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 alirezarezvani/claude-code-skill-factory --skill content-trend-researchergit clone --depth 1 https://github.com/alirezarezvani/claude-code-skill-factoryWrote 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/alirezarezvani/claude-code-skill-factory/content-trend-researcher)<a href="https://agentmods.dev/skills/alirezarezvani/claude-code-skill-factory/content-trend-researcher"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-code-skill-factory/content-trend-researcher/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/alirezarezvani/claude-code-skill-factory/content-trend-researcher"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-code-skill-factory/content-trend-researcher.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 | $0.00053 | $0.01813 |
| Opus 5 | $0.00026 | $0.00907 |
| Sonnet 5 | $0.00011 | $0.00363 |
| Haiku 4.5 | $0.00005 | $0.00181 |
Grade A, and why
content-trend-researcher 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 11d 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:
- content-trend-researcher — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Trend Researcher
A comprehensive content research and analysis skill designed for content creators, marketers, and publishers who need to create high-performing content based on real-world trends and user intent signals.
What This Skill Does
This skill acts as your content intelligence system, analyzing trends across 10+ platforms to help you:
- Identify trending topics - Find what's gaining traction across platforms
- Understand user intent - Analyze search patterns and engagement signals
- Discover content gaps - Find underserved topics with high demand
- Generate outlines - Create data-driven article structures optimized for engagement
- Platform-specific insights - Understand where different content types perform best
Capabilities
Multi-Platform Trend Analysis
- Google Trends - Search volume trends, rising queries, regional interest
- Google Analytics - Traffic patterns, user behavior, conversion signals
- Substack - Newsletter trends, subscriber growth patterns
- Medium - Article performance, tags, claps, reading time
- Reddit - Subreddit activity, upvotes, comment engagement, trending discussions
- LinkedIn - Professional content trends, engagement metrics
- X (Twitter) - Viral topics, hashtag performance, thread engagement
- Blogs - Top-ranking blog posts, backlink profiles
- Podcasts - Episode popularity, download trends, ratings
- YouTube - Video performance, view trends, watch time, engagement
User Intent Analysis
- Informational intent - "How to", "What is", "Guide to"
- Commercial intent - "Best", "Review", "Comparison", "vs"
- Transactional intent - "Buy", "Pricing", "Discount"
- Navigational intent - Brand searches, specific resource lookups
- Problem-solving intent - "Fix", "Troubleshoot", "Solution"
Content Strategy Intelligence
- Optimal content formats per platform
- Best publishing times based on engagement data
- Headline formulas with proven performance
- Content length recommendations
- Topic clustering and pillar content identification
What ships with it
7 files 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.
- 11d ago First seen · 247 lines · 53 tokens per session scan A e849946b1429
content-trend-researcher is a skill published in the GitHub repository alirezarezvani/claude-code-skill-factory (861 stars, last pushed 10mo ago), licensed MIT. It adds 53 tokens to every session and 1,813 once invoked, about $0.0003 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.
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