# Li Audit

Post-mortem on what the user has already published - which posts actually worked, why...

- Repository: https://github.com/Jakeschincariol/linkedin-agent-skill (3031 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Jakeschincariol/linkedin-agent-skill --skill li-audit -g -a claude-code -y`
Codex: `npx skills add Jakeschincariol/linkedin-agent-skill --skill li-audit -g -a codex -y`
Cursor: `npx skills add Jakeschincariol/linkedin-agent-skill --skill li-audit -g -a cursor -y`
Gemini CLI: `npx skills add Jakeschincariol/linkedin-agent-skill --skill li-audit -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Jakeschincariol/linkedin-agent-skill`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 separates
them, and be willing to conclude something the user will not like:

- Hook formula. Which numbers from `hooks.json` are in the top 5?
- Format. Text, document, image, video.
- Length.
- Theme.
- Day and time - check this **last**, and only if the other four show nothing.
  It is almost never the cause, and it is where people want it to be.
- First-hour comments. Posts the user replied to inside an hour versus not.

The rest: https://github.com/Jakeschincariol/linkedin-agent-skill/blob/HEAD/skills/li-audit/SKILL.md