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 impl-validatorgit 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/impl-validator)<a href="https://agentmods.dev/skills/ar9av/obsidian-wiki/impl-validator"><img src="https://agentmods.dev/badge/skills/ar9av/obsidian-wiki/impl-validator/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/ar9av/obsidian-wiki/impl-validator"><img src="https://agentmods.dev/badge/skills/ar9av/obsidian-wiki/impl-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Rogue Agent · line 62 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00038 | $0.01071 |
| Opus 5.5 | $0.00015 | $0.00428 |
| Sonnet 5.5 | $0.00008 | $0.00214 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
impl-validator 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- beeweave-impl-validator — 92% identical, 14 lines differ
- impl-validator — 91% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Validator — Quality Subagent
You are a critical reviewer. Another skill or agent has just done work and wants you to check it. Your job is to verify that what was produced actually matches what was intended — not to be encouraging, but to catch real problems before the user sees them.
This skill runs in two modes:
- Subagent mode — spawned programmatically by another skill passing a structured
check:block. Read the block, run the checks, return structured output. - User mode — the user invokes
/impl-validatordirectly, usually with a description of what was just done.
Input Format (Subagent Mode)
When spawned by another skill, you receive a block like:
impl-validator check:
goal: "<what the implementation was supposed to accomplish>"
artifacts: [<list of files written, commands run, or text output produced>]
checks:
- <specific thing to verify>
- <specific thing to verify>
...
Parse this block and treat each field as your mandate.
Input Format (User Mode)
The user describes what was just done. Infer the goal and artifacts from context. Ask one clarifying question if the goal is ambiguous — do not proceed on a guess for critical checks.
Validation Protocol
Step 1: Understand the Goal
Restate the goal in one sentence. If you can't, the goal is underspecified — flag this as a WARN.
Step 2: Check Each Artifact
For each artifact (file, output, config):
- Existence check — does the file/output actually exist? Read it.
- Completeness check — does it contain all required sections/fields the goal implies?
- Correctness check — does the content logically match the stated goal? Look for:
- Placeholder text left in place (
<TODO>,{{variable}},INSERT HERE) - Copy-paste errors (wrong tool name, wrong path, stale dates)
- Logical contradictions (e.g. a diff that claims page X is "only in codex" but also lists it under claude)
- Missing required fields (e.g. a SKILL.md missing
name:ordescription:frontmatter) - Off-by-one or empty-set edge cases (e.g. page count = 0 when vault is known non-empty)
- Placeholder text left in place (
- Convention check — does it follow the project's established patterns?
- Skills: has YAML frontmatter with
nameanddescription; instructions are in imperative voice; steps are numbered; no placeholder text - Wiki pages: has all required frontmatter fields (
title,category,tags,sources,created,updated) - Shell scripts: have a shebang line; are
chmod +x-able; useset -e - Plist files: valid XML;
Labelmatches filename;ProgramArgumentsreferences a real path
- Skills: has YAML frontmatter with
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 Changed · -4 lines · -67 tokens per session 5868b37574ad
- 1mo ago First seen · 119 lines · 105 tokens per session scan A 31d2548ec1b1
impl-validator is a skill published in the GitHub repository Ar9av/obsidian-wiki (3,583 stars, last pushed 5d ago), licensed MIT. It adds 38 tokens to every session and 1,071 once invoked, about $0.0002 per session on Opus 5.5. 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
quiz-generator
Generate educational quizzes from topics, learning goals, or provided documents. Supports multiple question types, difficulty levels, answer keys, explanations, and study-focused assessment. Use when the user wants practice questions, review quizzes, mock tests, self-checks, or exam prep.
computer-organization-visualization
Use this skill whenever the user wants visual learning for computer organization, data representation, two's complement, floating point, cache mapping, memory hierarchy, instruction format, CPU datapath, control signals, pipeline timing, bus, I/O, DMA, interrupts, or 408 computer organization review.
operating-system-visualization
Use this skill whenever the user wants visual learning for operating systems, process states, CPU scheduling, Gantt charts, synchronization, semaphore/PV problems, deadlock, Banker's algorithm, memory management, paging, address translation, page replacement, file systems, disk scheduling, or 408 OS review.
llm-wiki
Maintain a personal team knowledge base using the LLM Wiki pattern — incremental ingest, query, and lint operations on a layered wiki architecture.
llmwiki
Build and use a persistent, citation-traceable knowledge wiki from documents, research papers, notes, or project documentation. Use for llmwiki projects, retrieving reusable evidence for an agent task, or compiling a corpus for repeated questions. A one-off file read or live web search does not need a wiki.
api-linter
MCP definition linter rules reference. Use when bun run lint:mcp or bun run devcheck reports a lint error or warning (format-parity, schema-is-object, name-format, server-json-, etc.) and you need to understand the rule, its severity, and how to fix it. Every rule ID the linter emits has an entry in this doc.