obsidian-second-brain turns an Obsidian vault into persistent, searchable memory for Claude Code and other command-line coding agents, storing knowledge as linked Markdown notes. It is for developers, founders, writers, and researchers who want agents to retain project context across sessions. Its catalogue entries provide commands, hooks, a plugin, a skill, and instructions for capturing, finding, and maintaining that memory.
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/eugeniughelbur/obsidian-second-brainWrote 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/eugeniughelbur/obsidian-second-brain/obsidian-distill)<a href="https://agentmods.dev/commands/eugeniughelbur/obsidian-second-brain/obsidian-distill"><img src="https://agentmods.dev/badge/commands/eugeniughelbur/obsidian-second-brain/obsidian-distill/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/eugeniughelbur/obsidian-second-brain/obsidian-distill"><img src="https://agentmods.dev/badge/commands/eugeniughelbur/obsidian-second-brain/obsidian-distill.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 | $0.00024 | $0.01581 |
| Opus 5 | $0.00012 | $0.00790 |
| Sonnet 5 | $0.00005 | $0.00316 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
obsidian-distill 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use the obsidian-second-brain skill. Execute /obsidian-distill $ARGUMENTS:
The optional argument is a note path, a [[wikilink]], a folder, or a source URL/file. If none is given, ask what to distill (offer the longest recently-touched notes in raw/ and the research folders as candidates).
A distillation is not a summary. A summary throws the source away; a distillation keeps a verifiable trail, so a teammate (or a future agent) can check every claim against the exact place it came from. This is the trust primitive: condensed but never unmoored from evidence.
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Read
_CLAUDE.mdfirst if it exists in the vault root. Resolve the destination folder perreferences/folder-map.md(a distillation is a concept/reference note: wiki-stylewiki/concepts/, Obsidian-styleKnowledge/). -
Resolve and read the source:
- A vault note or
[[wikilink]]: read it in full. - A folder: read every note in it and distill them as one corpus (note which file each claim comes from).
- A URL: fetch it (read the page). A local file: read it. Save the untouched original to
raw/first if it is not already there - the distillation must point at a stable source. - Never distill from memory or a partial read. If the source is too large to read in one pass, read it in ordered chunks and keep going; do not sample (see the anti-fabrication rule).
- A vault note or
-
Segment the source into stable, citable source blocks and number them. A block is a heading section, a paragraph, a list, or a transcript turn - whatever the source's natural unit is. Record for each block a locator the reader can find again: a heading path, a paragraph index, or a timestamp/line range. These locators are the provenance anchors.
-
Extract the key claims. For each claim, write one tight sentence and attach its provenance: which block(s) it came from, as
(src: B3)or(src: B3, B7). Every claim MUST carry at least one block reference - a claim with no source does not go in the distillation (if you believe it but the source does not say it, it is your inference, not a distilled claim; see step 6). Mark each claim's confidence (stated | high | medium | speculation) and add a recency marker on any time-sensitive external fact ((as of YYYY-MM, domain)). -
Group the claims under short thematic headings (the distilled structure), preserving the provenance tags inline. Keep the source's own terms; do not smooth them into generic phrasing.
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Keep inference separate from evidence. If the distillation surfaces a conclusion the source implies but never states, put it under a clearly labelled
## Inferences (not in the source)section, each markedconfidence: speculation, so distilled fact and your reasoning never blur. -
Write the distillation note. Path:
<resolved-folder>/Distill - <source-title> (YYYY-MM-DD).md. It MUST followreferences/ai-first-rules.md. If that path does not resolve from your working directory, search upward for it; if you still cannot read it, say so before writing rather than producing a note that silently skips the rule. Frontmatter includestype: distillation,ai-first: true,source(the verbatim path/URL of what was distilled),source-blocks(the count),date,tags: [distillation, thinking],related-peopleandrelated-projects(wikilinks, per thetype: distillationschema). Body order:## For future agent- 2-3 sentences: what was distilled, why, and that every claim carries a(src: Bn)pointer back to the numbered source blocks at the bottom for verification.## Distilled claims- the grouped claims with inline provenance tags.## Inferences (not in the source)- only if step 6 produced any.## Source blocks- the numbered list (B1,B2, ...) with each block's locator (heading/paragraph/timestamp) and a short quote or first line, so the anchors are resolvable from inside the vault. This is the recency/verification anchor.
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Link the distillation from the original source note (add a
Distilled: [[...]]line) and from today's daily note. Append a one-line entry to the operation log (Logs/YYYY-MM-DD.mdif it exists, elselog.md).
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 · 49 lines · 24 tokens per session scan A 2e38bb7393eb
obsidian-distill is a command published in the GitHub repository eugeniughelbur/obsidian-second-brain (4,392 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 1,581 once invoked, about $0.0001 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.
Other commands, from other repositories
setup-vault-types
Configure which document types your vault uses (journals, books, meetings, clients, etc.) and scaffold extractors.
second-brain-mapping
Map your vault: extract structured metadata from every typed file, surface cross-doc insights, optionally build a knowledge graph.
cierre
A sales call just ended: turn its transcript into the full follow-up (CRM, tasks, email draft, reminder, coaching).
daily-journal
Daily journal interview and entry creator with emotional floor tagging.
deconstruct
First-principles analyst: surface hidden assumptions, find foundational truths, rebuild from scratch.
diagnose
Run a self-check on your AI Brain Starter install (CLAUDE.md, Meta folder, skills, hooks, MCPs).