jakeschincariol/linkedin-agent-skill

li-profile

- Score a LinkedIn profile out of 100 against a 12-part rubric and rewrite the parts that lose points - headline, about, experience, featured, banner.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-profile

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

li-profile

A profile is not a resume. A resume answers "what have you done". A profile answers "should I message this person", and it answers it in about four seconds, from the headline and the first two lines of the about.

Input

Ask the user to paste: headline, about section, current role and the last two experience entries, plus whether they have a banner and featured section. A screenshot of the top card is enough for the first pass. Do not log into LinkedIn on their behalf.

Score it

Read rubric.json in this folder. Twelve items, 100 points, each with what full marks looks like. Score every item, show the table, and give the total. Be honest - most profiles land in the 30s and 40s on the first pass, and a generous score is useless.

PROFILE SCORE  41/100

  headline            3/12   job title only, no outcome, no audience
  about first 2 lines 2/10   opens with "passionate about"
  about body          4/10   history, not offer
  featured            0/8    empty
  banner              0/6    default blue
  ...

Then rewrite, in this order

Fix in descending order of points lost. Do not rewrite everything at once - the user has to actually paste each of these in.

1. Headline (220 characters). The formula that works: {what you do for whom} | {proof} | {how to start}. Not your job title. Not "Helping X do Y" as the first three words, which every second profile now opens with. Give three options.

2. About, first two lines. Everything after line 2 is behind "see more" on mobile, so those two lines are the whole about section for most readers. They must state who you help and what changes. No "passionate", no "results-driven", no third-person bio, no opening with your own name.

3. About body. Written to one reader, in the second person. Structure: the problem they have, what you do about it, one piece of proof with a number, what to do next. Under 1,400 characters even though the limit is 2,600.

4. Featured. Three items: the best post, the proof asset, the way to contact. An empty featured section is eight points and the only place on the profile you fully control.

5. Experience. Each role gets one line of scope and two to three bullets that are outcomes with numbers, not duties. Cut anything older than ten years to a single line.

6. Banner. One sentence of positioning and one way to reach you. The default blue gradient is the clearest signal on the page that nobody is home.

Output

Score table, then the rewrites as copy-ready blocks in fix-first order, each one already run through /li-human. Re-score at the end and show the delta honestly - if the rewrite gets to 88 and not 98, say 88, and say what the remaining points need (usually recommendations, a real banner and posting history, none of which a rewrite can create).

Nothing is saved to LinkedIn by this skill. The user pastes each section in.

관련 작업

같은 저장소의 Skills

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모든 Skills
jakeschincariol
커뮤니티

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.

설치 수
1
GitHub Stars
2.4천
업데이트
9월 17일
jakeschincariol
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li-comment

- 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.

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1
GitHub Stars
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업데이트
9월 17일
jakeschincariol
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li-dm

- 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.

설치 수
1
GitHub Stars
2.4천
업데이트
9월 17일
jakeschincariol
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li-human

- 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.

설치 수
1
GitHub Stars
2.4천
업데이트
9월 17일