Come usare
- Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
- Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
- Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-auditPrima di iniziare
- L’installazione non include abbonamenti, quote API o accesso ai file.
- Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.
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.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.
