How do I install the Track AI Citations skill?

Run npx skillmds@latest add ondrejknedla/track-ai-citations in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Track AI Citations skill do?

Track AI citations It is listed under Coding & Dev Tools on SkillMD.

Is Track AI Citations safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Track AI Citations?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Track AI Citations free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Track AI Citations?

OndrejKnedla (@ondrejknedla) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Track AI Citations by ondrejknedla — Agent Skill

Name: Track AI Citations
Author: OndrejKnedla

File contents

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 ?", " vs ", "Is good for X?"
Category / high-value (10): the broad terms you want to win ("best tool for ").
Problem / question (10): how real users phrase the need ("how do I ?").
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.

---name: track-ai-citationsdescription: Track AI citations---# Track AI citationsYou can't manage what you don't measure. Vendor "AI visibility scores" are opaque; this is therigorous 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 engineFor 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 scheduleRe-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.## OutputHand 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.

Frequently asked questions

Track AI Citations