li-audit

Solid

Post-mortem on what the user has already published - which posts actually worked, why, and what to stop doing. Use when the user pastes their LinkedIn analytics or past posts and asks "what's working", "why did this flop", "read my analytics", "audit my content", or wants to know what to double down on.

AI & Automation 131 stars 20 forks Updated 3 days ago MIT

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Skill Content

# li-audit The only honest source of what works for an account is that account. Every rule in every LinkedIn guide, including the ones in this pack, is a prior. The user's own last 30 posts are the evidence. ## Input Ask for whichever the user has: - The post analytics export (LinkedIn: Analytics -> Content -> Export). CSV. - Or a screenshot per post with impressions, reactions, comments, reposts. - Or just the posts and their reaction counts, which is enough for a first pass. Also read `~/.claude/linkedin/log.md` if it exists, since it records which hook formula each post used. ## What to actually measure Raw impressions are the least useful number on the page, because they are mostly a function of how many people already follow the user. Compute these instead, and show the working: | metric | how | what it tells you | | --- | --- | --- | | **Engagement rate** | (reactions + comments + reposts) / impressions | whether the post earned its reach | | **Comment ratio** | comments / reactions | whether it started something or just got a nod | | **Reach multiple** | impressions / follower count | whether it travelled past the existing audience | | **Save/send rate** | if available | the strongest single predictor of future reach | Rank by engagement rate and reach multiple, not impressions. A post with 900 impressions and 40 comments beat the one with 12,000 impressions and 6. ## Then find the pattern With the top 5 and bottom 5 side by side, look for what actually se...

Details

Author
Jakeschincariol
Repository
Jakeschincariol/linkedin-agent-skill
Created
3 days ago
Last Updated
3 days ago
Language
Python
License
MIT

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