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Website Audit

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XuanRanL
website-audit

Full website SEO audit — crawls up to 500 pages, detects business type, spawns up to 15 parallel specialist agents (8 always + 7 conditional), generates SEO Health Score (0-100) with enterprise HTML report. Completely standalone — not connected to the blog-writer pipeline. Triggers on: audit my site, website audit, full SEO check, site health, analyze my website, SEO analysis, technical SEO audit, or any URL + audit intent. Use whenever user mentions 'audit', 'check my site', 'SEO health', 'website analysis', or provides a URL asking for SEO evaluation.

Overview

PublisherXuanRanL
Repositoryloamwright-SEO-Skill
Skill namewebsite-audit
Stars
52
Forks
9
Bundled files
Instructions only
LicenseApache-2.0
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 XuanRanL on GitHub. Read the source before you install it.

Installation

Install the Website 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/XuanRanL/loamwright-SEO-Skill/tree/main/skills/website-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/XuanRanL/loamwright-SEO-Skill.git /tmp/loamwright-SEO-Skill
mkdir -p .claude/skills
cp -r /tmp/loamwright-SEO-Skill/skills/website-audit .claude/skills/website-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Website 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 Website 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 Website 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.

Full Website SEO Audit

Standalone website auditing tool. URL in → crawl → analyze → report out. No dependency on the blog-writer pipeline, project system, or article creation flow.

Invocation

/website-audit https://example.com
/website-audit https://example.com --max-pages 100

Process

Phase 1: INITIALIZE

  1. Validate URL:
bash
python -c "from scripts._core.ssrf_guard import validate_url, SSRFError; import sys
try:
    validate_url('$URL', allow_http=True); print('OK')
except SSRFError as e:
    print(f'BLOCKED: {e}'); sys.exit(1)"

⚠️ validate_url() signals success by RETURNING a truthy ValidatedURL and failure by RAISING SSRFError. The old snippet here did err = validate_url(...); print(err or 'OK'), which prints the success object and can never print OK — and that inverted reading got copied into verify_backlinks.py, where it marked every valid backlink "blocked" and silently disabled link verification entirely. Never treat the return value as an error indicator. 2. Create output directory: ./Website Audit/seo-audit-{domain}-{YYYY-MM-DD}/ ⚠️ The Website Audit/ parent already exists and holds every prior audit — write there, NOT in the repo root. This was violated on 2026-08-06 (audit-client.example) and cost a 149 MB directory move plus an artifact URL rescue. Create the full path before crawling, not after. 3. Show cost estimate to user (based on detected page count):

  • 8 core agents (Opus): ~$8
  • With conditional agents: ~$15 max
  • Await user confirmation if over $10

Phase 2: CRAWL

bash
python -m scripts.audit.crawl_site "$URL" --max-pages 500 --concurrent 5 --delay 1.0 --output ./"Website Audit"/seo-audit-{domain}-{date}/crawl-results.json

Configuration:

  • Max pages: 500 (user-overridable via --max-pages)
  • Respect robots.txt: Yes
  • Follow redirects: Yes (max 3 hops)
  • Timeout per page: 30 seconds
  • Concurrent requests: 5
  • Delay between requests: 1 second

Phase 3: DETECT BUSINESS TYPE

bash
python -m scripts.audit.business_type_detector ./"Website Audit"/seo-audit-{domain}-{date}/homepage.html --url "$URL" --json > ./"Website Audit"/seo-audit-{domain}-{date}/business-type.json

Business types: SaaS | E-commerce | Local (brick-and-mortar/SAB/hybrid) | Publisher | Agency | Other

Phase 4: SPAWN SUBAGENTS (parallel)

Always spawn (8 agents):

AgentResponsibilityKey Scripts
audit-technicalCrawlability, indexability, security, URL structure, JS renderingcrawl_site, fetch_page, parse_html
audit-contentE-E-A-T scoring (80 items), readability, thin content, YMYL detectioncore_eeat_scorer
audit-schemaJSON-LD detection, validation, deprecated type veto (T09)schema_validator
audit-sitemapXML sitemap structure, coverage gaps, quality gatesfetch_page
audit-performanceLCP, INP, CLS via PageSpeed Insights + CrUX field datapagespeed_check
audit-visualDesktop + mobile screenshots, above-fold analysis, responsive checkcapture_screenshot, render_probe
audit-geoAI crawler access, llms.txt, citability (134-167w optimal), brand signalsfetch_page
audit-sxoSERP backwards analysis, page-type mismatch detection, persona scoringfetch_page, parse_html

Conditionally spawn (7 agents):

AgentConditionResponsibility
audit-localbusiness_type ∈ {local_*}GBP signals, NAP consistency, reviews, local schema
audit-mapslocal detected AND geoapify/dataforseo key availableGeo-grid, competitor radius
audit-googleGoogle API key configuredCrUX field data, GSC indexation, GA4 traffic
audit-backlinksAlways (CC free); enriched if Moz/Bing keysDA/PA, referring domains, toxic links
audit-cluster/blog detected OR pillar signalsSemantic clustering, cannibalization
audit-driftPrevious baseline exists for this domain17-rule drift comparison
audit-ecommercebusiness_type == "ecommerce"Product schema, marketplace intel

Each agent writes results to: ./"Website Audit"/seo-audit-{domain}-{date}/modules/{name}.json

A negative rendering claim needs a time-series, and a real browser to settle ties

Never rate a "does not render / shows zero / is blank / is missing" finding above medium from a single screenshot. On 2026-08-06 two such findings shipped as critical on audit-client.example and both were wrong — a headless capture had caught a slow fadeInUp animation before it started, and that frame was read as the final state.

Measured on that page: headless, 10s wait, 6 runs → 5 never started; headed, 10s wait, 3 runs → all animated, 1 still mid-flight. So the failure mode is a single capture landing mid-animation, not "headless is broken" — sampling over time is the fix, and the real browser is the tie-breaker.

bash
python -m scripts.audit.render_probe "$URL" --selector "[data-to-value]" --selector ".hero h1" --check

It reports three columns that must never be collapsed into one claim:

ColumnAudienceIf content is missing here
static (no JS)text-extracting crawlers, AI fetchers, link previewsreal finding — rate for machine readability
headless samplessome render services, screenshot botscorroborating signal
headed sampleshuman visitors — the ground truthonly this justifies a UX/visual severity

--check exits non-zero when headless and headed disagree, i.e. when any headless-derived finding would be unsafe to publish. A static_unavailable result (403/WAF/network) is reported as unavailable, never as empty — a transport failure is not a content verdict.

Phase 5: SCORE

bash
python -m scripts.audit.audit_scorer ./"Website Audit"/seo-audit-{domain}-{date}/modules/ --json > ./"Website Audit"/seo-audit-{domain}-{date}/scores.json

Scoring Weights:

CategoryWeightContributing Modules
Technical SEO22%technical, sitemap, backlinks (authority)
Content Quality23%content, cluster (bonus)
On-Page SEO20%sxo, visual (partial), local (bonus)
Schema / Structured Data10%schema, ecommerce (if present)
Performance (CWV)10%performance, google (field data)
AI Search Readiness10%geo
Images5%visual (image portion)

Phase 6: VALIDATE

bash
python -m scripts.audit.validate_audit_report ./"Website Audit"/seo-audit-{domain}-{date}/ --json

Pre-delivery checks: all core modules reported, no empty sections, score math correct.

Phase 7: REPORT

bash
python -m scripts.audit.report_generator ./"Website Audit"/seo-audit-{domain}-{date}/ --output ./"Website Audit"/seo-audit-{domain}-{date}/FULL-AUDIT-REPORT.md
python -m scripts.audit.report_html ./"Website Audit"/seo-audit-{domain}-{date}/ --output ./"Website Audit"/seo-audit-{domain}-{date}/FULL-AUDIT-REPORT.html

Generate ACTION-PLAN.md from findings sorted by severity.

Branding — MANDATORY on every client-facing report

Audit reports are Loamwright SEO (沃匠 SEO) lead-generation assets, not neutral technical documents. Loamwright is the operator's OWN agency. Every HTML report ships with its branding.

Read the brand facts — never write them from memory:

bash
python -c "import json;d=json.load(open('projects/loamwright/business-context.json',encoding='utf-8'));print(json.dumps({k:d[k] for k in ['brand_name_full','site_url','company','article_signature']},ensure_ascii=False,indent=1))"
grep -oE '\-\-xr-[a-z-]+:\s*#[0-9a-fA-F]{3,8}' projects/loamwright/brand/article-css.css

Required elements (the 2026-08-06 audit-client.example report is the reference implementation — copy its structure):

  1. Top brand bar — wordmark + positioning line + CTA link to https://loamwrightseo.com/contact-us/
  2. Masthead — "沃匠 SEO 出品" pill and an 审计方 field
  3. Brand palette — primary #138670, accent #12B76A, link #0B5143. The brand's own #B45309 / #B42318 double as the warn/critical severity colors; don't invent a second palette.
  4. 关于沃匠 SEO section — real proof signals only: 6 years, 100+ brands (incl. publicly listed), 0 account managers, 5.0 rating, 8 disciplines
  5. CTA block — free SEO audit offer (200+ checks, ranked by revenue impact)
  6. Branded footer — site, email, contact URL, and the signature "沃匠 SEO 团队,Lewei Zhang 复核"

Two constraints: the Artifact CSP blocks font CDNs and CJK fonts can't be inlined, so use system font stacks (PingFang SC / Microsoft YaHei) — that is the correct call, not a compromise. And first-person IS on-brand for Loamwright (founder-led), but never fabricate client results or experiences that didn't happen.

Output Files

./"Website Audit"/seo-audit-{domain}-{date}/
  crawl-results.json          # Crawled pages manifest
  business-type.json          # Detected industry + conditional agents
  modules/                    # Per-agent analysis results (JSON)
  scores.json                 # Aggregated 7-category health score
  FULL-AUDIT-REPORT.md        # Comprehensive findings (Markdown)
  FULL-AUDIT-REPORT.html      # Interactive report (self-contained HTML)
  ACTION-PLAN.md              # Prioritized recommendations
  screenshots/                # Desktop + mobile captures

Report Structure

Executive Summary

  • Overall SEO Health Score: {score}/100 [{badge}]
  • Business type detected
  • Pages crawled
  • Top 5 critical issues
  • Top 5 quick wins

Per-Category Sections (7 categories)

Each section includes:

  • Category score with badge (Excellent/Good/Needs Improvement/Poor)
  • Findings table: severity | issue | evidence | recommendation
  • Sub-section detail per analyzed dimension

Action Plan

  • Critical: Blocks indexing or causes penalties (fix immediately)
  • High: Significantly impacts rankings (fix within 1 week)
  • Medium: Optimization opportunity (fix within 1 month)
  • Low: Nice to have (backlog)

Error Handling

ScenarioAction
URL unreachable (DNS/connection)Report error clearly; do not guess content
robots.txt blocks crawlingAnalyze accessible pages; note limitation in report
Rate limiting (429)Back off; report partial results
Timeout on large sitesCap at limit; report findings for crawled pages
Subagent timeout/failureSkip that module; note in report; redistribute weight
No Google API keySkip audit-google; use lab-only CWV data from PSI
No backlink APIsUse Common Crawl only (always available, free)

Reference Files (load on-demand)

  • references/audit/eeat-framework.md — E-E-A-T scoring rubric
  • references/audit/cwv-thresholds.md — Core Web Vitals thresholds
  • references/audit/quality-gates.md — Content minimums per page type
  • references/audit/schema-types.md — Recommended schema + deprecation status
  • references/audit/local-seo-signals.md — Local ranking factors
  • references/audit/backlink-quality.md — Toxic link patterns + anchor benchmarks
  • references/audit/scoring-weights.md — Full scoring methodology
  • references/audit/business-types.md — Detection signal matrix

Cost & Model

  • Default model: inherits from session (Opus)
  • Override: user can say "use sonnet" to reduce cost ~6x
  • Budget guard: cost_ledger enforces limits from ~/.xuanran-seo/config.yaml
  • Estimated cost: $8-15 per full audit (Opus), $1.50-2.50 (Sonnet)

Frequently asked questions

What does the Website Audit AI skill do?

Full website SEO audit — crawls up to 500 pages, detects business type, spawns up to 15 parallel specialist agents (8 always + 7 conditional), generates SEO Health Score (0-100) with enterprise HTML report. Completely standalone — not connected to the blog-writer pipeline. Triggers on: audit my site, website audit, full SEO check, site health, analyze my website, SEO analysis, technical SEO audit, or any URL + audit intent. Use whenever user mentions 'audit', 'check my site', 'SEO health', 'website analysis', or provides a URL asking for SEO evaluation.

Why use Website Audit on TypingMind?

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

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

Which AI models can use Website 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 Website Audit?

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

Is the Website Audit AI skill free?

Yes. It is published on GitHub by XuanRanL under the Apache-2.0 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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