Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Affitor/affiliate-skills --skill trending-content-scoutgit clone --depth 1 https://github.com/Affitor/affiliate-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/affitor/affiliate-skills/trending-content-scout)<a href="https://agentmods.dev/skills/affitor/affiliate-skills/trending-content-scout"><img src="https://agentmods.dev/badge/skills/affitor/affiliate-skills/trending-content-scout/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/affitor/affiliate-skills/trending-content-scout"><img src="https://agentmods.dev/badge/skills/affitor/affiliate-skills/trending-content-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00181 | $0.05303 |
| Opus 5 | $0.00090 | $0.02652 |
| Sonnet 5 | $0.00036 | $0.01061 |
| Haiku 4.5 | $0.00018 | $0.00530 |
Grade A, and why
trending-content-scout 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- trending-content-scout — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 503 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trending Content Scout
Scan YouTube, TikTok, X, and Reddit for top-performing content by real engagement data. Find winning formats, hooks, and content gaps — before you create anything. Stop guessing what works. See what's already winning, then build on proven patterns.
This skill is the data foundation for the entire content pipeline. Run it first,
then feed its output into content-angle-ranker, viral-post-writer, tiktok-script-writer,
or any S2/S3 content skill.
Stage
This skill belongs to Stage S1: Research
When to Use
- Before creating any content for a keyword or niche
- When entering a new niche and need to understand what content works
- When comparing engagement across platforms for a topic
- When looking for content gaps competitors haven't filled
- When benchmarking your existing content against what's performing
- As the first step in any content creation workflow (before S2 skills)
Input Schema
keyword: string # (required) Search keyword — "AI video tools", "email marketing tips"
platforms: string[] # (optional, default: ["youtube", "tiktok"])
# Options: "youtube" | "tiktok" | "x" | "reddit"
sort_by: string # (optional, default: "engagement_score")
# Options: "views" | "likes" | "engagement_score" | "recency"
time_range: string # (optional, default: "30d") "7d" | "30d" | "90d" | "all"
limit: number # (optional, default: 20) Max content pieces to analyze
product: object # (optional) Specific product to focus on
name: string # "HeyGen"
url: string # "https://heygen.com"
No api_config needed in input — skills auto-detect configuration from conversation
context, project settings, or CLAUDE.md. See shared/references/social-data-providers.md
for setup instructions.
Workflow
Step 1: Determine Data Source
Check if the user has API configuration available:
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 · 503 lines · 181 tokens per session scan A 1971b8fe24dc
trending-content-scout is a skill published in the GitHub repository Affitor/affiliate-skills (656 stars, last pushed 6d ago), licensed MIT. It adds 181 tokens to every session and 5,303 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
trending-content-scout
Scan social platforms for top-performing content by engagement before you create anything. Use this skill when the user wants to see what content is winning in a niche, find viral content patterns, research what's working on YouTube/TikTok/X/Reddit, benchmark engagement, discover content gaps, or says "what content is…
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xquik-social-data
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Execute plans via delegatetask subagents (2-stage review).
duckduckgo-search
Free keyless web, news, and image search via ddgs.
mcporter
List, auth, and call MCP servers/tools from the terminal.