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 social-media-analyzergit 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/social-media-analyzer)<a href="https://agentmods.dev/skills/alirezarezvani/claude-code-skill-factory/social-media-analyzer"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-code-skill-factory/social-media-analyzer/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/social-media-analyzer"><img src="https://agentmods.dev/badge/skills/alirezarezvani/claude-code-skill-factory/social-media-analyzer.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 | $0.00028 | $0.00598 |
| Opus 5 | $0.00014 | $0.00299 |
| Sonnet 5 | $0.00006 | $0.00120 |
| Haiku 4.5 | $0.00003 | $0.00060 |
Grade A, and why
social-media-analyzer 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 10d 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:
- social-media-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Campaign Analyzer
This skill provides comprehensive analysis of social media campaign performance, helping marketing agencies deliver actionable insights to clients.
Capabilities
- Multi-Platform Analysis: Track performance across Facebook, Instagram, Twitter, LinkedIn, TikTok
- Engagement Metrics: Calculate engagement rate, reach, impressions, click-through rate
- ROI Analysis: Measure cost per engagement, cost per click, return on ad spend
- Audience Insights: Analyze demographics, peak engagement times, content performance
- Trend Detection: Identify high-performing content types and posting patterns
- Competitive Benchmarking: Compare performance against industry standards
Input Requirements
Campaign data including:
- Platform metrics: Likes, comments, shares, saves, clicks
- Reach data: Impressions, unique reach, follower growth
- Cost data: Ad spend, campaign budget (for ROI calculations)
- Content details: Post type (image, video, carousel), posting time, hashtags
- Time period: Date range for analysis
Formats accepted:
- JSON with structured campaign data
- CSV exports from social media platforms
- Text descriptions of key metrics
Output Formats
Results include:
- Performance dashboard: Key metrics with trends
- Engagement analysis: Best and worst performing posts
- ROI breakdown: Cost efficiency metrics
- Audience insights: Demographics and behavior patterns
- Recommendations: Data-driven suggestions for optimization
- Visual reports: Charts and graphs (Excel/PDF format)
How to Use
"Analyze this Facebook campaign data and calculate engagement metrics" "What's the ROI on this Instagram ad campaign with $500 spend and 2,000 clicks?" "Compare performance across all social platforms for the last month"
Scripts
calculate_metrics.py: Core calculation engine for all social media metricsanalyze_performance.py: Performance analysis and recommendation generation
What ships with it
5 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.
- 10d ago First seen · 71 lines · 28 tokens per session scan A b7211136a8bb
social-media-analyzer is a skill published in the GitHub repository alirezarezvani/claude-code-skill-factory (859 stars, last pushed 10mo ago), licensed MIT. It adds 28 tokens to every session and 598 once invoked, about $0.0001 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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