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-audience-analysis)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/apify-audience-analysis"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-audience-analysis/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-audience-analysis"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/apify-audience-analysis.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 | $0.00030 | $0.01295 |
| Opus 5.5 | $0.00012 | $0.00518 |
| Sonnet 5.5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00129 |
Grade A, and why
apify-audience-analysis 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
92% identical to apify-audience-analysis — 47 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Analysis
Analyze and understand your audience using Apify Actors to extract follower demographics, engagement patterns, and behavior data from multiple platforms.
When to Use
- You need audience demographics, engagement patterns, or follower behavior from social platforms.
- The task is to choose and run Apify Actors for audience analysis across Facebook, Instagram, YouTube, or TikTok.
- You need structured extraction plus a summarized interpretation of audience findings.
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 audience 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 Audience Analysis Type
Select the appropriate Actor based on analysis needs:
| User Need | Actor ID | Best For |
|---|---|---|
| Facebook follower demographics | apify/facebook-followers-following-scraper |
FB followers/following lists |
| Facebook engagement behavior | apify/facebook-likes-scraper |
FB post likes analysis |
| Facebook video audience | apify/facebook-reels-scraper |
FB Reels viewers |
| Facebook comment analysis | apify/facebook-comments-scraper |
FB post/video comments |
| Facebook content engagement | apify/facebook-posts-scraper |
FB post engagement metrics |
| Instagram audience sizing | apify/instagram-profile-scraper |
IG profile demographics |
| Instagram location-based | apify/instagram-search-scraper |
IG geo-tagged audience |
| Instagram tagged network | apify/instagram-tagged-scraper |
IG tag network analysis |
| Instagram comprehensive | apify/instagram-scraper |
Full IG audience data |
| Instagram API-based | apify/instagram-api-scraper |
IG API access |
| Instagram follower counts | apify/instagram-followers-count-scraper |
IG follower tracking |
| Instagram comment export | apify/export-instagram-comments-posts |
IG comment bulk export |
| Instagram comment analysis | apify/instagram-comment-scraper |
IG comment sentiment |
| YouTube viewer feedback | streamers/youtube-comments-scraper |
YT comment analysis |
| YouTube channel audience | streamers/youtube-channel-scraper |
YT channel subscribers |
| TikTok follower demographics | clockworks/tiktok-followers-scraper |
TT follower lists |
| TikTok profile analysis | clockworks/tiktok-profile-scraper |
TT profile demographics |
| TikTok comment analysis | clockworks/tiktok-comments-scraper |
TT comment engagement |
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 · 134 lines · 30 tokens per session scan A b85a5c534a33
apify-audience-analysis is a skill published in the GitHub repository lingxling/awesome-skills-cn (321 stars, last pushed 19d ago), licensed MIT. It adds 30 tokens to every session and 1,295 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to apify-audience-analysis, differing in 47 lines, and is treated as a copy.
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