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cccskills
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li-audit

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구LinkedIn
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  • SKILL.md3.3 KB

SKILL.md(원문)

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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:

metrichowwhat it tells you
Engagement rate(reactions + comments + reposts) / impressionswhether the post earned its reach
Comment ratiocomments / reactionswhether it started something or just got a nod
Reach multipleimpressions / follower countwhether it travelled past the existing audience
Save/send rateif availablethe 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.

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Build a LinkedIn document post (carousel) - slide-by-slide copy, the cover that earns the swipe, and the PDF to upload. Use when the user says "carousel", "document post", "slides for LinkedIn", "turn this into a carousel", or has a list-shaped idea that would die as a text post.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

Write comments on other people's LinkedIn posts that read as a person with an opinion, not a bot. Use when the user pastes a post and wants a comment, says "comment on this", "engage with this", "what do I say here", or wants a batch of comments for their engagement round.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

li-dm

무료

Write connection notes and DM follow-ups that get replies - the 200-character invite, the first message, and the two follow-ups. Use when the user says "write a connection request", "DM this person", "outreach message", "how do I follow up", or is reaching out to someone specific on LinkedIn.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

li-human

무료

Strip the machine fingerprint out of any draft - em dashes, AI slop words, invisible watermark characters - and score it against a five-check detection panel before it goes out. Use whenever text needs to sound human, when the user says humanize, "does this sound like AI", "remove the em dashes", "de-slop this", "will this get flagged", or before any LinkedIn post, comment, reply or DM is shown to the user.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

li-inbox

무료

Triage the LinkedIn inbox - sort connection requests and DMs into leads, recruiters, peers and spam, and draft the replies worth sending. Use when the user says "my inbox is a mess", "triage my DMs", "should I reply to this", pastes a batch of LinkedIn messages, or is drowning in connection requests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

li-plan

무료

Build the week on LinkedIn - what to post, when to post it, and who to engage with. Use when the user says "plan my week", "what should I post", "content calendar", "I have nothing to post about", or wants a posting schedule and an engagement list.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Jakeschincariol/linkedin-agent-skill2.4천2026년 9월 17일 업데이트

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