Individual skill
platform-strategy-sms
When the user wants platform-specific tactical guidance for LinkedIn, Twitter/X, Threads, or Bluesky. Also use when the user mentions 'LinkedIn strategy,' 'Twitter strategy,' 'Threads strategy,' 'Bluesky strategy,' 'algorithm,' 'what works on LinkedIn,' 'cross-posting,' 'platform differences,' 'adapt my content,' or 'which platform should I focus on.' For overall content strategy, see content-strategy-sms. For writing posts, see post-writer-sms.
skills/platform-strategy-sms/SKILL.md
Source commit 4f85b07069983b3b0b9bbe6519fed7017edcce81
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Instructions only
- Last checked
- Nov 10, 2026
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Repository context
- Repository stars (not skill ratings)
- 649
- Repository last updated
- Nov 5, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- ai-writing Repository topic
- bluesky Repository topic
- claude Repository topic
- claude-code Repository topic
- content-creation Repository topic
- content-strategy Repository topic
- cursor Repository topic
- linkedin Repository topic
- mcp Repository topic
- writing Derived category
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## When to Use
- User asks for **platform-specific tactical guidance** for LinkedIn, Twitter/X, Threads, or Bluesky
- User mentions "LinkedIn strategy," "Twitter strategy," "Threads strategy," or "Bluesky strategy"
- User says "algorithm," "what works on LinkedIn," or "cross-posting"
- User asks about "platform differences" or wants to **adapt content** across platforms
- User asks "which platform should I focus on" or wants a platform comparison
- User wants to understand how a specific platform's algorithm or culture works
## Role
You are an expert social media platform strategist. Your job is to give the user actionable, platform-specific tactics — not generic advice. Every recommendation should reflect how each platform's algorithm, culture, and audience actually behave.
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