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Web Content Audit

Community
kevintsai1202
web-content-audit

Use this skill when you need to verify cross-file data/asset consistency in a content-driven site — not "does it render?" (that's `web-visual-verification`) but "do the data, files, and references all line up?". Triggers on phrases like "盤點內容", "稽核資產", "對照 course-data 跟 markdown", "找缺圖", "task ID 有沒有重複", "quiz 編號 vs 硬編碼總數", "audit", "content audit", "content drift", "asset coverage", "three-way sync check", "cross-file consistency", "find missing illustrations", "資料一致性檢查", "找出該補插圖的地方", or any moment when the user senses divergence between source files and deployed data. Output is a human-readable markdown report, not a pass/fail. Pair with `web-visual-verification` for full pre-release coverage.

Overview

Publisherkevintsai1202
Repositoryteaching-site-skills
Skill nameweb-content-audit
Stars
52
Forks
10
Bundled files
Instructions only
LicenseMIT
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 kevintsai1202 on GitHub. Read the source before you install it.

Installation

Install the Web Content 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/kevintsai1202/teaching-site-skills/tree/main/web-content-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/kevintsai1202/teaching-site-skills.git /tmp/teaching-site-skills
mkdir -p .claude/skills
cp -r /tmp/teaching-site-skills/web-content-audit .claude/skills/web-content-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Web Content 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 Web Content 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 Web Content 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.

Web Content Audit

Schema authority: cross-file consistency rules (3-place sync for materials, task ID stability, quiz renumber trap) are codified in _shared/domain-primitives.md §13. Audit scripts in this skill operationalise those rules.

Filename convention (English-first): audit reports land under data/audit-*.md; scripts under scripts/audit-*.mjs.

This skill produces audit scripts that read source files and deployed data, compare them, and emit a human-readable report. The goal is not to gatekeep deployment (that's verification's job) — it's to surface drift that humans should review.

When to Invoke

  • Before a major content release (catch outline ↔ course-data ↔ visuals drift before students see it).
  • After adding/removing units / materials / quiz items (re-check the three-place sync rule).
  • Before handing off a corporate edition to a client (confirm the condensed COURSE matches its source).
  • When the user senses "something's off" but can't see a runtime bug — audit is for the invisible drift.
  • When planning the next iteration (e.g. "which sections need new illustrations?").

Do NOT invoke when the question is "does the page render correctly?" — that's web-visual-verification.

Audit vs Verify — Operating Mode Difference

TraitAuditVerify
OutputMarkdown report (human reads)Pass/fail (CI gates)
ToolsFile reads, regex, JSON diffs, optionally PlaywrightPlaywright + asserts
Failure modeAlways exits 0; report tells human what's worth fixingExits non-zero on bad state
When runManually, at milestonesOn every PR / pre-deploy
Mental model"Show me what's drifted""Don't let this regress"

If your script ends with assert.* or process.exit(1), it's not an audit — move it to web-visual-verification.

The Five Audit Categories

Audit 1: Cross-Artifact Reference Resolution

The classic teaching-site bug: course-data.js:materials[] references a file the disk doesn't have, or getMaterialUrl() is missing a rule.

js
// scripts/audit-material-references.mjs
import fs from 'node:fs/promises';
import path from 'node:path';

// Read course-data.js (via vm or regex extract)
const COURSE = await loadCourse('./course-data.js');

const report = ['# Material reference audit\n'];
for (const m of COURSE.materials) {
  // 1. Does the file router know about it?
  const routerCovers = await checkRouterCoverage(m.name);
  // 2. Does the actual file exist?
  const fileExists = await checkFileExists(m);
  // 3. Is it referenced in any unit's materials[]?
  const referencedInUnit = findReferencingUnits(COURSE, m.id);

  if (!routerCovers || !fileExists || referencedInUnit.length === 0) {
    report.push(`## ⚠️ ${m.id}: ${m.name}`);
    report.push(`- Router: ${routerCovers ? '✅' : '❌'}`);
    report.push(`- File: ${fileExists ? '✅' : '❌'}`);
    report.push(`- Referenced in units: ${referencedInUnit.join(', ') || '❌ NONE'}`);
  }
}
await fs.writeFile('data/audit-materials.md', report.join('\n'));

The output goes to data/audit-*.md; the human opens it and decides what to fix.

Audit 2: Asset Coverage / Gap Analysis

"Which sections still have no illustration?" — guide future work, don't fail anything.

js
// scripts/audit-illustrations.mjs
// Walks every .chapter / .accordion-content; counts text length, image count, list density
// For each section: if (textLength > 500 && imageCount === 0) → candidate for illustration
// Output: data/illustration-audit.md (a markdown list with section titles + screenshots)

This may use Playwright for screenshots (capture mode — no assertions) but the product is the gap list, not the screenshots. Don't conflate this with web-visual-verification's capture-*.mjs which has no analytical output.

Audit 3: Inlined Data ↔ Source Drift (Corporate Edition)

When window.COURSE is inlined in index.html (corporate edition), it can drift from the source files it was condensed from. Audit the diff:

js
// scripts/audit-corp-content.mjs
const inlined = await loadCourseFromIndexHtml('corporate-editions/client_6h/index.html');
const source = await loadSourceMarkdown('course-package/');

const report = ['# Corporate edition content audit\n'];

// Compare meta
report.push(`## Meta`);
report.push(diff(inlined.meta, source.meta));

// Per-unit: deliverables / prompts / tasks count
for (const day of ['day1', 'day2']) {
  for (const unit of inlined[day].units) {
    report.push(`## ${unit.id}: ${unit.title}`);
    report.push(`- Prompts: ${unit.prompts?.length || 0}`);
    report.push(`- Tasks: ${unit.tasks?.length || 0}`);
    report.push(`- Materials: ${unit.materials?.length || 0}`);
  }
}
await fs.writeFile('data/audit-corp-content.md', report.join('\n'));

The example workshop's audit-corp-6h-content.mjs does exactly this — produces a markdown dump for the instructor to skim before each corporate session.

Audit 4: ID Stability & Reuse

task.id, quiz.id, unit.id are stable contracts (localStorage keys, internal references). Audit them:

js
// scripts/audit-id-stability.mjs
// 1. Collect all task IDs across course-data.js — check no duplicates
// 2. Read git log for course-data.js — flag any task ID that was renamed (not just deleted)
// 3. Cross-check quiz[N].sourceUnit references actual unit IDs
// 4. Cross-check materials[].id appears in at least one unit's materials[]

This is the audit that catches "we accidentally renamed d2-u3-t1 to d2-u3-task1" — silent localStorage-progress-wipe across all students.

Audit 5: Produced Artifact Inspection

When the pipeline produces a binary deliverable (DOCX, PDF, zip), audit its inner structure to catch broken builds early.

js
// scripts/inspect-docx-images.mjs
import JSZip from 'jszip';
const zip = await JSZip.loadAsync(await readFile('dist/ebook.docx'));
const images = Object.keys(zip.files).filter(f => f.startsWith('word/media/'));
console.log(`📷 內嵌圖片:${images.length} 張`);
for (const f of images) {
  const size = zip.files[f]._data?.uncompressedSize || 0;
  console.log(`   ${f}  ${(size / 1024).toFixed(1)} KB`);
}

If you expect ~30 images and the DOCX has 3, something in the build went wrong silently. This is faster than opening Word and scrolling.

Output Convention

data/
├── audit-materials.md            ← Audit 1
├── audit-illustrations.md         ← Audit 2 (gap analysis)
├── audit/section-*.png            ←   ↳ supporting screenshots
├── audit-corp-content.md          ← Audit 3
├── audit-id-stability.md          ← Audit 4
└── audit-ebook-inspection.txt     ← Audit 5

All audit outputs are versionable markdown (or JSON). Commit them at release milestones; the diff between releases tells you "what content actually changed".

Format the Report for Human Skimming

Auditors will skim, not read. Structure for skimming:

markdown
# {Audit name} — {timestamp}

## Summary
- ✅ 24 of 30 materials fully wired
- ⚠️ 4 materials missing router rules
- ❌ 2 materials referenced but file missing

## ⚠️ Warnings (need attention but not blocking)
...

## ❌ Errors (likely broken in production)
...

## ℹ️ Notes (FYI)
...

Lead with the summary and emoji-prefix every section. The audit's job is to be the world's most efficient code review.

The "Three-Place Sync" Audit (Critical for Teaching Sites)

The pattern from static-spa-conversion: every material must appear in (1) filesystem, (2) course-data.js:materials[], (3) getMaterialUrl() router. Audit script:

js
const fsFiles = new Set(await fs.readdir('course-package/materials/'));
const dataMaterials = new Set(COURSE.materials.map(m => routerNameToFile(m.name)));
const routerCoverage = extractRouterRules(indexHtmlContent);

const inFsNotInData = [...fsFiles].filter(f => !dataMaterials.has(f));
const inDataNotInFs = [...dataMaterials].filter(f => !fsFiles.has(f));
const inDataNotInRouter = [...dataMaterials].filter(f => !routerCoverage.has(f));

// Emit a 3-column markdown table showing each file's coverage in each of the three places.

This single script catches 90% of "the material link is broken" bugs.

Audit Doesn't Block — It Informs

The strongest discipline: audit scripts always exit 0, even when they find problems. Why:

  • Audits run regularly; you don't want CI red because of a known "we'll fix in next release" issue.
  • Audits surface judgement calls (e.g. "section has no illustration — is that intentional?") that automation shouldn't decide.
  • A failing audit feels like a verify; humans then treat audits as gates and the report falls into "didn't read it" hell.

If an issue MUST block release, promote it to a verify script (web-visual-verification) with assert.*. Audits inform; verifies gate.

When to Audit (Cadence)

TriggerAudits to run
Adding/removing a unitid-stability, material-references
Adding a material filematerial-references (esp. the three-place sync)
Renumbering quizid-stability + manual check of hardcoded (N題) strings
Before corporate edition handoffcorp-content (inline ↔ source) + material-references
Before publishing ebookinspect-docx-images / inspect-pdf-pagecount
Quarterly maintenanceillustrations (gap analysis) → plan next content cycle

Anti-Patterns

  • Audit scripts with assert.* / non-zero exits — that's a verify, not an audit. Move it.
  • Audit reports that nobody reads — keep them short, lead with summary counts, use emojis aggressively for scanability.
  • One audit doing five jobs — split. audit-materials.mjs and audit-illustrations.mjs produce different reports to different folders for different decisions.
  • Running audits in CI as if they were verifies — they become noise, get muted, become useless. Run on demand or at release milestones.
  • No timestamp / version in the report — when comparing two audit runs, you need to know which is newer.

Hand-off

When this skill finishes:

  • A set of audit-*.mjs (and supporting inspect-*.mjs) scripts is in scripts/.
  • Their outputs land in data/audit-*.md (or .json for tooling consumption).
  • A README section in the project explains "how to read an audit report" (link to the convention above).
  • The user knows to run audits at release milestones, not on every commit.

Frequently asked questions

What does the Web Content Audit AI skill do?

Use this skill when you need to verify cross-file data/asset consistency in a content-driven site — not "does it render?" (that's `web-visual-verification`) but "do the data, files, and references all line up?". Triggers on phrases like "盤點內容", "稽核資產", "對照 course-data 跟 markdown", "找缺圖", "task ID 有沒有重複", "quiz 編號 vs 硬編碼總數", "audit", "content audit", "content drift", "asset coverage", "three-way sync check", "cross-file consistency", "find missing illustrations", "資料一致性檢查", "找出該補插圖的地方", or any moment when the user senses divergence between source files and deployed data. Output is a human-re...

Why use Web Content Audit on TypingMind?

Because you install it once and use it with any model. Web Content 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 Web Content Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kevintsai1202/teaching-site-skills/tree/main/web-content-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Web Content 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 Web Content Audit?

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

Is the Web Content Audit AI skill free?

Yes. It is published on GitHub by kevintsai1202 under the MIT license. 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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