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Li Audit Skill for Claude

>- 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.

854 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
112
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/Jakeschincariol/linkedin-agent-skill --skill li-audit

The instruction itself

5 sections, as written by the author

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.

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.

How to use it

Copy the folder

Take jakeschincariol/li-audit from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.