li-audit
Skill3 starsChanged 30 days agoMITView on GitHub
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.
7 other projects use a version of this, 6 of them edited.
Adopted by
Most recently taken up by
- furkanbilalakyuz/linkedin-agent-skill-tredited, first seen Nov 8, 2026
- HemanthMChakravarthy/linkedin-agent-skillstheir own version, first seen Nov 8, 2026
- mubaid/dsh-linkedin-agentedited, first seen Oct 25, 2026
- robbiehaynes/job-searchedited, first seen Oct 20, 2026
- avi691/linkedin-agent-skilledited, first seen Oct 8, 2026
- nessalazne/linkedin-agent-skilledited, first seen Sep 19, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 6
- Jakeschincariol/linkedin-agent-skill★ 51498% same
- avi691/linkedin-agent-skill★ 598% same
- nessalazne/linkedin-agent-skill★ 398% same
- furkanbilalakyuz/linkedin-agent-skill-tr★ 081% same
- mubaid/dsh-linkedin-agent★ 098% same
- robbiehaynes/job-search★ 098% same
Wrote their own version · 1
- HemanthMChakravarthy/linkedin-agent-skills★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository11 other files it gives its agents.
- li-carouselSkill
- li-commentSkill
- li-dmSkill
- li-humanSkill
- li-inboxSkill
- li-planSkill
- li-postSkill
- li-profileSkill
- li-publishSkill
- li-replySkill
- li-repurposeSkill
The file
We split this skill into the rules it gives an agent
1 rule. 1 is common, 0 are distinctive, and 0 are very specific to this project.
1
Common
Most of the 7 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 18
Ask for whichever the user has:
“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 ~/.linkedin-agent/log.md if it exists, since it records which
hook formula each post used.
PostZen cannot read LinkedIn analytics yet (waiting on LinkedIn's approval), so the numbers still come from the user, even with PostZen connected. The log's PostZen post ids and URLs help match each pasted row to its post.
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

