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Score a LinkedIn profile out of 100 against a 12-part rubric and rewrite the parts that lose points - headline, about, experience, featured, banner. Use when the user says "optimize my profile", "score my LinkedIn", "rewrite my headline", "fix my about section", or pastes their profile and asks how it reads.

Requires /li-human
Use this Skill: https://skilld.dev/gh/jakeschincariol/linkedin-agent-skill/li-profile

Nothing lands on disk. Nothing to clean up.

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Edit a local copy. It keeps the author and licence.

SKILL.md

≈80 tokens for metadata: the name and description. ≈727 when used: this file. ≈616 more on demand in 1 file.

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Example settings and source

The example uses a 200k-token context and default Claude Code settings. The count includes the name, description, separators, and when_to_use when present. Codex also counts local file paths.

Skit's source and limits: Codex 0.160.1, Claude Code 2.1.292.

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.

Source: SKILL.md on GitHub

No rule matched.

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

skilld run checks every file with the same patterns. It asks for approval before it loads a Skill with a behavior marked Needs approval.

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  • Gen Agent Trust Hub29d

    The skill is a LinkedIn profile optimizer that analyzes user-provided text against a local JSON rubric. It performs text-based scoring and rewriting without any network access, external dependencies, or sensitive file operations.

  • Socket29d

    No alerts

  • Snyk29d

    Risk: LOW · No issues

Signed by skilld at add2c23. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 45 minutes ago.

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