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platform-fluency

platform-fluency

github.com/scrollmarkMarketing & growth1669 installsUpdated 1d ago
Repository

About platform-fluency

A routing skill for platform-specific content strategy, built around platform references and a cross-platform model of discovery, lifespan, and audience intent. It guides when to consult references and cautions against relying on algorithm details without current platform guidance.

Key features

  1. 01Models discovery, lifespan, and audience intent
  2. 02Routes to platform reference files
  3. 03Guides cross-platform content adaptation
  4. 04Flags common cross-posting mismatches
  5. 05Cautions against stale algorithm guidance

Use cases

  1. 01Adapting a post for a specific platform
  2. 02Comparing platforms before planning content
  3. 03Checking platform guidance before algorithm advice

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/scrollmark/social-skills --skill platform-fluency

After installing, type/platform-fluencyin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/platforms/
instagram.md
linkedin.md
tiktok.md
x.md
youtube.md

Details

From the project README

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.

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