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 lingxling/awesome-skills-cn/plugin install agentic-awesome-skillsWrote 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/lingxling/awesome-skills-cn/apify-influencer-discovery)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-influencer-discovery"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-influencer-discovery/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/lingxling/awesome-skills-cn/apify-influencer-discovery"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-influencer-discovery.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 | $0.00035 | $0.01240 |
| Opus 5.5 | $0.00014 | $0.00496 |
| Sonnet 5.5 | $0.00007 | $0.00248 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
apify-influencer-discovery 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 19d 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
88% identical to apify-brand-reputation-monitoring — 96 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Influencer Discovery
Discover and analyze influencers across multiple platforms using Apify Actors.
When to Use
- You need to discover creators or influencers for outreach, partnerships, or campaign planning.
- The task is to evaluate authenticity, engagement, niche fit, or audience signals across social platforms.
- You need Apify-based extraction plus a shortlist or summary of suitable influencer candidates.
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 discovery source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the discovery script
- [ ] Step 5: Summarize results
Step 1: Determine Discovery Source
Select the appropriate Actor based on user needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Influencer profiles | apify/instagram-profile-scraper |
Profile metrics, bio, follower counts |
| Find by hashtag | apify/instagram-hashtag-scraper |
Discover influencers using specific hashtags |
| Reel engagement | apify/instagram-reel-scraper |
Analyze reel performance and engagement |
| Discovery by niche | apify/instagram-search-scraper |
Search for influencers by keyword/niche |
| Brand mentions | apify/instagram-tagged-scraper |
Track who tags brands/products |
| Comprehensive data | apify/instagram-scraper |
Full profile, posts, comments analysis |
| API-based discovery | apify/instagram-api-scraper |
Fast API-based data extraction |
| Engagement analysis | apify/export-instagram-comments-posts |
Export comments for sentiment analysis |
| Facebook content | apify/facebook-posts-scraper |
Analyze Facebook post performance |
| Micro-influencers | apify/facebook-groups-scraper |
Find influencers in niche groups |
| Influential pages | apify/facebook-search-scraper |
Search for influential pages |
| YouTube creators | streamers/youtube-channel-scraper |
Channel metrics and subscriber data |
| TikTok influencers | clockworks/tiktok-scraper |
Comprehensive TikTok data extraction |
| TikTok (free) | clockworks/free-tiktok-scraper |
Free TikTok data extractor |
| Live streamers | clockworks/tiktok-live-scraper |
Discover live streaming influencers |
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.
- 19d ago First seen · 131 lines · 35 tokens per session scan A 25d05c154fb1
apify-influencer-discovery is a skill published in the GitHub repository lingxling/awesome-skills-cn (321 stars, last pushed 19d ago), licensed MIT. It adds 35 tokens to every session and 1,240 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to apify-brand-reputation-monitoring, differing in 96 lines, and is treated as a copy.
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