AI Search Visibility Tracker

Four workflows covering the full AI visibility lifecycle: discover which prompts matter → find citation opportunities → audit your site → track over time.

All workflows use

apify/google-search-scraper

for AI search. Workflow C also uses

apify/website-content-crawler

Recommended flow: Run Workflow A to discover prompts → Workflow B to find citation opportunities → Workflow C to audit your site → Workflow D to track everything on a schedule.

Workflow A — Competitor Prompt Discovery

Goal: Find which queries surface a competitor in AI search answers, so you know which prompts are worth monitoring.

Inputs to collect

#	Input	Notes
1	Competitor domain(s)	e.g. `brightdata.com` , `scraperapi.com`
2	Seed topic keywords	e.g. "web scraping", "data extraction API"
3	AI sources	Default: all six (AI Overviews, AI Mode, ChatGPT, Perplexity, Copilot, Gemini)

Workflow

Generate 15–30 candidate queries from seed keywords using these templates:

best [topic]

[topic] tools

how to [topic]

[topic] for [use case]

[topic] vs [competitor brand]

[competitor brand] alternative

[topic] API

[topic] pricing

[topic] tutorial

Run

apify/google-search-scraper

for each candidate query. For each result, extract:

aiOverview.sources[]

aiMode.sources[]

chatGptAnswer.sources[]

perplexityAnswer.sources[]

copilotAnswer.sources[]

geminiAnswer.sources[]

Also check
```
answer_text
```
/
```
aiOverview.text
```
for competitor brand name mentions (word-boundary match:
```
\bBrand\b
```
)

For each (query, source) pair where the competitor domain or brand appears: record a hit.
Output a prompt-major table sorted by total hit count descending:

| Query | ChatGPT | Perplexity | AI Overviews | AI Mode | Copilot | Gemini | Total |
|-------|---------|------------|--------------|---------|---------|--------|-------|
| "best web scraping API" | ✓ | ✓ | — | ✓ | — | ✓ | 4 |
| "how to scrape Google" | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 6 |

Deliver the top-N queries (default 10) as a ready-to-paste list for Workflow D's
```
config.json
```
prompts.

Workflow B — Citation Opportunity Finder

Goal: For a target topic, identify which domains and content types AI engines most often cite — revealing where to publish or pitch content.

Inputs to collect

#	Input	Notes
1	Target topic / industry	e.g. "web scraping", "ecommerce automation"
2	Seed queries	5–20 queries covering the topic space
3	AI sources	Default: all six
4	Deep-crawl top cited?	Optional: crawl top-3 cited pages with `website-content-crawler` for structure patterns

Workflow

Run
```
apify/google-search-scraper
```
for each seed query across selected AI sources.
Collect every URL from
```
sources[]
```
across all results. Normalise to registrable domain (
```
blog.example.com
```
→
```
example.com
```
).
Aggregate:
- By domain: count citations, list which AI sources cite it, list which queries triggered it
- By content type: infer from URL path patterns (docs →
```
/docs/
```
  ,
```
/reference/
```
  ; blog →
```
/blog/
```
  ; news → known news domains)
Rank by total citation count. Output:

Top-cited domains for "web scraping" (42 queries × 6 sources):
| Domain | Citations | AI Sources | Inferred type |
|--------|-----------|------------|--------------|
| docs.apify.com | 38 | ChatGPT, Perplexity, AI Mode | Documentation |
| scraperapi.com/blog | 21 | AI Overviews, Gemini | Long-form blog |

If deep-crawl enabled: run
```
apify/website-content-crawler
```
on the top-3 cited URLs per domain. From the markdown output, extract:
- First heading that directly answers the query
- Presence of code blocks in first 500 words
- Word count
- Whether an H2/H3 contains the exact query phrase
Summarise patterns: "AI engines in this topic prefer [long-form docs / short direct-answer posts]. Typical cited page: [word count range], [has/lacks direct-answer H2], [has/lacks code example above the fold]."

Workflow C — GEO Website Audit

Goal: Check whether a specific website's content is structured for AI citation; compare it against what AI engines actually cite for your target prompts.

Inputs to collect

#	Input	Notes
1	Your website URL	e.g. `https://apify.com`
2	Target prompts	Use Workflow A output, or supply 5–10 directly
3	AI sources	Default: all six

Workflow

Run
```
apify/google-search-scraper
```
for each target prompt. For each (prompt × source) record whether your registrable domain appears in
```
sources[]
```
.
For prompts where your domain is not cited: identify the top-cited competitor URL for that prompt.
Run
```
apify/website-content-crawler
```
on:
- Your most relevant page(s) for each un-cited prompt
- The top-cited competitor page for each un-cited prompt
For each un-cited prompt, produce a gap card:

Prompt: "how to scrape Google search results"
Your page: apify.com/blog/scraping-google  →  NOT cited on ChatGPT, Perplexity, AI Mode
Top-cited: docs.brightdata.com/scraping/google (cited 5/6 sources)

Structural gaps:
  ✗ Your page: answer buried after 900 words, no direct-answer H2
  ✓ Competitor: H2 "How to scrape Google in 3 steps" at word 120 + code block at word 180

Recommended actions (priority order):
  1. Add H2 that mirrors the query phrase within first 300 words
  2. Move code example above the fold
  3. Add a "Quick answer" summary box at the top

Deliver: per-prompt gap cards + a consolidated action table ranked by expected impact.

Workflow D — Recurring Visibility Tracker

Goal: Snapshot brand citations and mentions across all six AI surfaces on a recurring schedule and track changes over time.

Prerequisites

(No need to check upfront)

```
APIFY_TOKEN
```
saved in a
```
.env
```
file next to
```
config.json
```
(the runner auto-loads it).
Python 3.9+ on PATH.
```
pip3 install requests
```
(only third-party dependency);
```
pip3 install tldextract
```
recommended for accurate registrable-domain matching on multi-part TLDs.
For automated daily runs: macOS / Linux with launchd or cron available (the installer handles both). Windows users get printed
```
schtasks
```
instructions.

Steps

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Load or collect the seven required inputs
- [ ] Step 2: Confirm AI sources and cadence
- [ ] Step 3: Write config.json + .env, then install the OS schedule
- [ ] Step 4: Run a snapshot now so the user sees the first report
- [ ] Step 5: Deliver the history report (diff vs. all prior runs)

Step 1: Load or Collect the Seven Required Inputs

config.json

exists in the user's working directory, load it and skip to Step 4 unless the user asks to reconfigure. On first run, ask all seven anchors before any Actor call:

#	Input	Why it matters
1	Brand URL	Primary domain. Drives registrable-domain citation matching ( `blog.apify.com` -> `apify.com` ).
2	Brand name(s)	Surface forms for text-mention matching (e.g., `Apify` , `apify.com` , `@apify` ). URL-only matching misses mentions without links.
3	Competitor brands	Ask explicitly: "Which competitors do you want tracked alongside your brand?" Accept `name + domain` pairs. Zero is allowed; the question must still be asked on first run.
4	Prompts to monitor	One or more search queries. Each runs through every enabled AI source. If you don't know which prompts to use yet, run Workflow A first — it discovers competitor-visible prompts you can paste here.
5	Cadence	`daily` / `weekly` / `monthly` . Drives the schedule entry that `install_cron.sh` writes.
6	Which AI sources	Present the six (AI Overviews, AI Mode, ChatGPT, Perplexity, Copilot, Gemini), all enabled by default. Each adds per-result cost -- current pricing on the Actor page (https://apify.com/apify/google-search-scraper).
7	Apify Dataset name	The named dataset to append to. If absent, created on first run; the name is recorded in `config.json` .

After those seven, ask optional follow-ups:

countryCode

languageCode

location

(UULE), preferred run hour (default

09:00

local).

Then one verbosity question -- save as

config.json:include_full_answers

on_demand
(default): report shows short quoted snippets around each surface-form match. Full LLM answers live in the named KV store; user can ask later.
always
: report embeds the full LLM answer verbatim whenever any entity is mentioned. Useful for one prompt; gets unwieldy at 5+ prompts.

Step 2: Confirm AI Sources and Cadence

Echo back the user's seven choices in a single paragraph for confirmation. If the user toggles sources, update the in-memory config before writing.

Step 3: Write

config.json

.env

, Then Install the OS Schedule

Create the working directory layout next to where the user wants reports to land:

working-dir/
  config.json     # copied from the skill's config.example.json, edited with collected values
  .env            # APIFY_TOKEN=apify_api_xxx   (chmod 600)

bash

cp ${CLAUDE_PLUGIN_ROOT}/reference/scripts/config.example.json ./config.json
# then edit with the collected values, save

echo 'APIFY_TOKEN=your_token_here' > ./.env
chmod 600 ./.env

Then install the OS schedule:

bash

bash ${CLAUDE_PLUGIN_ROOT}/reference/scripts/install_cron.sh --cadence daily --hour 9

Cron expression mapping:

Cadence	Cron expression	When
daily	`0 H * * *`	every day at H:00 local
weekly	`0 H * * 1`	every Monday at H:00
monthly	`0 H 1 * *`	the 1st of every month at H:00

Step 4: Run a Snapshot Now

macOS:

bash

launchctl kickstart "gui/$(id -u)/com.apify.ai-visibility-tracker"
tail -f ~/Library/Logs/ai-visibility-tracker.log

Linux / generic:

bash

python3 ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_snapshot.py --config ./config.json

Both paths:

Call
```
apify/google-search-scraper
```
with the configured prompts + AI-source toggles.
Parse each (prompt x source) cell for citations and brand/competitor mentions.
Append rows to the named Apify Dataset (schema in
```
reference/output-schema.md
```
).
Write the raw item to the named KV store (long-term archive).
Compute the history vs. all prior runs.

Write

reports/snapshot-<ISO-date>.md

next to

config.json

Step 5: Deliver the History Report

Open

reports/snapshot-<ISO-date>.md

and surface the top findings in chat. Lead with:

First-ever citations / mentions today -- entity x source combinations crossing the threshold for the first time.
Drops -- entity was cited in the latest prior run but isn't today.
For every cited entity, the exact matched URL(s).

Output format

Snapshot summaries are entity-major: one Markdown table per tracked entity (brand, then competitors), one row per AI source, columns

Source | Cited | Mentioned | SoV% | Matched URLs | History

Actors

Actor Used in Key input fields

Actor	Used in	Key input fields
`apify/google-search-scraper`	A, B, C, D	`queries[]` , `aiOverview` , `chatGptSearch` , `perplexitySearch` , `googleAiMode` , `bingCopilotSearch` , `googleGeminiSearch`
`apify/website-content-crawler`	B (optional), C	`startUrls[]` , `maxCrawlPages` , `outputMarkdown: true`

apify/google-search-scraper

A, B, C, D

queries[]

aiOverview

chatGptSearch

perplexitySearch

googleAiMode

bingCopilotSearch

googleGeminiSearch

apify/website-content-crawler

B (optional), C

startUrls[]

maxCrawlPages

outputMarkdown: true

Pricing changes; check the pricing tab before quoting numbers. Disable unused AI sources to reduce cost.

Telemetry. Workflow D runs through

reference/scripts/run_snapshot.py

, which already sets

User-Agent: apify-awesome-skills/ai-search-visibility-tracker-...

on every Apify API call. When calling these Actors ad-hoc in Workflows A-C, pass the matching flags so usage is attributed:

--user-agent apify-awesome-skills/apify-ai-search-visibility-tracker

and

--json

(use

--format json

for

apify datasets get-items

), and append

2>/dev/null

. Example:

bash

apify actors call apify/google-search-scraper \
  --user-agent apify-awesome-skills/apify-ai-search-visibility-tracker \
  --json 2>/dev/null

Quality Rules

Non-interactive. No stdin reads in
```
run_snapshot.py
```
-- launchd / cron has no stdin.
Word-boundary brand matching (
```
\bbrand\b
```
, case-insensitive). See
```
reference/citation-matching.md
```
.

Registrable-domain citation matching (

blog.apify.com

counts as

apify.com

). See

reference/citation-matching.md

Never skip a row. If an AI source returns nothing, write a row with

cited: false, mentioned: false, answer_text: "[no answer returned]"

Every row carries the Apify run ID so any finding can be reverified.

Error Handling

APIFY_TOKEN not found

-- Tell the user to put it in

.env

next to

config.json

(

echo 'APIFY_TOKEN=...' > .env && chmod 600 .env

). Token at https://console.apify.com/account/integrations.

config.json not found

-- Run Step 3 first to create it from the template.

Dataset name not set

-- Ask the user for a name; the runner will create the dataset on first append.

Actor run FAILED

-- Print the console link from the runner output and ask the user to inspect it.

AI source returned no answer

-- The row is still written with

[no answer returned]

. Not an error.

website-content-crawler returns no markdown

-- Page may be JS-heavy; try with

useBrowserCrawler: true

Schedule not firing

-- See

reference/scheduling.md

troubleshooting section.

No previous run to diff against

-- First run only. The report renders the snapshot without a history section.

apify-ai-search-visibility-tracker

NPX Install

Tags

SKILL.md Content

AI Search Visibility Tracker

Workflow A — Competitor Prompt Discovery

Inputs to collect

Workflow

Workflow B — Citation Opportunity Finder

Inputs to collect

Workflow

Workflow C — GEO Website Audit

Inputs to collect

Workflow

Workflow D — Recurring Visibility Tracker

Prerequisites

Steps

Step 1: Load or Collect the Seven Required Inputs

Step 2: Confirm AI Sources and Cadence

Step 3: Write
`config.json`
+
`.env`
, Then Install the OS Schedule

Step 4: Run a Snapshot Now

Step 5: Deliver the History Report

Output format

Actors

Quality Rules

Error Handling

apify-ai-search-visibility-tracker

NPX Install

Tags

SKILL.md Content

AI Search Visibility Tracker

Workflow A — Competitor Prompt Discovery

Inputs to collect

Workflow

Workflow B — Citation Opportunity Finder

Inputs to collect

Workflow

Workflow C — GEO Website Audit

Inputs to collect

Workflow

Workflow D — Recurring Visibility Tracker

Prerequisites

Steps

Step 1: Load or Collect the Seven Required Inputs

Step 2: Confirm AI Sources and Cadence

Step 3: Write config.json + .env, Then Install the OS Schedule

Step 4: Run a Snapshot Now

Step 5: Deliver the History Report

Output format

Actors

Quality Rules

Error Handling

Step 3: Write
`config.json`
+
`.env`
, Then Install the OS Schedule