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Track ai citations

Skill OndrejKnedla/seo-geo-playbook-ok/skills/track-ai-citations

SEO + GEO toolkit as Claude Code skills: audit, A-F score, llms.txt, AI-citation tracking, fixes. Zero-dependency.

Install
npx -y skills add OndrejKnedla/seo-geo-playbook-ok --skill track-ai-citations

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Establish and re-run an AI-citation baseline, does ChatGPT, Perplexity, Gemini, or Google AI Overviews actually cite this brand? Builds a fixed query set, runs it incognito, tracks a single "did it cite us?" column at day 0 / +30 / +90, and reports the trend. The only honest way to know if GEO work is paying off. Triggers on: AI citation tracking, AI visibility tracking, am I cited by ChatGPT, Perplexity ranking, GEO measurement, AI rank tracking, share of voice in AI.

SKILL.md

3.5 KB, 752 tokens by cl100k_base, as published. Nobody here has run it

Track AI citations

You can't manage what you don't measure. Vendor "AI visibility scores" are opaque; this is the rigorous DIY method that produced the measured 13% → 22% → 30% curve in ../../references/GEO-ADOPTION.md.

Method

1. Build a fixed query set (~30 prompts)

The set is fixed so re-tests are comparable. Mix four buckets:

  • Branded (5): "What is <brand>?", "<brand> vs <competitor>", "Is <brand> good for X?"
  • Category / high-value (10): the broad terms you want to win ("best <category> tool for <audience>").
  • Problem / question (10): how real users phrase the need ("how do I <job-to-be-done>?").
  • Long-tail / niche (5): specific, lower-volume, where a young brand can win first.

Save them to assets/queries-template.csv (copy it, fill the query column).

2. Run the set, incognito, per engine

For each query, in a logged-out / incognito session (personalization skews results), record one thing: did the answer cite or recommend the brand? yes/no, plus which competitors it cited instead. Engines to cover: ChatGPT (Search), Perplexity, Google AI Overviews, Gemini, Claude. Note: Claude is a conservative citer and Perplexity an aggressive one, track each engine in its own column, don't average blindly.

Tip: a scraping tool (Firecrawl etc.) or the engines' own APIs can automate this, but a manual pass of 30 queries takes ~30 minutes and is more trustworthy. Keep raw screenshots/links for evidence.

3. Score and store

citation rate = (queries citing the brand) / (total queries), per engine and overall. Append a dated row to assets/citation-log.csv. Never overwrite history, the trend is the point.

4. Re-test on a schedule

Re-run the same set at +30 and +90 days. Expect the shape from GEO-ADOPTION.md:

  • On-page fixes (SSR JSON-LD, canonical, schema, citable structure) move the line fast, then plateau.
  • The climb past the plateau is bought off-site (brand mentions: YouTube > Reddit ≈ Wikipedia > LinkedIn > PR), not with more pages.

5. Report

  • Citation rate per engine, with the delta vs the previous checkpoint.
  • Which queries flipped from no→yes (what's working) and yes→no (regressions / volatility).
  • Which competitors keep getting cited where you don't (the gap to close).
  • The honest verdict: if on-page is clean and the line has plateaued, the bottleneck is off-site authority, stop writing pages, start earning mentions.

Output

Hand the trend to geo-charts to render the citation-rate line for a shareable report.

Reference files (read on demand)

  • ../../references/platform-profiles/: how each engine cites (Perplexity passage-level, ChatGPT position bias, Claude conservative, Google AI Mode fan-out), so you read each engine's column correctly.
  • ../../references/statistics-2026.md: per-platform citation behavior and the off-site signal weights.
  • ../../references/GEO-ADOPTION.md: the baseline curve and re-test protocol this method produced.

What ships with it: 2 files

1.8 KB alongside SKILL.md

assets/

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