jakeschincariol/linkedin-agent-skill

li-comment

- Write comments on other people's LinkedIn posts that read as a person with an opinion, not a bot.

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-comment

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-comment

Commenting is the highest-leverage thing on LinkedIn and the easiest to do badly. A comment on a post with 400 reactions gets seen by more people than most of your own posts. A generic one gets seen by nobody and costs you credibility with the author.

Input

The user pastes the post text (and the author's name and role if they have it). If they paste a screenshot, read it. If they give you a URL you cannot open, ask them to paste the text - do not guess what the post said, and do not use browser automation to scrape the feed.

The nine comment types

Pick by what the post actually is. Never default to type 1.

#typewhenshape
1Add a datumpost makes a claim you can support with a number"We saw the same thing: 40% of our..."
2Add the missing casepost is right but incomplete"This holds until {condition}. Then..."
3Respectful disagreeyou genuinely think it is wrongname the agreement first, then the fork
4Extend one lineone sentence in the post is the good onequote it, then build on it
5Ask the real questionpost skipped the hard partone question, specific, no "curious to hear"
6The receiptyou have done the thing they describedwhat happened, in two sentences
7The correctionthere is a factual errorbe right, be brief, be kind, be sure
8The reframethe post has the right facts and the wrong frame"Another way to read this:"
9The one-linerthe post needs nothing, you want presenceunder 12 words, must be funny or true

Rules

  • 2 to 4 sentences. Longer reads as a hijack. Shorter reads as filler.
  • Never open with "Great post", "Love this", "So true", "Couldn't agree

more", "This resonates", or the author's first name followed by an exclamation mark. All six are invisible.

  • No emoji openers. No 🔥 or 👏 as a first character.
  • Never restate the post. The author knows what they wrote and so does

everyone reading.

  • One idea. A comment with two points reads as a blog attempt.
  • Say the specific thing. If the comment could sit under any post on the

topic, it is not a comment, it is noise.

  • Disagreement is allowed and works, but the agreement has to come first

and be real.

Output

Give two options of different types, labelled, plus a one-line reason for the one you would post. Run both through /li-human first - a comment with an em dash in it is more obviously machine-written than a post, because comments are short and people read them closely.

COMMENT OPTIONS  (on @author's post about hiring)

[6 · Receipt]
We tried the no-resume version of this for 3 hires last year. Two were the
best hires we made. The third was a disaster, and the difference was whether
they had done the actual job before, not how they interviewed.

[3 · Respectful disagree]
Agree on the signal problem. But the fix that worked for us was not removing
the resume, it was giving every candidate the same 90-minute paid task. Same
outcome, far less argument internally.

Post the first. It is your own data and it concedes a failure, which is the
part people reply to.

Batch mode

If the user wants an engagement round, ask for the 5-10 posts as pasted text in one message, return one comment each in a single block, and keep a running note of who they have already commented on this week in ~/.claude/linkedin/log.md. Commenting on the same three people every day is visible and it looks like what it is.

Never

Do not auto-post. Do not use a browser tool to publish comments on the user's behalf. Automated posting and scraping both violate LinkedIn's User Agreement and put the account at risk. This skill writes the comment. The user posts it.

관련 작업

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- 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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- 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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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.

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li-inbox

- 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.

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2.4천
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