Skillshub apify-brand-reputation-monitoring

Track reviews, ratings, sentiment, and brand mentions across Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, and TikTok. Use when user asks to monitor brand reputation, analyze reviews, track mentions, or gather customer feedback.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/apify/agent-skills/apify-brand-reputation-monitoring" ~/.claude/skills/comeonoliver-skillshub-apify-brand-reputation-monitoring && rm -rf "$T"

manifest: skills/apify/agent-skills/apify-brand-reputation-monitoring/SKILL.md

source content

Brand Reputation Monitoring

Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.

Prerequisites

(No need to check it upfront)

```
.env
```
file with
```
APIFY_TOKEN
```
Node.js 20.6+ (for native
```
--env-file
```
support)
```
mcpc
```
CLI tool:
```
npm install -g @apify/mcpc
```

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize results

Step 1: Determine Data Source

Select the appropriate Actor based on user needs:

User Need	Actor ID	Best For
Google Maps reviews	`compass/crawler-google-places`	Business reviews, ratings
Google Maps review export	`compass/Google-Maps-Reviews-Scraper`	Dedicated review scraping
Booking.com hotels	`voyager/booking-scraper`	Hotel data, scores
Booking.com reviews	`voyager/booking-reviews-scraper`	Detailed hotel reviews
TripAdvisor reviews	`maxcopell/tripadvisor-reviews`	Attraction/restaurant reviews
Facebook reviews	`apify/facebook-reviews-scraper`	Page reviews
Facebook comments	`apify/facebook-comments-scraper`	Post comment monitoring
Facebook page metrics	`apify/facebook-pages-scraper`	Page ratings overview
Facebook reactions	`apify/facebook-likes-scraper`	Reaction type analysis
Instagram comments	`apify/instagram-comment-scraper`	Comment sentiment
Instagram hashtags	`apify/instagram-hashtag-scraper`	Brand hashtag monitoring
Instagram search	`apify/instagram-search-scraper`	Brand mention discovery
Instagram tagged posts	`apify/instagram-tagged-scraper`	Brand tag tracking
Instagram export	`apify/export-instagram-comments-posts`	Bulk comment export
Instagram comprehensive	`apify/instagram-scraper`	Full Instagram monitoring
Instagram API	`apify/instagram-api-scraper`	API-based monitoring
YouTube comments	`streamers/youtube-comments-scraper`	Video comment sentiment
TikTok comments	`clockworks/tiktok-comments-scraper`	TikTok sentiment

Step 2: Fetch Actor Schema

Fetch the Actor's input schema and details dynamically using mcpc:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

Replace

ACTOR_ID

with the selected Actor (e.g.,

compass/crawler-google-places

This returns:

Actor description and README
Required and optional input parameters
Output fields (if available)

Step 3: Ask User Preferences

Before running, ask:

Output format:
- Quick answer - Display top few results in chat (no file saved)
- CSV - Full export with all fields
- JSON - Full export in JSON format
Number of results: Based on character of use case

Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Results

After completion, report:

Number of reviews/mentions found
File location and name
Key fields available
Suggested next steps (sentiment analysis, filtering)

Error Handling

APIFY_TOKEN not found

- Ask user to create

.env

with

APIFY_TOKEN=your_token

mcpc not found

- Ask user to install

npm install -g @apify/mcpc

Actor not found

- Check Actor ID spelling

Run FAILED

- Ask user to check Apify console link in error output

Timeout

- Reduce input size or increase

--timeout