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 skills add Ar9av/obsidian-wiki --skill wiki-agentgit 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-agent)<a href="https://agentmods.dev/skills/ar9av/obsidian-wiki/wiki-agent"><img src="https://agentmods.dev/badge/skills/ar9av/obsidian-wiki/wiki-agent.svg" alt="Measured on agentmods" height="20"></a>- Socket pass
- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 130 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
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 | $0.00190 | $0.03710 |
| Opus 5 | $0.00095 | $0.01855 |
| Sonnet 5 | $0.00038 | $0.00742 |
| Haiku 4.5 | $0.00019 | $0.00371 |
Grade A, and why
wiki-agent 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 8d 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 — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Agent — Targeted Cross-Agent History Search + Ingest
You are doing a query-driven targeted ingest from one specific AI agent's raw conversation history. The user is typically working in a different agent right now and wants to pull in context from another agent's past sessions.
This is not bulk ingest. You find sessions about a specific topic, extract the relevant blobs, distill them into the wiki, and return a synthesized answer the user can act on immediately.
Command Routing
Parse the invocation to determine the target agent and optional query:
| Command | Target | Example |
|---|---|---|
/wiki-claude [query] |
Claude Code history | /wiki-claude "how did I set up auth middleware" |
/wiki-codex [query] |
Codex CLI history | /wiki-codex "rust ownership patterns" |
/wiki-hermes [query] |
Hermes agent history | /wiki-hermes "memory architecture" |
/wiki-openclaw [query] |
OpenClaw history | /wiki-openclaw "project planning approach" |
/wiki-copilot [query] |
Copilot chat history | /wiki-copilot "test strategy for API routes" |
/wiki-pi [query] |
Pi agent history | /wiki-pi "how did I refactor the auth module" |
If no query is given, default to recent sessions mode: ingest the last 5 unprocessed sessions from that agent and return a summary of what was found. This is equivalent to a focused wiki-history-ingest for that agent only.
Before You Start
Writing profile: Before drafting or rewriting natural-language Markdown, read and apply the Writing Profile Resolution section in llm-wiki/SKILL.md. Framework schema, provenance, safety, and operation-specific requirements take precedence.
WRITING.md preferences apply only to newly drafted or rewritten natural-language Markdown; preserve source content and structured records.
- 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. - Read
$OBSIDIAN_VAULT_PATH/.manifest.json→ know what's already ingested. - Read
$OBSIDIAN_VAULT_PATH/hot.mdif it exists → warm context on recent wiki activity.
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.
- 8d ago First seen · 323 lines · 190 tokens per session scan A 48644fd9dd48
wiki-agent is a skill published in the GitHub repository Ar9av/obsidian-wiki (3,364 stars, last pushed yesterday), licensed MIT. It adds 190 tokens to every session and 3,710 once invoked, about $0.0010 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
knowledge-base-management
A lifecycle system for managing an Obsidian knowledge base, which is a folder of linked notes. It organizes raw material, AI-maintained wiki pages, and generated views into separate layers.
llm-wiki
Maintain a personal team knowledge base using the LLM Wiki pattern — incremental ingest, query, and lint operations on a layered wiki architecture.
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).
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.
link-retrieve
Use before answering work that may depend on user memory, project history, source-backed notes, or prior decisions; retrieve compact Link context through the CLI without loading the whole wiki or requiring MCP.
link-ingest
Use when raw files are present, source pages look stale, or a user asks to ingest notes into Link; refresh source-backed wiki pages, propose memories, and validate updates through the CLI without MCP.