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Wiki Audit

Community
bozhouDev
wiki-audit

Use when fact-checking a single wiki page against its cited sources — verifies that every footnote actually supports its claim and surfaces uncited factual claims. Run after ingesting a high-stakes page or any time you want confidence in one page's accuracy.

Overview

PublisherbozhouDev
Repositorybozhou-skills
Skill namewiki-audit
Stars
78
Forks
7
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bozhouDev on GitHub. Read the source before you install it.

Installation

Install the Wiki Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bozhouDev/bozhou-skills/tree/main/wiki-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bozhouDev/bozhou-skills.git /tmp/bozhou-skills
mkdir -p .claude/skills
cp -r /tmp/bozhou-skills/wiki-audit .claude/skills/wiki-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wiki Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wiki Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wiki Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wiki Audit

Verify a single wiki page against its cited sources. Two phases: detect uncited factual claims, then verify cited claims by dispatching one subagent per source in parallel.

Pre-condition

Find SCHEMA.md (search from cwd upward, or ~/wikis/). If not found, tell the user to run wiki-init first. Read SCHEMA.md for the wiki root path and the Citations section (the rules and footnote format the audit enforces).

If SCHEMA.md has no Citations section (older wiki, initialized before this skill existed): use the fallback convention below for this run, and offer at the end to append the Citations section to SCHEMA.md so future operations stay consistent.

Cite every non-common-knowledge factual claim. Granularity is paragraph or claim,
never per-sentence. Format: Markdown footnotes. Two citation kinds:

Quote:     [^N]: <target> <locator> — "<verbatim quote>"
Synthesis: [^N]: <target> <locator> [synthesis] — <what supports the claim>

Three rules:
1. Target is one of: [[source-slug]] (a source-type wiki page), raw/<file> or
   assets/<file> (a local file path), or a URL. Never an entity / concept /
   analysis page.
2. A locator is present (§section, p.N, [HH:MM:SS], URL anchor, dated post).
3. Either a verbatim quote, or the [synthesis] tag plus a description of what
   the cited range supports.

If the user did not name a page, ask which page to audit. Accept slug, filename, or absolute path. Resolve to wiki/pages/<slug>.md. Audit one page per run.

Process

1. Read the target page

Read the full page. Note:

  • The frontmatter sources: list (which sources the page formally cites).
  • All footnote definitions ([^N]: ...) and references ([^N] in body text).

If the page has zero footnotes but contains factual content, that is itself the audit result — every claim becomes a Phase A finding. Still run Phase A; skip Phase B.

2. Phase A — uncited claim detection (1 subagent)

Dispatch one subagent. Give it:

  • The full page contents.
  • The Citations section copied from SCHEMA.md.
  • The page's sources: list.

Task: list every non-common-knowledge factual claim that lacks a footnote. Return a structured list of (line number, claim text, suggested-source-from-the-sources-list-or-"unknown").

The subagent applies the SCHEMA.md "what to cite" rule: paragraph- or claim-level granularity, common knowledge exempt.

3. Phase B — cited claim verification (N subagents, parallel)

For every footnote definition in the page, parse:

  • The target — one of [[source-slug]], a path under raw//assets/, or a URL.
  • The locator (§section, p.N, timestamp, URL anchor, dated post).
  • Either the verbatim quote or the [synthesis] description.

Resolve each target to readable content:

  • [[source-slug]] → read wiki/pages/<source-slug>.md to find the raw file path (in its **Source:** line). Then read that raw file from raw/ or assets/.
  • raw/<file> or assets/<file> → read the file directly.
  • <URL> → check whether a cached copy exists in assets/ (filename derived from URL). If yes, read it. If not, mark the footnote 🚫 source-missing (do not re-fetch — that belongs in the fix step).

Any target that cannot be resolved gets verdict 🚫 source-missing; do not dispatch a subagent for it.

Group resolvable footnotes by their resolved file (multiple footnotes against the same PDF read it once). Dispatch one subagent per file, in parallel using the Agent tool. Each subagent gets:

  • The raw source content (from raw/, assets/, or cached URL).
  • The list of footnotes against that source — for each: number, locator, and either the verbatim quote or the [synthesis] description.
  • The verdict rubric below.

Each subagent returns, per footnote, one verdict and a 1-line note:

  • ✅ supported — quote string-matches the source at the cited locator, or the [synthesis] description honestly summarizes the cited range.
  • ❌ unsupported — quote not found at the cited locator, or the claim is contradicted by the source.
  • ⚠️ partial — quote is paraphrased rather than verbatim (and lacks the [synthesis] tag), or the synthesis description overstates the cited range.

For ❌ and ⚠️, the note must include what the source actually says, so the user can decide how to fix.

Why per-source, not per-footnote: PDFs are expensive to read. One read of a 30-page paper for five footnotes beats five reads.

4. Write the audit report

Always write — do not ask permission. Path: wiki/pages/audit-<page-slug>-<today>.md.

markdown
---
title: Audit Report — <page-slug> — <today>
tags: [audit, maintenance]
sources: []
updated: <today>
---

# Audit Report — [[<page-slug>]] — <today>

## Summary
- Cited claims verified: N
- ✅ Supported: N    ❌ Unsupported: N    ⚠️ Partial: N    🚫 Source missing: N
- 🆘 Uncited factual claims: N

## 🆘 Uncited Claims (Phase A)
- Line 42: "Transformers replaced LSTMs as the default sequence model."
  Suggested source: [[attention-is-all-you-need]] or unknown
  Fix: add footnote, weaken claim, or remove

## ❌ Unsupported (Phase B)
- [^3]: claims "8 attention heads with 128 dims each"
  Source says: "h = 8 parallel attention layers ... d_k = d_v = 64"
  Fix: correct the dimension to 64

## ⚠️ Partial (Phase B)
- [^7]: [synthesis] description says "compares to RNNs across 4 benchmarks"
  Source range covers 2 benchmarks, not 4. Tighten the description.

## 🚫 Source Missing
- [^5]: https://example.com/post — no cached copy in assets/
  Fix: re-fetch source, or remove citation

## ✅ Supported
- [^1], [^2], [^4], [^6], [^8] — all verified

Add the report to wiki/index.md under the Maintenance category (create the category if it does not yet exist — wiki-lint uses the same category).

5. Offer concrete fixes

For each non-empty category, offer one at a time:

  • 🆘 Uncited: "Search the page's sources: list for support and propose footnotes for these claims?"
  • ❌ Unsupported: "Apply the corrections shown in the report? (I'll show diffs first.)"
  • ⚠️ Partial: "Tighten the synthesis descriptions, or add the [synthesis] tag where missing? (diffs first.)"
  • 🚫 Source missing: "Re-fetch URLs / locate misplaced PDFs back into raw/?"

Apply only after user confirmation. Show the exact diff before each write.

6. Append to wiki/log.md

Always append — do not ask permission:

## [<today>] audit | [[<page-slug>]] — N supported, N unsupported, N partial, N uncited
Report: [[audit-<page-slug>-<today>]]
Fixed: <list, or "none">

7. Report to user

  • Audit report: wiki/pages/audit-<page-slug>-<today>.md
  • One-line verdict (e.g. "5/8 cited claims verified, 2 uncited claims found")
  • Whether any fixes were applied

Frequently asked questions

What does the Wiki Audit AI skill do?

Use when fact-checking a single wiki page against its cited sources — verifies that every footnote actually supports its claim and surfaces uncited factual claims. Run after ingesting a high-stakes page or any time you want confidence in one page's accuracy.

Why use Wiki Audit on TypingMind?

Because you install it once and use it with any model. Wiki Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wiki Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bozhouDev/bozhou-skills/tree/main/wiki-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wiki Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wiki Audit?

As many as you like. As long as a model supports skills, you can use Wiki Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wiki Audit AI skill free?

It is published on GitHub by bozhouDev. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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