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Skillzwave

social-media-analyzer

325 stars 73 forks
0
C

Analyzes social media campaign performance across platforms with engagement metrics, ROI calculations, and audience insights for data-driven marketing decisions

CommandsAgents
#media campaign#campaign#claude-skills-creator#social media#ai-agents#claude-code#claude-ai#ROI calculations
Also in: video

Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

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skilz install alirezarezvani/claude-code-skill-factory/social-media-analyzer
skilz install alirezarezvani/claude-code-skill-factory/social-media-analyzer --agent opencode
skilz install alirezarezvani/claude-code-skill-factory/social-media-analyzer --agent codex
skilz install alirezarezvani/claude-code-skill-factory/social-media-analyzer --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding assistants

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/alirezarezvani/claude-code-skill-factory
2. Copy the agent skill directory:
cp -r claude-code-skill-factory/generated-skills/social-media-analyzer ~/.claude/skills/
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Need detailed installation help? Check our platform-specific guides:

Claude Desktop Guide Claude Code Guide Troubleshooting

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Agentic Skill Details

Owner
alirezarezvani (GitHub)
Repository
claude-code-skill-factory
Stars
325
Forks
73
Type
Technical
Meta-Domain
media
Primary Domain
monitoring
Market Score
0
Agentic Skill Grades →

Agent Skill Grade

C
Score: 71/100 Click to see breakdown

Score Breakdown

Spec Compliance
11/15
PDA Architecture
19/30
Ease of Use
17/25
Writing Style
7/10
Utility
16/20
Modifiers: +1

Areas to Improve

  • Description needs trigger phrases
  • Missing reference architecture
  • Vague trigger terms

Recommendations

  • Address 2 high-severity issues first
  • Add trigger phrases to description for discoverability
  • Add table of contents for files over 100 lines

Graded: 2026-01-24

Developer Feedback

I've been looking at how you structured the social media data extraction—the tokenization approach is clever, but I'm curious whether you considered the trade-offs between flexibility and performance when handling rate-limited APIs.

Links:

The TL;DR

You're at 71/100, solidly in C territory. This is based on Anthropic's best practices for agentic skills. Your Utility pillar is strong (16/20)—the ROI calculations and multi-platform support actually solve real problems. But Progressive Disclosure Architecture (19/30) and Ease of Use (17/25) are dragging you down. The good news? These are fixable with some structural changes.

What's Working Well

  • Utility is legit (16/20) - You're addressing a real marketing need with concrete metrics (engagement rate, ROI, CTR). The scripts have actual calculation logic and benchmarks that add value.
  • Supporting files exist - You've got HOW_TO_USE.md, sample_input.json, and actual Python scripts. That's more than most skills have.
  • Navigation is clean - SKILL.md has clear section headers and logical flow. No confusing structure here.
  • Validation thinking - Your scripts include safeguards like safe_divide for zero-division errors. That's defensive coding done right.

The Big One: Missing Reference Architecture

Here's what's holding back your PDA score: SKILL.md doesn't reference the supporting files that exist in your skill package. Users reading SKILL.md have no idea that HOW_TO_USE.md, sample_input.json, and the scripts are available. They discover them by accident, if at all.

Why it matters: Progressive Disclosure means guiding users from overview → details → implementation. Right ...

AI-Detected Topics

Extracted using NLP analysis

media campaign campaign claude-skills-creator social media ai-agents claude-code claude-ai ROI calculations Analyzes social engagement

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