Platform Fluency

This skill is a router. The deep platform knowledge lives in the reference files — this skill tells you when and how to use them.

Cross-Platform Mental Model

Platforms differ on three axes. Knowing where a platform sits on each one shapes every recommendation.

Discovery mechanism:

• Content-graph (TikTok) — the algorithm shows content based on what it is, not who made it. Follower count barely matters.
• Follower-graph (Instagram, LinkedIn) — your existing audience sees your content first. Reach beyond that depends on engagement signals.
• Search + recommended (YouTube) — intent-driven. People find content through search, suggestions, and browse. Evergreen content has a long tail.
• Conversation-graph (X) — content spreads through replies, quotes, and reposts. Real-time relevance matters most.

Content lifespan:

• Ephemeral: Stories (24h), X posts (hours of relevance), TikTok (days to weeks)
• Medium: Instagram Reels/feed (weeks), LinkedIn posts (days to a week)
• Evergreen: YouTube long-form (months to years), YouTube Shorts (weeks)

Audience intent:

• Passive scroll (TikTok, Instagram) — entertainment-seeking, low commitment per piece
• Active search (YouTube, increasingly TikTok) — looking for something specific
• Professional context (LinkedIn) — career-relevant, during work hours
• Conversation (X) — wants to engage, react, discuss

When to Load References

If the conversation involves a specific platform, load references/platforms/{platform}.md from this skill's own directory. If comparing platforms or adapting content across them, load both.

Native vs. Cross-Posted

Every platform's audience can detect cross-posted content. Dead giveaways:

• Wrong aspect ratio or resolution
• Watermarks from other platforms (especially TikTok logo on Reels)
• Caption style that belongs elsewhere (hashtag walls on LinkedIn, professional tone on TikTok)
• Timing and pacing that feels off for the platform

Native content consistently outperforms cross-posted content. When in doubt, adapt.

Algorithm Guidance

Don't give specific algorithm advice without loading the relevant platform reference — algorithms change frequently. Provide the framework (what signals matter, how distribution works) rather than specific numbers. If the reference's last_updated date is more than 6 months old, note that algorithmic details may have shifted.

If the reference can't be loaded, work with the mental model above and be upfront about the limitation.