SEO Content QA

Use this skill to audit content reliability before or after publishing.

Requirements

• Python 3.10+ (scripts use modern type syntax)
• curl available in PATH (for HTTP HEAD checks)
• No pip dependencies for pre-publish scripts — standard library only
• Network access to r.jina.ai — used as a fallback for SERP search and competitor page fetching via browser rendering (handles JavaScript-heavy pages). Pass --no-jina to disable if needed.
• Node.js 18+ + SEOmator CLI — required for post-publish page checks (251 rules)

bash npm install -g @seomator/seo-audit seomator self doctor # verify installation If SEOmator is not installed, post_publish_check.py automatically falls back to basic checks (title / H1 / meta / canonical) and prints an install prompt.

• PageSpeed Insights API key — optional, enables Core Web Vitals field data (LCP / CLS / INP / FCP / TTFB). Pass via --psi-key or set in config.

Quick start

Run the unified runner for normal draft review:

bash
python3 skills/seo-geo-qa/scripts/seo_qa_runner.py path/to/article.md --keyword "best email apps"

If you know the site's main domain, pass it so internal vs external links are counted correctly:

bash
python3 skills/seo-geo-qa/scripts/seo_qa_runner.py path/to/article.md --keyword "best email apps" --site-domain example.com

If you want project defaults, pass a lightweight JSON config:

bash
python3 skills/seo-geo-qa/scripts/seo_qa_runner.py path/to/article.md --keyword "best email apps" --config path/to/seo-geo-qa.json

Standard workflow

1. Run seo_qa_runner.py on the draft.
2. Read the markdown report for the human audit trail.
3. Use the JSON report for automation or later aggregation.
4. Fix critical issues first.
5. Re-run until the article reaches PASS (or REVISE in writer mode).
6. After publishing, run post_publish_check.py on the live URL.

Lower-level tools

Use these only when debugging a specific failure mode.

Link/source verification

bash
python3 skills/seo-geo-qa/scripts/verify_links.py path/to/article.md
python3 skills/seo-geo-qa/scripts/verify_links.py path/to/article.md --json

SERP gap analysis

bash
# Auto-search (uses DuckDuckGo + Jina Reader fallback for browser-rendered content access)
python3 skills/seo-geo-qa/scripts/serp_gap_analyzer.py "best email apps" path/to/article.md

# Supply competitor URLs directly (skips search, still uses Jina to fetch pages)
python3 skills/seo-geo-qa/scripts/serp_gap_analyzer.py "best email apps" path/to/article.md --urls https://competitor1.com https://competitor2.com

# Disable Jina (direct HTTP only, faster but may fail on JavaScript-rendered pages)
python3 skills/seo-geo-qa/scripts/serp_gap_analyzer.py "best email apps" path/to/article.md --no-jina

How the SERP search works:

1. Tries DuckDuckGo's HTML endpoint via direct HTTP (fast path)
2. If blocked or returns no results, falls back to Jina Reader (r.jina.ai) which renders the page with a real browser and decodes DDG's redirect links
3. Competitor pages are always fetched via Jina first (browser-rendered for reliable access), then falls back to direct HTTP

Post-publish page check

Full audit (SEOmator 251 rules + custom checks for llms.txt / hreflang / CWV):

bash
# Standard — SEOmator + custom checks
python3 skills/seo-geo-qa/scripts/post_publish_check.py https://example.com/blog/post

# With Core Web Vitals (requires PageSpeed Insights API key)
python3 skills/seo-geo-qa/scripts/post_publish_check.py https://example.com/blog/post --psi-key YOUR_KEY

# Scope SEOmator to specific categories (faster)
python3 skills/seo-geo-qa/scripts/post_publish_check.py https://example.com/blog/post --categories core,technical,schema

# Lightweight fallback — skip SEOmator, run custom checks only
python3 skills/seo-geo-qa/scripts/post_publish_check.py https://example.com/blog/post --no-seomator

# JSON output for automation
python3 skills/seo-geo-qa/scripts/post_publish_check.py https://example.com/blog/post --json

What the post-publish check covers:

| Layer | Source | Checks | |---|---|---| | SEOmator | 251 rules / 20 categories | Core SEO, Performance, Links, Images, Security, Technical SEO, Schema, JS Rendering, Accessibility, Mobile, i18n, E-E-A-T, AI/GEO Readiness, … | | Custom | llms.txt | Existence + HTTP 200 at /llms.txt | | Custom | hreflang | Self-reference validation, return-link symmetry | | Custom (optional) | PageSpeed Insights API | LCP, CLS, INP, FCP, TTFB field data |

Report persistence

The runner writes timestamped markdown + JSON reports by default.

Default behavior:

• saves to qa-reports/<article-slug>/ next to the article
• does not overwrite old reports
• uses markdown for human review and JSON for machine state

Override with --report-dir or config.

Configuration

Read references/configuration.md when you need project-level defaults.

Source quality

Read references/source-tiers.md when you need to decide whether a citation is acceptable.

Verdict rules

Read references/verdict-rules.md when you need to tune PASS / FAIL / REVISE behavior.

Example output

Read references/example-report.md for a real QA report with annotations on how to interpret each section.

LLM review items

The seo_qa_runner.py JSON output includes a llm_review_required flag and a llm_review_items list. These identify checks that scripts cannot resolve deterministically — keyword intent alignment, borderline source quality, word count near thresholds, and SERP overlap edge cases.

When llm_review_required is true, read llm_review_items and apply editorial judgment before issuing a final verdict. Do not pass or fail on these items automatically.

Design intent

This skill is not a writing assistant. It is a reliability layer.

Scripts handle deterministic checks (link liveness, source tiers, structural metrics). LLM handles semantic judgment only — and only for items explicitly listed in llm_review_items. Do not let LLM re-evaluate what scripts have already decided.