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/tmolavi/mcp-agent-skills-hub/apify-competitor-intelligence)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/apify-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/apify-competitor-intelligence/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/tmolavi/mcp-agent-skills-hub/apify-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/apify-competitor-intelligence.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 | $0.00039 | $0.01459 |
| Opus 5 | $0.00019 | $0.00730 |
| Sonnet 5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
apify-competitor-intelligence 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 12d 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
95% identical to apify-competitor-intelligence — 3 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Intelligence
Analyze competitors using Apify Actors to extract data from multiple platforms.
When to Use
- You need competitor benchmarks for content, reviews, pricing, ads, audience, or channel performance.
- The task involves selecting Apify Actors to compare competitors across maps, booking, social, or video platforms.
- You need structured competitor data plus synthesized takeaways for strategy or positioning.
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: Identify competitor analysis type (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the analysis script
- [ ] Step 5: Summarize findings
Step 1: Identify Competitor Analysis Type
Select the appropriate Actor based on analysis needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Competitor business data | compass/crawler-google-places |
Location analysis |
| Competitor contact discovery | poidata/google-maps-email-extractor |
Email extraction |
| Feature benchmarking | compass/google-maps-extractor |
Detailed business data |
| Competitor review analysis | compass/Google-Maps-Reviews-Scraper |
Review comparison |
| Hotel competitor data | voyager/booking-scraper |
Hotel benchmarking |
| Hotel review comparison | voyager/booking-reviews-scraper |
Review analysis |
| Competitor ad strategies | apify/facebook-ads-scraper |
Ad creative analysis |
| Competitor page metrics | apify/facebook-pages-scraper |
Page performance |
| Competitor content analysis | apify/facebook-posts-scraper |
Post strategies |
| Competitor reels performance | apify/facebook-reels-scraper |
Reels analysis |
| Competitor audience analysis | apify/facebook-comments-scraper |
Comment sentiment |
| Competitor event monitoring | apify/facebook-events-scraper |
Event tracking |
| Competitor audience overlap | apify/facebook-followers-following-scraper |
Follower analysis |
| Competitor review benchmarking | apify/facebook-reviews-scraper |
Review comparison |
| Competitor ad monitoring | apify/facebook-search-scraper |
Ad discovery |
| Competitor profile metrics | apify/instagram-profile-scraper |
Profile analysis |
| Competitor content monitoring | apify/instagram-post-scraper |
Post tracking |
| Competitor engagement analysis | apify/instagram-comment-scraper |
Comment analysis |
| Competitor reel performance | apify/instagram-reel-scraper |
Reel metrics |
| Competitor growth tracking | apify/instagram-followers-count-scraper |
Follower tracking |
| Comprehensive competitor data | apify/instagram-scraper |
Full analysis |
| API-based competitor analysis | apify/instagram-api-scraper |
API access |
| Competitor video analysis | streamers/youtube-scraper |
Video metrics |
| Competitor sentiment analysis | streamers/youtube-comments-scraper |
Comment sentiment |
| Competitor channel metrics | streamers/youtube-channel-scraper |
Channel analysis |
| TikTok competitor analysis | clockworks/tiktok-scraper |
TikTok data |
| Competitor video strategies | clockworks/tiktok-video-scraper |
Video analysis |
| Competitor TikTok profiles | clockworks/tiktok-profile-scraper |
Profile data |
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.
- 12d ago First seen · 143 lines · 39 tokens per session scan A 419a33c4322f
apify-competitor-intelligence is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 16d ago), licensed MIT. It adds 39 tokens to every session and 1,459 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to apify-competitor-intelligence, differing in 3 lines, and is treated as a copy.
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