jakeschincariol/linkedin-agent-skill

li-inbox

- Triage the LinkedIn inbox - sort connection requests and DMs into leads, recruiters, peers and spam, and draft the replies worth sending.

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-inbox

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-inbox

Most LinkedIn inboxes are 80% noise, and the cost of that noise is that the 20% goes unanswered for a week. This skill separates them, then writes only what is worth writing.

Input

The user pastes the messages. Screenshots are fine. Do not log into their account or read their inbox with a browser tool.

Sort into five

bucketsignalaction
LEADdescribes a problem the user solves, or asks about working togetherreply today, full answer
RECRUITERa role, a company, a salary bandreply if the role is real, one line if not
PEERsomeone in the same field with something to sayreply this week, keep it human
ASKwants advice, time, an intro, a favourreply if it is cheap and specific, decline cleanly if not
SPAMagency pitch, lead-gen sequence, crypto, "quick question" with no questionarchive, no reply

Print the counts first. Seeing "3 leads, 2 recruiters, 41 spam" is most of the value.

Detecting a sequence

Automated outreach has a shape: an invite note with no specifics, a message that arrives within minutes of the accept, "quick question", "I noticed you're in {industry}", a calendar link in message one, then a bump exactly four days later. When you see it, mark it SPAM and say which tell gave it away. The user does not owe a reply to a script.

Replies

  • LEAD - answer the actual question in the message, in full, for free. If

it is a fit, the offer is one sentence at the end. If it is not, say so and point them somewhere useful. Both outcomes are good.

  • RECRUITER - if the role is genuinely interesting, ask the three things

the message left out: comp band, level, and whether it is in-office. If it is not, one line: not looking, happy to refer, and mean the refer.

  • ASK - if it costs under ten minutes and is specific, do it. If it is

"can I pick your brain", decline in one warm sentence and give them the one answer you would have given on the call. That is the polite version and it is also the more useful one.

  • DECLINES are short, warm and final. No "let's revisit in Q3" if there is

no Q3.

Output

Grouped by bucket, counts first, drafts only for the buckets that get replies, each one humanized. Then the gate: the user sends them.

INBOX  ·  52 items  ·  3 LEAD, 2 RECRUITER, 4 PEER, 2 ASK, 41 SPAM

SPAM  (41) - archive. 38 are the same sequence: no-specifics invite,
"quick question" within 4 minutes of accept, calendar link in message one.

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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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