AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills/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/sickn33/agentic-awesome-skills/apify-influencer-discovery)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/apify-influencer-discovery"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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/sickn33/agentic-awesome-skills/apify-influencer-discovery"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/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 | $0.00017 | $0.00620 |
| Sonnet 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 4d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- apify-influencer-discovery — 100% identical, 1 lines differ
- apify-influencer-discovery — 100% identical, 1 lines differ
- apify-influencer-discovery — 95% identical, 3 lines differ
- apify-influencer-discovery — 95% identical, 3 lines differ
- apify-influencer-discovery — 95% identical, 3 lines differ
- apify-influencer-discovery — 95% identical, 3 lines differ
- apify-influencer-discovery — 95% identical, 3 lines differ
- apify-influencer-discovery — 91% identical, 13 lines differ
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.
- 4d ago Changed · +1 lines 25d05c154fb1
- 6d ago First seen · 130 lines · 35 tokens per session scan A 81a4ec0d7dff
apify-influencer-discovery is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,230 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 1,240 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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