Li audit skill

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

by Jakeschincariol·MIT license·★ 1,856 Stars on the repo·GitHub ↗

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Inside this skill

SKILL.md · the author's own words
# 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

Word-for-word excerpt, not a result. Read the full SKILL.md ↓

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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.

1---
2name: li-audit
3description: >-
4 Post-mortem on what the user has already published - which posts actually
5 worked, why, and what to stop doing. Use when the user pastes their LinkedIn
6 analytics or past posts and asks "what's working", "why did this flop", "read
7 my analytics", "audit my content", or wants to know what to double down on.
8---
9 
10# li-audit
11 
12The only honest source of what works for an account is that account. Every
13rule in every LinkedIn guide, including the ones in this pack, is a prior. The
14user's own last 30 posts are the evidence.
15 
16## Input
17 
18Ask for whichever the user has:
19 
20- The post analytics export (LinkedIn: Analytics -> Content -> Export). CSV.
21- Or a screenshot per post with impressions, reactions, comments, reposts.
22- Or just the posts and their reaction counts, which is enough for a first
23 pass.
24 
25Also read `~/.claude/linkedin/log.md` if it exists, since it records which
26hook formula each post used.
27 
28## What to actually measure
29 
30Raw impressions are the least useful number on the page, because they are
31mostly a function of how many people already follow the user. Compute these
32instead, and show the working:
33 
34| metric | how | what it tells you |
35| --- | --- | --- |
36| **Engagement rate** | (reactions + comments + reposts) / impressions | whether the post earned its reach |
37| **Comment ratio** | comments / reactions | whether it started something or just got a nod |
38| **Reach multiple** | impressions / follower count | whether it travelled past the existing audience |
39| **Save/send rate** | if available | the strongest single predictor of future reach |
40 
41Rank by engagement rate and reach multiple, not impressions. A post with 900
42impressions and 40 comments beat the one with 12,000 impressions and 6.
43 
44## Then find the pattern
45 
46With the top 5 and bottom 5 side by side, look for what actually separates
47them, and be willing to conclude something the user will not like:
48 
49- Hook formula. Which numbers from `hooks.json` are in the top 5?
50- Format. Text, document, image, video.
51- Length.
52- Theme.
53- Day and time - check this **last**, and only if the other four show nothing.
54 It is almost never the cause, and it is where people want it to be.
55- First-hour comments. Posts the user replied to inside an hour versus not.
56 
57State the finding as a claim with the evidence attached, and say how confident
58it is. With 30 posts you can see a pattern; with 6 you cannot, and you should
59say that instead of inventing one.
60 
61## Output
62 
63```
64AUDIT · 31 posts · Jun 12 - Sep 5
65 
66TOP 5 BY ENGAGEMENT RATE
67 8.1% #3 Mistake "$18,000 is what no contract cost me" 1,940 imp
68 6.4% #20 Walk-Away "I fired my highest-paying client" 2,210 imp
69 ...
70 
71BOTTOM 5
72 0.4% #5 List "7 tools every founder needs" 11,400 imp
73 ...
74 
75WHAT THE DATA SAYS
761. Posts where you were the one who looked bad: mean 6.2% vs 1.1% for
77 everything else. n=6. This is your strongest signal and it is not close.
782. Tool listicles get impressions and nothing else. High reach, no comments,
79 no leads. Three of your bottom five.
803. Day of week shows nothing. Your Tuesday mean and your Friday mean are
81 inside the noise. Stop optimising it.
82 
83STOP: listicles about tools.
84DO MORE: the ones with a cost you paid, and a number.
85```
86 
87Then hand the conclusions to `/li-plan` so next week's plan is built on the
88user's own evidence rather than on defaults.
89 

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