apify-brand-reputation-monitoring
Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.
Install / Use
npx skills add sickn33/agentic-awesome-skills --skill apify-brand-reputation-monitoringInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
OperationsSupported Platforms
Our assessment of apify-brand-reputation-monitoring
apify-brand-reputation-monitoring scores 92/100 on our quality scale, 59th of 259 Operations skills we index (top 23%).
Its SKILL.md is 4.9 KB long, well organised into 11 sections with 5 code examples: a solid amount of guidance for an agent.
With 46,875 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated yesterday, so apify-brand-reputation-monitoring is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-26. Automated pattern scan on 2026-09-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
apify-brand-reputation-monitoring compared with similar skills
All 4 of these similar skills score higher than apify-brand-reputation-monitoring; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| apify-brand-reputation-monitoring (this skill)by sickn33 | 92 | 46.9k | 1d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 85.5k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.8k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 83.7k | today | MCP Server |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
Frequently asked questions
- How do I install apify-brand-reputation-monitoring?
- Run
npx skills add sickn33/agentic-awesome-skills --skill apify-brand-reputation-monitoring. The install tabs above show the steps for each supported agent. - Which AI agents does apify-brand-reputation-monitoring work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is apify-brand-reputation-monitoring safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is apify-brand-reputation-monitoring still maintained?
- The repository was last updated yesterday, so apify-brand-reputation-monitoring is actively maintained.
Skill content
View source on GitHubname: apify-brand-reputation-monitoring description: "Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors." risk: critical source: community date_added: "2026-09-04"
Brand Reputation Monitoring
Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.
When to Use
- You need to monitor reviews, ratings, or brand mentions across social, travel, or map platforms.
- The task is to select and run an Apify Actor for brand sentiment or reputation tracking.
- You need exported monitoring results and a summary of reputation signals.
Prerequisites
(No need to check it upfront)
.envfile withAPIFY_TOKEN- Node.js 20.6+ (for native
--env-filesupport) mcpcCLI 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
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
