li-audit
SkillDatabases & dataLets your agent analyze your past LinkedIn posts and analytics to show what worked, what flopped, and why.
Then ask your AI: use the li-audit skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.
About this skill
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
What this skill tells your AI
The instructions your AI receives, as published by jakeschincariol/linkedin-agent-skill in skills/li-audit/SKILL.md and read by Ahel’s review.
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.jsonare 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.
State the finding as a claim with the evidence attached, and say how confident it is. With 30 posts you can see a pattern; with 6 you cannot, and you should say that instead of inventing one.
Output
AUDIT · 31 posts · Jun 12 - Sep 5
TOP 5 BY ENGAGEMENT RATE
8.1% #3 Mistake "$18,000 is what no contract cost me" 1,940 imp
6.4% #20 Walk-Away "I fired my highest-paying client" 2,210 imp
...
BOTTOM 5
0.4% #5 List "7 tools every founder needs" 11,400 imp
...
WHAT THE DATA SAYS
1. Posts where you were the one who looked bad: mean 6.2% vs 1.1% for
everything else. n=6. This is your strongest signal and it is not close.
2. Tool listicles get impressions and nothing else. High reach, no comments,
no leads. Three of your bottom five.
3. Day of week shows nothing. Your Tuesday mean and your Friday mean are
inside the noise. Stop optimising it.
STOP: listicles about tools.
DO MORE: the ones with a cost you paid, and a number.
Then hand the conclusions to /li-plan so next week's plan is built on the
user's own evidence rather than on defaults.
Signals
- GitHub stars
- 2k
- Forks
- 251
- Last commit
- Sep 2026
Others that do the same job
Advanced
- Item type
- skill
- Key
li-audit-jakeschincariol- Source
- github.com/jakeschincariol/linkedin-agent-skill
github.com/jakeschincariol/linkedin-agent-skill
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