Linkedin Profile Optimizer logo

Linkedin Profile Optimizer

CommunityPopular
sergebulaev
linkedin-profile-optimizer

Audit and rewrite a LinkedIn profile end-to-end for 2026: headline, About 7-step, Featured, banner, photo, Experience metrics, Skills, custom URL, recommendations. Triggers on "review my profile", "rewrite my headline", "fix my About", "optimize banner", "profile audit", "LinkedIn bio". Converts resume-style profiles to ones that convert 3-5x better. Not for writing feed content (use linkedin-post-writer).

Overview

Publishersergebulaev
Repositorylinkedin-skills
Skill namelinkedin-profile-optimizer
Stars
2.6K
Forks
456
Bundled files
5
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sergebulaev on GitHub. Read the source before you install it.

Installation

Install the Linkedin Profile Optimizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sergebulaev/linkedin-skills.git /tmp/linkedin-skills
mkdir -p .claude/skills
cp -r /tmp/linkedin-skills/skills/linkedin-profile-optimizer .claude/skills/linkedin-profile-optimizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linkedin Profile Optimizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Linkedin Profile Optimizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Linkedin Profile Optimizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

LinkedIn Profile Optimizer

Audit the nine components of a LinkedIn profile (photo, banner, headline, About, Featured, Experience, Skills, custom URL, recommendations) against 2026 best practices, then rewrite each section that needs it. Optimized profiles get ~3.9x more views and convert visitors 3-5x better than default/resume-style profiles.

When to use

  • User pastes their LinkedIn profile URL and asks for an audit
  • User wants to rewrite their headline, About section, or Featured section
  • User is launching a content strategy and needs the profile to match
  • Any of: "review my profile", "fix my headline", "optimize bio", "profile audit", "LinkedIn optimization"

Input

  • Profile URL (or screenshots of sections)
  • Goal: clients / job seeking / authority — Featured and CTA vary by goal
  • Optional: draft content to grade against the existing profile

Output

A structured audit + rewrite in this shape:

  1. Scorecard (9 sections, pass/fail/needs-work)
  2. Priority fixes (ranked by impact)
  3. Before → After rewrites for each failing section
  4. Expected uplift (based on benchmark data)

Steps

  1. Intake. Collect profile state + goal. Flag missing sections.
  2. Score each of 9 sections against the checklist (see references/).
  3. Rewrite headline using [What You Do] | [Who You Help] [Achieve What Result] — fit all 220 chars.
  4. Rebuild About with 7-step structure; verify first 265-275 chars hook before "see more".
  5. Curate Featured (3 strong items) matched to the goal:
    • Clients: lead magnet + case study with results + calendar link
    • Job seeking: portfolio + best work samples + top-performing post
    • Authority: best content + media/podcast features + newsletter signup
  6. Rewrite Experience bullets as action verb + specific metric. Add 5+ skills per role. Pin top 3 skills.
  7. Claim custom URL (linkedin.com/in/firstnamelastname, not the -123abc456 default).
  8. Draft recommendation requests with specifics ("about [project/skill]") — don't send LinkedIn's generic template.
  9. Deliver before/after diff + expected uplift (3.9x views, 3-5x conversion, 71% more likely to land interviews).

Nine-component scorecard

#SectionPass criteria (2026)
1Photo≥400x400, face fills 60% of frame, <3 years old, natural light, slight smile
2Banner1584x396, text in right 2/3, high contrast, includes value prop + CTA, tests well on mobile
3HeadlineUses all 220 chars; format `[What You Do]
4About200-300 words, first-person, 7-step structure, hook in first 265-275 chars
5Featured3 items, matched to goal, custom 1200x627 thumbnails
6ExperienceEvery bullet = action verb + metric, 5+ skills per role, media attached
7Skills50 listed, top 3 pinned, mirrors target job descriptions, ≥1 endorsement each
8Custom URLlinkedin.com/in/firstnamelastname (not the default hash)
9RecommendationsAt least 3 recent, specific (not generic), from diverse contexts

Key benchmarks (from co.actor research)

  • Optimized About sections: 3.9x more views
  • 5+ listed skills: 3x more connection requests
  • Comprehensive profile: 71% more likely to land interviews
  • Featured section content: 30% longer viewing time
  • Personal founder profile vs company page: 315% more engagement, 270% more conversions

Hard rules

Global voice rules: see root SKILL.md §Voice rules. Additional skill-specific rules:

  • First person ("I help...") never third person ("Jane is a passionate...")
  • Never "passionate thought leader" / "driven professional" / "results-oriented" (profile-specific AI vocab)
  • Avoid wall-of-text. Use line breaks in About section
  • 80% of users leave Featured empty. Filling it is a free edge

Reference files

  • references/profile-headline-formulas.md — 220-char formula + before/after examples
  • references/about-section-templates.md — 7-step structure with character budgets
  • references/featured-section-playbook.md — goal-matched content types
  • references/banner-photo-specs.md — dimensions, composition, mobile test
  • references/experience-skills-rules.md — bullet rewriting + skills strategy + custom URL + recommendations

Related skills

  • linkedin-content-planner — post pillars should echo the profile's headline/About thesis
  • linkedin-post-writer — Featured section rotates quarterly; pin your flagship post
  • linkedin-humanizer — scrub profile copy for the same AI tells we scrub from posts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Linkedin Profile Optimizer AI skill do?

Audit and rewrite a LinkedIn profile end-to-end for 2026: headline, About 7-step, Featured, banner, photo, Experience metrics, Skills, custom URL, recommendations. Triggers on "review my profile", "rewrite my headline", "fix my About", "optimize banner", "profile audit", "LinkedIn bio". Converts resume-style profiles to ones that convert 3-5x better. Not for writing feed content (use linkedin-post-writer).

Why use Linkedin Profile Optimizer on TypingMind?

Because you install it once and use it with any model. Linkedin Profile Optimizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Linkedin Profile Optimizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sergebulaev/linkedin-skills/tree/main/skills/linkedin-profile-optimizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Linkedin Profile Optimizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Linkedin Profile Optimizer?

As many as you like. As long as a model supports skills, you can use Linkedin Profile Optimizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Linkedin Profile Optimizer AI skill free?

Yes. It is published on GitHub by sergebulaev under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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