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.

ソースを見る

使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/jakeschincariol/linkedin-agent-skill --skill li-profile

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

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.

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jakeschincariol
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導入数
1
GitHub Stars
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更新日
9月17日
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導入数
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GitHub Stars
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更新日
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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.

導入数
1
GitHub Stars
2393
更新日
9月17日