seo-content-audit

Example output: [examples/seo-content-audit-stripe-rate-limiters-20260514/VERDICT.md](../../examples/seo-content-audit-stripe-rate-limiters-20260514/VERDICT.md)

Content Quality Audit

Score an existing piece of content against modern E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and CITE (Clear answer, Include primary stats, Timestamp, Entity authority) rubrics. Surface veto items that block publication regardless of overall score. Produce a clear publish / publish-with-fixes / no-publish verdict with the top 5 fixes.

Prerequisites

• SE Ranking MCP server connected.
• Claude's WebFetch tool available.
• User provides: (a) the URL of an existing piece of content (or pasted content + intended URL), (b) target keyword the content is meant to rank for. Optional: target country (default us).

Process

1. Fetch content WebFetch (always) + mcp__firecrawl-mcp__firecrawl_scrape (when available)
2. Validate target & preflight. See skills/seo-firecrawl/references/preflight.md for the canonical 3-stage preflight (credit balance, Firecrawl availability, Google APIs). Skill-specific notes:
3. Estimated SE Ranking cost for this skill: ~10–15 credits typical (AIO context + AIO prompt sampling for the target keyword + audited URL).
4. Firecrawl: optional with WebFetch fallback, 1 Firecrawl credit per URL audited (default cap 50 URLs, hard cap 200). Surface the projected Firecrawl credit count before continuing. Pass --no-firecrawl to force WebFetch-only inspection (lower-confidence veto checks; see step 4 caveat).
5. Google APIs: tier 2 (GA4 available) unlocks step 3b (GA4 organic traffic on the audited URL) after the AIO context step. See skills/seo-google/references/cross-skill-integration.md § "seo-content-audit" for the full recipe.
6. WebFetch first (free, instant): pull the markdown for word count, H-tag hierarchy, source citations (links to authorities, numbered references), images, tables, code blocks, comment thread.
7. Page-type detection. From the URL pattern, H1 phrasing, and JSON-LD @type, classify the page as one of: ultimate guide / pillar, how-to, listicle / best-of, comparison (X vs Y), explainer, review (single product), landing page (commercial). Look up the corresponding word-count floor from references/core-eeat.md → "Word-count floors by page type". Surface the detected type, the applied floor, and the actual word count in evidence/01-content-snapshot.md. If the actual word count is materially below the floor, flag it for the depth E-E-A-T items (auto ✗ unless the auditor justifies the exception).
8. Firecrawl second — recovers what WebFetch's markdown loses:
9. From metadata: canonical URL, robots, lang, og:title.
10. From the returned html: every <script type="application/ld+json"> block. Parse for Article / BlogPosting schema and extract author (name, @type: Person, optional url + sameAs), datePublished / dateModified (ISO 8601), publisher, mainEntityOfPage. Detect Person schema standalone if present.
11. DOM-level byline detection: locate the structural byline (<a rel="author">, <meta name="author">, <span class="byline">, [itemprop="author"]). Distinguish a real byline element from prose mentions ("Written by Jane in collaboration..." in body text is not a byline; <a rel="author">Jane Doe</a> is).
12. If Firecrawl unavailable: WebFetch portion runs unchanged. Mark schema-type detection and structural byline detection as (skipped — Firecrawl required) in evidence/01-content-snapshot.md. Step 4's veto checks #1 and #4 fall back to prose-level inspection (less reliable) — surface that caveat in VERDICT.md.

1. AIO context DATA_getAiOverview and DATA_getAiOverviewLeaderboard
2. For the target keyword: is there an AIO?
3. Who is cited in the AIO?
4. Is the candidate URL cited?
5. What patterns characterise the cited sources (publication tier, freshness, structure)?

1. AIO prompt sampling DATA_getAiPromptsByTarget
2. Sample LLM prompts where the target URL's domain appears as a source.
3. Cross-reference with the candidate URL — does it show up in any sampled prompts?

3b. GA4 organic traffic on the audited URL (only if google-api.json is present, tier ≥ 2)

• Replaces the implicit traffic estimation with actual measured organic sessions for the audited URL.
• Pull the top organic landing pages (last 28 days):

python3 scripts/ga4_report.py --report top-pages --days 28 --json

• Filter the result client-side for the audited URL's path. Surface in VERDICT.md "## Snapshot" alongside the existing AIO citation cross-check:
• GA4 organic last 28d: {sessions} sessions / {users} users / avg engagement {n}s
• If the URL doesn't appear in the top-100 organic landing pages: "GA4: not in top-100 organic landing pages last 28d — low or zero traffic."
• This is a signal, not a veto. Low GA4 traffic on a YMYL page with high E-E-A-T is informative ("we're not earning the visibility our content quality should support") but doesn't change the publish decision.
• See skills/seo-google/references/cross-skill-integration.md § "seo-content-audit" for the full recipe.

1. Score E-E-A-T using references/core-eeat.md
2. 60-item rubric across 4 dimensions (15 items each).
3. Per-item: yes/no/partial. Compute dimension scores (0–100% each).
4. Score the 8-item AI-content markers subsection (see references/core-eeat.md → "AI-content markers"). Mark each fired/not-fired.
5. Apply 4 veto checks. Any veto = no-publish.
6. Anonymous author on YMYL topic. (High-confidence with Firecrawl-recovered author schema + DOM byline; medium-confidence with prose-only inspection.)
7. Factual claims with no sources cited.
8. Undisclosed affiliate / sponsored relationships.
9. AI-generated YMYL content with no human-review markers (≥4 AI-content markers fired AND YMYL topic AND no editor byline / "reviewed by" credit / "last reviewed" or "fact-checked on" date). The "editor byline / reviewed by" check uses Firecrawl-recovered DOM byline + Article-schema author when available; falls back to prose-level pattern match if Firecrawl is absent (lower confidence — note in VERDICT.md).

1. Score CITE using references/cite.md
2. 30-item CITE rubric (Clear answer in 1st 200 words, Include primary stats, Timestamp freshness, Entity authority).
3. Per-item: yes/no/partial. Compute dimension scores.
4. Apply 3 veto checks (no answer in first 300 words / no datestamp on time-sensitive content / no entity disambiguation for proper-noun queries).

1. Cross-check against AIO winners
2. For the patterns characteristic of cited sources (from step 2), evaluate the candidate against each: does it have what the cited sources have?
3. Surface specific gaps.

1. Synthesise verdict using templates/verdict.md
2. Publish: E-E-A-T ≥ 75%, CITE ≥ 70%, no vetoes triggered.
3. Publish with fixes: E-E-A-T 60–74% OR CITE 55–69%, no vetoes. Top 5 fixes specified.
4. No publish: any veto triggered, OR E-E-A-T < 60%, OR CITE < 55%. Substantial rewrite needed.

Output format

Create a folder seo-content-audit-{target-slug}-{YYYYMMDD}/ with:

seo-content-audit-{target-slug}-{YYYYMMDD}/
├── VERDICT.md                       (publish / publish-with-fixes / no-publish — primary deliverable; inlines content snapshot + AIO context)
├── 03-eeat-scoring.md               (60-item rubric scored — load-bearing reference an editor consults item-by-item)
├── 04-cite-scoring.md               (30-item rubric scored — load-bearing reference)
├── 05-aio-winner-comparison.md      (gap vs cited sources — must remain top-level; live AIO competitive evidence per EVAL_RESULT_v2.md §9)
└── evidence/
    ├── 01-content-snapshot.md       (HTML extracts + page metadata — raw step output)
    └── 02-aio-context.md            (AIO presen