How to Find Someone’s LinkedIn Skills: Complete Guide 2026

LinkedIn skills have become the number one criterion for qualifying candidates and evaluating B2B prospects. According to LinkedIn data, recruiters are now 5 times more likely to search by skills than by degree, and 26% of job postings in 2026 no longer require a college degree—a 16% increase since 2020.

But here’s the problem: LinkedIn only shows you 3 to 5 skills on a public profile, while most professionals list 20, 30, or even 50 skills. How do you access the complete list without spending hours clicking through every profile?

In this guide, you’ll learn how to automatically extract all LinkedIn skills from someone using just their name, email, or profile URL. Whether you’re a recruiter, SDR, or growth marketer, you’ll discover concrete methods to qualify your leads and enrich your CRM with actionable data.

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What you’ll learn (and the expected results)

After following this guide, you’ll be able to:

• Extract the complete skills list from any LinkedIn profile in seconds
• Automatically qualify your leads based on their technical or business skills
• Source candidates with precise skill sets for your open positions
• Segment your prospecting lists by expertise (SQL, Python, HubSpot, etc.)
• Enrich your CRM with actionable data to personalize your campaigns

Expected outcome: You’ll go from 5 minutes per profile to 3 seconds per lead, with a data completion rate of 85%+ (vs 20% manually).

Prerequisites

Before you start, make sure you have:

• Starting data: A list of names OR emails OR LinkedIn profile URLs
• Google Sheets: To store and manipulate your data (free)
• Derrick App: Free account with 200 credits included (no credit card required)
• Estimated time: 15 minutes to set up your first workflow + 3 seconds per lead afterwards

Why LinkedIn skills are crucial in 2026

Before diving into the “how,” let’s understand the “why.” LinkedIn skills are no longer just a profile detail—they’ve become the primary qualification signal in three B2B contexts:

1. Skills-based hiring

Sarah, Head of Talent at a SaaS scale-up, needs to hire a Data Engineer. Problem: out of 300 applications, only 40% have the right degree, but 70% have the right skills (Python, SQL, Airflow). By filtering by skills rather than education, she triples her talent pool.

According to LinkedIn, skills-based hiring can expand the talent pool by 6.1x globally. Companies that recruit this way reduce their time-to-hire by 25% and improve retention by 18%.

2. B2B lead qualification

Mike, SDR at a marketing software company, prospects Growth Marketers. He extracts skills from 500 LinkedIn profiles and discovers that:

• 120 master “Marketing Automation” → priority audience for his tool
• 80 have “SQL” or “Python” → technical profiles to approach differently
• 60 mention “HubSpot” → competitor installed, adapted pitch needed

Result: His response rate jumps from 8% to 18% thanks to skills-based segmentation.

3. Market analysis and competitive intelligence

Emily, Sales Ops Manager, analyzes the skills of 2,000 employees at her 5 main competitors. She identifies:

• Which technologies they use (tech stack)
• Which certifications they value
• Which emerging skills they’re recruiting for

This intelligence allows her to anticipate their strategic moves and adjust her own hiring plan. LinkedIn skills are therefore much more than a search filter: they’re an economic intelligence tool.

How LinkedIn skills work: what you need to know

The 3 types of skills on LinkedIn

LinkedIn categorizes skills into three groups:

1. Hard Skills (technical competencies): Programming languages, tools, software, certifications
   • Examples: Python, Salesforce, Google Analytics, SQL, Adobe Photoshop
   • Easily verifiable and measurable
2. Soft Skills (human competencies): Communication, leadership, problem-solving
   • Examples: Negotiation, Team Management, Creativity, Critical Thinking
   • More subjective but crucial for cultural fit
3. Industry Skills (sector-specific expertise): Industry-specific knowledge
   • Examples: GDPR, Lean Six Sigma, IFRS, HIPAA Compliance
   • Indicate vertical expertise

Important: A LinkedIn profile can contain up to 50 skills, but only 3 to 5 appear publicly without a connection. The rest is hidden behind the “See all skills” button—hence the value of automating extraction.

Explicit vs implicit skills

LinkedIn also distinguishes:

• Explicit skills: Listed in the “Skills” section of the profile
• Implicit skills: Mentioned in the summary, positions, project descriptions

Extraction tools (including Derrick) capture both types, providing a much more complete view than the simple “Skills” section.

The endorsement system: credibility indicator

Each skill can be endorsed (validated) by other LinkedIn members. The more endorsements a skill has, the more credible it is. Some tools also extract this endorsement count, allowing you to sort skills by recognition level.

Now that you understand the mechanics, let’s get into action.

How to find LinkedIn skills: step-by-step guide

Method 1: From a first and last name

Use case: You have a list of prospects with their full name, but no LinkedIn URL.

Step 1: Prepare your data in Google Sheets

Create a Google Sheets with at least two columns:

• Column A: First Name
• Column B: Last Name

Example:

Tip: Adding the company name increases accuracy by 40% in case of name duplicates.

Expected result: A clean list, no empty rows, with correctly spelled names.

Step 2: Install the Derrick extension

1. Open your Google Sheets
2. Go to Extensions > Add-ons > Get add-ons
3. Search for “Derrick” in the marketplace
4. Click Install and authorize permissions

Expected result: A new “Derrick” menu appears in the Google Sheets toolbar.

Step 3: Search for LinkedIn profiles

1. Select your First Name and Last Name columns
2. Click Derrick > LinkedIn Profile Finder
3. Configure source columns:
   • First Name: Column A
   • Last Name: Column B
   • Company: Column C (if available)
4. Click Launch search

Derrick will query LinkedIn and return the corresponding profile URL in a new column.

Expected result: After 30 seconds to 2 minutes (depending on your list size), you get a new column with LinkedIn profile URLs.

Average success rate: 75-85% depending on data quality. Profiles not found are often due to duplicates or private profiles.

Step 4: Extract skills from profiles

Now that you have LinkedIn URLs, you can extract all profile data, including skills.

1. Select the column containing LinkedIn URLs
2. Click Derrick > LinkedIn Profile Scraper
3. Check “Skills” in the attributes to extract (by default, all 50+ attributes are checked)
4. Click Enrich

Derrick will scrape each profile and create new columns with:

• Complete skills list
• Number of endorsements per skill
• Work experience
• Education
• Location
• And 40+ other attributes

Expected result: A new “Skills” column contains all skills separated by commas. For example:

Python, SQL, Machine Learning, Data Analysis, Tableau, AWS, Docker, Git, Agile, Scrum

Processing time: 3 seconds per profile on average. For 100 leads, count 5 minutes.

Link to complete workflow: Find skills from first and last name

Method 2: From an email address

Use case: You have professional emails (e.g., from a campaign or web form) and want to enrich these contacts with their LinkedIn skills.

Step 1: Prepare your email list

In Google Sheets, create a column with professional email addresses:

Important: Personal emails (Gmail, Outlook, etc.) give worse results. Prioritize professional emails with company domains.

Step 2: Search for associated LinkedIn profiles

1. Select the Email column
2. Click Derrick > LinkedIn Profile Finder > By Email
3. Launch the search

Derrick uses multiple methods to associate an email with a LinkedIn profile:

• Reverse search by company domain
• Analysis of firstname.lastname@company.com pattern
• Cross-referencing with other public databases

Expected result: LinkedIn profile URL in a new column.

Success rate: 60-70% (less precise than name search, but useful for cold leads).

Step 3: Extract skills

Follow the same procedure as Method 1, Step 4:

• Select the LinkedIn URLs column
• Launch LinkedIn Profile Scraper
• Get the complete skills list

Link to complete workflow: Find skills from an email

Method 3: From a LinkedIn profile URL (fastest method)

Use case: You already have LinkedIn URLs (Sales Navigator export, manual list, event scraping).

Step 1: List your LinkedIn URLs

Paste your URLs into a Google Sheets column:

Accepted format: Full URL or short profile (/in/name-surname-123/).

Step 2: Extract skills directly

1. Select the URLs column
2. Derrick > LinkedIn Profile Scraper
3. Check “Skills” and click Enrich

Expected result: Complete skills list in 3 seconds per profile.

Advantage: This is the fastest and most accurate method (100% match rate since you already have the URL).

Link to complete workflow: Find skills from a LinkedIn URL

Final result: what you’ve accomplished

At this point, you have:

✅ An enriched list of all your prospects with their complete LinkedIn skills

✅ Structured columns ready to be filtered, segmented, and analyzed

✅ A repeatable workflow for any new list

Example of final result in Google Sheets:

Numbers: For 500 leads, you now have:

• 500 enriched profiles in 25 minutes (vs 40 hours manually)
• Completion rate: 85% (vs 20% manually)
• Cost: 500 Derrick credits (~$2 with Small plan, vs $0 but 40h of human time)

You can now export this file to your CRM, create segments by skills, or personalize your outbound campaigns.

Common mistakes (and how to fix them)

Problem 1: Profiles not found or incomplete

Symptom: Derrick returns “Profile not found” or finds the wrong profile.

Impact: You lose 15-25% of your potential leads and risk contacting the wrong people.

Solution:

1. Check spelling: “Jean-François” vs “Jean Francois” can make a difference
2. Add company name: Increases accuracy by 40% in case of name duplicates
3. Use professional email: sarah.johnson@aircall.io is more reliable than sarah.j@gmail.com
4. Check profile visibility: Some profiles are private or restricted
5. Special cases: Names with particles (“de”, “van”, “von”) must be entered correctly

Correction example:

• ❌ “Sarah De La Tour” (space after “De”)
• ✅ “Sarah de la Tour” (lowercase for particles)

Problem 2: Skills list too long and unstructured

Symptom: The Skills column contains 40 skills separated by commas, unreadable and unusable.

Impact: Impossible to filter or segment effectively. You have to sort manually.

Solution:

1. Use Google Sheets formulas to separate skills into distinct columns: =SPLIT(C2, ",") This will create one column per skill.
2. Create detection columns with conditional formulas: =IF(ISNUMBER(SEARCH("Python", C2)), "YES", "NO") Allows you to create a “Has Python?” column for quick filtering.
3. Export to your CRM: HubSpot, Salesforce, and Pipedrive allow creating custom multi-value fields for skills.
4. Use Derrick’s AI: The Ask Claude or Ask OpenAI function can analyze the list and automatically extract the most relevant skills according to your criteria.

Example automation with Ask Claude:

Prompt: "Extract the 5 most relevant technical skills for a Data Engineer position"
Input: Python, SQL, Communication, Project Management, AWS, Docker, Excel, PowerPoint
Output: Python, SQL, AWS, Docker

Problem 3: Outdated data or skills not up to date

Symptom: Extracted skills are from 2 years ago, the person has changed roles.

Impact: You’re prospecting with outdated arguments. Conversion rate drops.

Solution:

1. Check last LinkedIn post date: If the person is active, their profile is likely up to date
2. Re-scrape periodically: Every 3-6 months for priority leads
3. Combine with other sources: GitHub (for devs), G2 reviews (for tools used)
4. Filter by last update date: Derrick also extracts the profile’s last modification date

Tip: Create an automated workflow that re-scrapes your top 100 leads every quarter.

Problem 4: LinkedIn scraping limits and blocking risk

Symptom: LinkedIn displays a CAPTCHA or limits access after too many requests.

Impact: Workflow interrupted, risk of temporary account ban.

Solution:

1. Use Derrick in the cloud: Requests go through Derrick’s infrastructure, not your personal account
2. Respect rate limits: Maximum 500-1,000 profiles per day
3. Spread your scraping: 100 profiles in the morning, 100 in the afternoon
4. Don’t scrape from your main account: Use a dedicated account if doing volume
5. Legal alternative: Derrick respects LinkedIn ToS by scraping only public data

Important: LinkedIn scraping is legal if you only collect public data (HiQ vs LinkedIn court decision, 2026). Derrick never collects private or protected data.

Problem 5: Difficulty segmenting by skill level

Symptom: You have the skills list, but not the level (beginner, intermediate, expert).

Impact: Impossible to prioritize candidates/leads based on their actual expertise level.

Solution:

1. Use endorsements: A skill with 99+ endorsements indicates recognized expertise
2. Analyze current position: “Senior Data Engineer” with Python = likely expert level
3. Combine with experience: If Python is listed and the person has 8 years of experience as a dev, they’re a senior
4. Derrick’s AI Lead Scoring function: Automatically assigns a qualification score based on context

Example of automated scoring:

Criteria: 
- Skill sought: Python
- Endorsements: >50
- Current position: contains "Senior" or "Lead"
- Experience: >5 years

Result: Score A (priority candidate)

Going further: advanced use cases

1. Create prospecting segments by tech stack

Objective: Qualify your B2B SaaS leads based on the tools they master.

Example: You’re selling a marketing automation tool. You want to target Growth Marketers who already use HubSpot (for upsell) vs those who use Mailchimp (easier migration).

Workflow:

1. Extract skills from 1,000 Growth Marketers
2. Create a filter column: =IF(ISNUMBER(SEARCH("HubSpot", Skills)), "HubSpot User", IF(ISNUMBER(SEARCH("Mailchimp", Skills)), "Mailchimp User", "Other"))
3. Segment your campaigns by stack:
   • HubSpot Users → Message: “Complete your HubSpot stack with…”
   • Mailchimp Users → Message: “Switch from Mailchimp to an all-in-one solution…”

Measured result: +35% response rate vs generic message.

2. Sourcing candidates with precise technical criteria

Objective: Recruit a niche profile (e.g., “Backend Engineer with Rust, Kubernetes, and fintech experience”).

Workflow:

1. Export 500 profiles from Sales Navigator with “Backend Engineer” filter
2. Extract skills via Derrick
3. Filter: Skills CONTAINS "Rust" AND "Kubernetes" AND Company CONTAINS "fintech|bank|payment"
4. Result: 12 ultra-qualified candidates instead of 500 to sort manually

Time saved: 8 hours of screening → 15 minutes.

3. Automated competitive intelligence

Objective: Analyze the skills of 500 employees at your competitor to identify their tech stack.

Workflow:

1. Export all competitor employees via LinkedIn Company Scraper
2. Extract skills from all profiles
3. Create a pivot table of the most frequent skills
4. Identify the technologies they use (AWS vs GCP, React vs Angular, etc.)

Insight obtained: “Our competitor is massively recruiting on Kafka and Kubernetes → they’re probably preparing a migration to event streaming.”

Related link: 10 Best LinkedIn Scraping Tools

Legal aspects and GDPR compliance

What’s legal

✅ Scraping public data: Information publicly displayed on LinkedIn (name, job title, skills) is legally accessible (HiQ vs LinkedIn decision, 2026).

✅ Enriching your CRM: You can store this data if you have a legitimate interest (business relationship, recruitment).

✅ B2B prospecting: B2B email prospecting (professional address) is authorized without prior consent.

What’s prohibited

❌ Reselling data: LinkedIn data cannot be resold to third parties.

❌ Collecting private data: Personal emails, private phone numbers, InMail messages.

❌ Spamming: Sending automated mass messages via LinkedIn InMail (LinkedIn ToS violation).

GDPR best practices

1. Inform people: Add a mention “We found your profile on LinkedIn” in your first contact
2. Honor deletion requests: If someone asks you to delete their data, do it within 30 days
3. Limit retention period: Don’t keep cold prospect data for more than 3 years
4. Document your legal basis: “Legitimate interest for B2B commercial prospecting”

In summary: Derrick collects only public LinkedIn data. It’s legal, but you must respect GDPR in how you use it.

Conclusion: where to start now

You now know how to extract and leverage LinkedIn skills to qualify your leads, source candidates, or analyze competition. Here’s your immediate action plan:

Step 1: Create a free Derrick account (200 credits offered, no CC)

Step 2: Choose your method based on your data:

• You have names → Method 1 (by name)
• You have emails → Method 2 (by email)
• You have LinkedIn URLs → Method 3 (by URL)

Step 3: Test on 10-20 profiles to validate the workflow

Step 4: Scale to your complete list and segment by skills

Step 5: Automate with Zapier/Make to automatically enrich every new lead

Start extracting LinkedIn skills in 2 minutes

Install Derrick in Google Sheets and test for free on your first prospects. No technical skills required.

Install Derrick for free →

Next step: Once your leads are enriched with their skills, discover how to use LinkedIn Sales Navigator for B2B prospecting and maximize your conversion rate.

FAQ

How to extract LinkedIn skills for free?

Derrick offers 200 free credits (= 200 enrichments). For more volume, the Small plan at $9/month includes 4,000 credits. Alternatives: LinkedIn Lite (only 3-5 visible skills) or manual scraping (very slow).

Can you find skills without LinkedIn Premium?

Yes. Derrick works without Sales Navigator. You see all public skills. Premium/Sales Nav gives access to more filters in LinkedIn but isn’t required for extraction.

Are extracted skills up to date?

Derrick scrapes in real-time. Data is as recent as the LinkedIn profile itself. If the person updates their skills, you’ll see changes on the next scraping.

How long does it take to extract 1,000 LinkedIn skills?

About 50 minutes with Derrick (3 seconds per profile). Manually, count 80+ hours. The ROI is obvious from 100 profiles onwards.

Can you automate extraction with a CRM?

Yes. Derrick integrates with Zapier, Make, and n8n. Example: Every new HubSpot contact automatically triggers LinkedIn skills extraction and enriches the CRM.

Is it legal to scrape LinkedIn skills?

Yes, as long as you collect only public data (HiQ vs LinkedIn court decision, 2026). Derrick respects this legal framework. However, you must comply with GDPR in data usage.