $0.00073 | $0.00447 |
| Sonnet 5 | $0.00029 | $0.00179 |
| Haiku 4.5 | $0.00015 | $0.00089 |
Grade A, and why
obsidian-knowledge 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 9d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Obsidian Knowledge Ingest
This skill helps with any knowledge work in an Obsidian vault — from saving a single link to building a whole course's notes. It does as much or as little structuring as the material needs, fits the vault it's in, recalls what it learned about that vault before each run, and reflects afterward to get better over time — without any model training.
Match the effort to the material. Decide the altitude first (see "Choose the altitude" in the workflow): a Capture is one clean note, a Small collection is a few notes plus a light index, and only a genuinely large, structured input earns the Full build below. Default to the lightest touch; escalate only when the material clearly asks for it.
How to use it
-
Read the rules first. Read these bundled references in order:
references/style-guide.md— the teaching-note voice and quality barreferences/self-evolution.md— the recall → reflect learning loopreferences/obsidian-conventions.md— frontmatter, wikilinks, naming, LaTeX, Mermaid, canvasreferences/vault-architecture.md— folder roles and branch boundariesreferences/ingestion-workflow.md— the full workflow you will execute
If the target vault already has these files at
.agents/in its root, prefer those — they may be customized for that vault. -
Recall (Phase 0, Step 0). Before classifying, read
.agents/learned/if it exists: applyconventions.md, useexamples.mdas few-shot classification guidance, and follow any matching playbook inlearned/skills/. Silent on a fresh vault. -
Execute the workflow in
references/ingestion-workflow.md:- Phase 0 — Ingest: classify the input profile and extract a structural map.
- Phases 1–3 — Compile: scaffold the tree, write content/source/concept notes (deep ML/Quant notes use the artifacts that teach — runnable code, a LaTeX equation, a diagram — where each earns its place, not to fill a quota), build the concept-graph canvas.
- Phases 4–5 — Distribute: wire prev/next navigation, validate all wikilinks
(run the bundled
scripts/validate_links.pythat sits next to this skill, or the inline check in the workflow's Phase 4), run the teaching-quality pass. - Phase 6 — Commit: commit the collection; clear
Inbox/only with explicit user approval.
What ships with it
6 files 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.
- 9d ago First seen · 58 lines · 146 tokens per session scan A 682f19c13e74
obsidian-knowledge is a skill published in the GitHub repository Michael-OvO/obsidian-knowledge-agent (207 stars, last pushed 2mo ago), licensed MIT. It adds 146 tokens to every session and 895 once invoked, about $0.0007 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 2026-08-30.
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