trending-content-scout

trending-content-scout is a skill for Claude Code, Codex from gabrielmoreira/agent-skills-mirror. It costs 181 tokens per session (5,303 once invoked), scanned A, a copy of trending-content-scout, MIT.

A research tool that looks for high-performing posts and videos on YouTube, TikTok, X, and Reddit. It uses engagement data to show which formats and openings are attracting attention.

In plain words
What is it for?
Use it to research a keyword, benchmark content across platforms, find winning patterns, and identify content gaps.
Why use it?
It helps you understand what is already working before producing content. It can also expose audience questions and topics competitors have missed.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: mentions CLAUDE.md; mentions Claude Code; built for openclaw.

Good fit Use it to research a keyword, benchmark content across platforms, find winning patterns, and identify content gaps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gabrielmoreira/agent-skills-mirror/trending-content-scout
Install

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.

Any agent
npx skills add gabrielmoreira/agent-skills-mirror --skill trending-content-scout
Clone the repo
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirror

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for trending-content-scout

README.md
[![agentmods](https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/trending-content-scout/github.svg)](https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/trending-content-scout)
Your own site
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/trending-content-scout"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/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.

agentmods 80×15 button for trending-content-scout

Your own site · 80×15
<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/trending-content-scout"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/trending-content-scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 181 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,303 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 1971b8fe24dc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 9d 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.

Origin

This is a copy

100% identical to trending-content-scout — 0 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.

mirrors/repos/Affitor@affiliate-skills/skills/research/trending-content-scout/SKILL.md · 503 lines

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.

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:

Read the full file on GitHub · 503 lines

Changes

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.

  1. 9d ago First seen · 503 lines · 181 tokens per session scan A 1971b8fe24dc

Subscribe to this mod's changes

trending-content-scout is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), 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. It is 100% identical to trending-content-scout, differing in 0 lines, and is treated as a copy.

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