obsidian-wiki is a framework that helps AI agents build and maintain an interconnected knowledge base from text-based material in an Obsidian vault. It is for people who want their agents to remember discoveries, connect related information, and answer questions with wiki-link citations. Catalogue add-ons provide the agent skills, instructions, agents, and configuration used to create and maintain these wikis.
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/ar9av/obsidian-wiki/wiki-exportnpx skills add Ar9av/obsidian-wiki --skill wiki-exportgit clone --depth 1 https://github.com/Ar9av/obsidian-wikiWrote 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/ar9av/obsidian-wiki/wiki-export)<a href="https://agentmods.dev/skills/ar9av/obsidian-wiki/wiki-export"><img src="https://agentmods.dev/badge/skills/ar9av/obsidian-wiki/wiki-export.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 5.1 | $0.00172 | $0.07052 |
| Opus 5 | $0.00086 | $0.03526 |
| Sonnet 5 | $0.00034 | $0.01410 |
| Haiku 4.5 | $0.00017 | $0.00705 |
Grade A, and why
wiki-export 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 6d 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 — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Export — Knowledge Graph Export
You are exporting the wiki's wikilink graph to structured formats so it can be used in external tools (Gephi, Neo4j, custom scripts, browser visualization).
Before You Start
- Resolve config — follow the Config Resolution Protocol in
llm-wiki/SKILL.md(inline@nameoverride → walk up CWD for.env→ global config → prompt setup). This givesOBSIDIAN_VAULT_PATH - Confirm the vault has pages to export — if fewer than 5 pages exist, warn the user and stop
Project Filter (optional)
If the user's invocation includes a project name — e.g. /wiki-export prismor, "export the prismor project", "export project:security" — activate project filter mode:
- Extract the project name from the argument or phrase. Normalise: lowercase, strip the word "project".
- Keep only pages where either condition holds:
- The page
idstarts withprojects/<name>/(path-based match) - The page's
tagsarray contains<name>(tag-based match)
- The page
- Drop any edge where either endpoint was excluded.
- Note the filter in the summary:
(filtered: project:<name> — X of Y pages) - Set
graph.graph.filter = "project:<name>"in the JSON output.
If both a project filter and a visibility filter are active, apply both (project filter first, then visibility filter on the remaining set).
Visibility Filter (optional)
By default, all pages are exported regardless of visibility tags. This preserves existing behavior.
If the user requests a filtered export — phrases like "public export", "user-facing export", "exclude internal", "no internal pages" — activate visibility filtered mode:
- Build a blocked tag set:
{visibility/internal, visibility/pii} - Skip any page whose frontmatter tags contain a blocked tag when building the node list
- Skip any edge where either endpoint was excluded
- Note the filter in the summary:
(filtered: visibility/internal, visibility/pii excluded)
Pages with no visibility/ tag, or tagged visibility/public, are always included.
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.
- 6d ago First seen · 446 lines · 172 tokens per session scan A 9463638f2c7f
wiki-export is a skill published in the GitHub repository Ar9av/obsidian-wiki (3,354 stars, last pushed today), licensed MIT. It adds 172 tokens to every session and 7,052 once invoked, about $0.0009 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 skills, from other repositories
llm-wiki
Build and maintain a persistent, interlinked Obsidian-compatible markdown wiki using Karpathy's LLM Wiki pattern. Extension-backed with auto-generated metadata, guardrails, and 14 custom tools (+3 opt-in agent-trajectory tools).
kb-memory
Use when knowledge base and memory system for AI agents. Covers company KB, persistent memory, session recall, and brain architecture for context preservation.
llm-wiki
Maintain a personal team knowledge base using the LLM Wiki pattern — incremental ingest, query, and lint operations on a layered wiki architecture.
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
link-memory
Use after important user-approved decisions, when durable context should be proposed or reviewed, and for explicit Link memory lifecycle work: remember, recall, review, update, archive, restore, forget, or explain local memories through the CLI without requiring MCP.