jakeschincariol/linkedin-agent-skill

li-reply

- Handle the replies under the user's own LinkedIn posts - draft answers to every comment, sorted by which ones are worth answering.

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-reply

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-reply

The reply thread under your own post is where reach is actually decided. Every reply is a second engagement event on the post, and the first hour of replies does most of the work. But the value is not equal across comments, so this skill sorts before it writes.

Input

The user pastes the comments, ideally with names and roles. Screenshots are fine. Do not scrape the thread with a browser tool.

Triage first

Sort every comment into one of five buckets and say the count out loud:

bucketwhat it iswhat it gets
LEADsomeone describing the problem you solvea real answer + a soft open door
SUBSTANCEadds data, disagrees, extendsthe longest reply on the thread
PEERa name worth being seen next toa reply that gives them something
SUPPORT"great post", 🔥, a taga like, and a 3-8 word reply at most
NOISEpitch, spam, bad faithnothing, or one line and out

Then write in that order, and stop writing when the value stops.

How to reply

  • Answer the actual question. If someone asks how, tell them how, in the

reply. Do not send them to a DM to hear the answer they could have had.

  • Use their name once, at the start, and never with an exclamation mark.
  • Match their length. A two-line comment does not get a six-line reply.
  • To a critic: concede the true part first, in their words, then hold the

line on the part you believe. Never delete, never get defensive, never reply twice on the same thread.

  • To a lead: answer fully in public. The open door is one sentence at the

end, and it is an offer of help, not a pitch. Public value is what makes the next person DM you.

  • To a pitch in your comments: ignore it. Replying gives it reach.

Output

One block, grouped by bucket, each reply copy-ready and already humanized:

REPLIES  ·  17 comments  ·  1 LEAD, 3 SUBSTANCE, 4 PEER, 8 SUPPORT, 1 NOISE

LEAD
@Sarah Chen - "we have the same problem with proposals"
> The part that fixed it for us was pulling the pricing table out of the doc
> entirely and sending it as its own page. Cut a whole review round. Happy to
> send you the template if it is useful.

SUBSTANCE
@Marcus Webb - disagrees on cadence
> Fair, and 4x a week only worked because I had 8 months of posts behind it.
> From a standing start I would do what you said.

SUPPORT  (like these, reply to the first three)
@... "This is great" -> Thanks Dan.
...

NOISE  (1)
Skipped: an agency pitch. Replying gives it reach.

Then the gate: nothing is posted until the user says yes. They paste the replies themselves.

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.

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업데이트
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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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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.

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1
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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