Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/apify-brand-reputation-monitoring)<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/apify-brand-reputation-monitoring"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/apify-brand-reputation-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/apify-brand-reputation-monitoring"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/apify-brand-reputation-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.01121 |
| Opus 5 | $0.00013 | $0.00561 |
| Sonnet 5 | $0.00005 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
apify-brand-reputation-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
84% identical to apify-brand-reputation-monitoring — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Reputation Monitoring
Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.
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 |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 123 lines · 25 tokens per session scan A 1550ae57e74c
apify-brand-reputation-monitoring is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 1,121 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to apify-brand-reputation-monitoring, differing in 13 lines, and is treated as a copy.
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