Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add aAAaqwq/AGI-Super-Team/plugin install agi-super-teamWrote 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/aaaaqwq/agi-super-team/apify-competitor-intelligence)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/apify-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/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/aaaaqwq/agi-super-team/apify-competitor-intelligence"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/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 | $0.01329 |
| Opus 5 | $0.00019 | $0.00665 |
| Sonnet 5 | $0.00008 | $0.00266 |
| Haiku 4.5 | $0.00004 | $0.00133 |
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 3d 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
89% identical to apify-competitor-intelligence — 14 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 — 132 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.
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.
- 3d ago First seen · 132 lines · 39 tokens per session scan A 2cb6ff5e915c
apify-competitor-intelligence is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 1,329 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to apify-competitor-intelligence, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
deslop
The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
root-cause
Find the mechanism behind a failure instead of patching its symptom - reproduce first, one variable per experiment with the prediction written before the run, exit by naming the mechanism and pinning it with a failing test. Use for a bug, an unexplained red test, or a failure that will not reproduce.
guidance
Add, edit, or audit guidance docs. Default writes guidance for Claude (.claude/guidance/, Markdown, moflo universal rules). -h writes for human readers (docs/, lighter ruleset). --html emits HTML with a minimal default stylesheet instead of Markdown. -a audits the .claude/guidance/ directory.
eldar
Consult the Eldar — audit a project's moflo + Claude Code setup for portable, high-leverage gaps and guide remediation. Default mode is read-only audit with severity-ranked findings; --fix presents an interactive triage menu and walks the user through each chosen fix (healer, missing CLAUDE.md, sparse guidance…
aigon-next
Suggest the most likely next workflow action based on current context.
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.