Free lesson · The Job Search System
Skills, Endorsements, and Recommendations
Skills, Endorsements, and Recommendations
Student: Swati, my LinkedIn skills section has about 40 skills listed, mostly endorsed by random people. Does this even matter?
Swati: It matters more than you'd think, but not in the way most people assume. The skills section is not a badge collection - it's a keyword index. LinkedIn Recruiter has a filter for skills. When a recruiter searches for "Kubernetes" and filters by that skill, your profile only appears if "Kubernetes" is in your skills list. Having it in your experience section text helps with search relevance, but the skills filter is a binary check - either the skill is listed or it isn't.
That said, having 40 skills is too many. Like your resume skills section, your LinkedIn skills should be curated and focused.
The Skills Section as Keyword Index
LinkedIn allows you to list up to 50 skills and pin your top 3. The top 3 appear prominently on your profile. The rest are visible but less prominent.
How to curate your skills list:
Step 1: Pin your top 3. These should be the 3 skills most central to your target role. If you're targeting backend engineering roles: "Distributed Systems," "Java," "System Design." These appear at the top of your skills section and are the first thing recruiters see.
Step 2: Keep 15 to 20 total. Remove anything you listed years ago and can no longer discuss confidently. Remove generic skills like "Problem Solving" or "Team Leadership" - these aren't how recruiters search.
Step 3: Match your target JDs. Look at 5 job descriptions for roles you'd want. What skills do they list? Make sure those skills appear on your LinkedIn profile. This is the same keyword matching principle from resume ATS optimization, applied to LinkedIn search.
The skill ordering matters. LinkedIn displays your pinned top 3 skills prominently, then lists the rest in order of endorsement count. This means your most-endorsed skills appear higher on your profile regardless of how relevant they are. If "Microsoft Office" has 50 endorsements because old colleagues endorsed it years ago, but "Kubernetes" has 3 endorsements because it's a recent skill - "Microsoft Office" appears above "Kubernetes" in your skills list.
To fix this: remove irrelevant highly-endorsed skills (bye, Microsoft Office), and proactively seek endorsements for your target-role skills. The combination of removing noise and adding signal reshapes your skills section to match what recruiters are searching for.
Student: Should I add skills I'm learning but haven't used professionally?
Swati: Only if you can discuss them substantively in a conversation. If you've been learning Rust through personal projects and can discuss ownership models, lifetimes, and error handling patterns - add it. If you've completed one tutorial - don't. The test is: "If a recruiter asks me about this skill on a call, can I have a 5-minute informed conversation?" If yes, list it. If no, wait until you can.
graph TD
A["LinkedIn Skills Strategy"] --> B["Top 3 Pinned Skills<br/>Your core identity<br/>'Distributed Systems, Java, AWS'"]
A --> C["Skills 4-10<br/>Supporting technologies<br/>'Go, Kubernetes, PostgreSQL,<br/>Docker, Kafka, Redis, gRPC'"]
A --> D["Skills 11-20<br/>Broader capabilities<br/>'System Design, API Design,<br/>Microservices, CI/CD, Terraform'"]
A --> E["Skills 20+<br/>Remove these<br/>They dilute your signal"]
style B fill:#51cf66,color:#fff
style C fill:#339af0,color:#fff
style D fill:#fcc419,color:#000
style E fill:#ff6b6b,color:#fff
This diagram shows the LinkedIn skills strategy in tiers. The top 3 pinned skills define your core identity, like Distributed Systems, Java, and AWS. Skills 4 through 10 are supporting technologies like Go, Kubernetes, PostgreSQL, Docker, Kafka, Redis, and gRPC. Skills 11 through 20 cover broader capabilities like System Design, API Design, Microservices, CI/CD, and Terraform. Skills beyond 20 should be removed because they dilute your signal.
Endorsements: Social Proof Signals
Student: What about endorsements? People I barely know have endorsed me for skills I don't actually use.
Swati: Endorsements have a mixed reputation. Recruiters don't put heavy weight on the endorsement count itself - they know people endorse each other casually. But endorsements do two things:
First, they confirm skill claims. A skill with 20 endorsements reads as more credible than one with zero, even if the endorsements are from people who haven't directly observed your work in that skill.
Second, they affect search ranking. LinkedIn's search algorithm gives a slight boost to profiles where listed skills have endorsements. More endorsements on a skill = slightly higher search relevance for that skill.
Tip: The endorsement exchange strategy: Identify 10 colleagues or classmates whose skills you can genuinely endorse. Endorse their top 5 skills. LinkedIn sends them a notification, and about 60 to 70 percent of people reciprocate by endorsing your skills in return. This is not gaming the system - you're endorsing skills you've actually observed, and they're doing the same for you. It's a mutually beneficial professional courtesy.
Focus your endorsement requests on your top 3 pinned skills. You want the most endorsements on the skills most central to your target role.
What to do about irrelevant endorsements: If someone has endorsed you for a skill you don't want on your profile, simply remove the skill. When you remove a skill, all endorsements for it disappear too. Only keep skills you're actively claiming as part of your professional identity.
LinkedIn Skill Assessments: LinkedIn offers skill assessment quizzes - short tests for skills like Python, Java, AWS, and React. Passing an assessment adds a "Verified" badge to that skill on your profile. These badges provide a small but real credibility boost, especially for skills where you don't have extensive endorsements.
My recommendation: take the assessments for your top 5 to 7 skills. They're free, they take about 15 minutes each, and the "Verified" badge differentiates your skill claims from the thousands of unverified ones. If you fail an assessment, LinkedIn doesn't penalize you - nobody sees failed attempts. You can retake them after a waiting period.
Recommendations: The Highest-Value Social Proof
Recommendations are different from endorsements. An endorsement is a single click - "yes, this person knows Java." A recommendation is a written paragraph - a colleague, manager, or direct report taking time to describe your contributions and capabilities in their own words. The effort required makes recommendations significantly more credible.
Student: How many recommendations do I need?
Swati: Quality over quantity. 2 to 3 strong recommendations from the right people are worth more than 10 generic ones. Here's the hierarchy of recommendation value:
| Source | Value | Why |
|---|---|---|
| Direct manager | Very high | They supervised your work and chose to endorse you publicly |
| Skip-level manager | Very high | Senior leader who thought enough of your work to write about it |
| Tech lead or senior peer | High | Technical credibility from someone who reviewed your code/designs |
| Cross-team collaborator | Medium | Shows you work well across organizational boundaries |
| Direct report | Medium-high | Shows leadership qualities (for senior/manager roles) |
| College friend or classmate | Low | No professional observation of your work |
graph TD
A["Who Should Write<br/>Your Recommendations?"] --> B["1st Priority: Your manager<br/>or skip-level<br/>Strongest credibility signal"]
B --> C["2nd Priority: Tech lead<br/>or senior peer<br/>Technical credibility"]
C --> D["3rd Priority: Cross-team<br/>collaborator<br/>Shows breadth"]
D --> E["Avoid: Friends, classmates<br/>who never worked with you<br/>No professional credibility"]
style B fill:#51cf66,color:#fff
style C fill:#339af0,color:#fff
style E fill:#ff6b6b,color:#fff
This diagram shows the priority order for who should write your LinkedIn recommendations. First priority is your manager or skip-level manager, providing the strongest credibility signal. Second priority is a tech lead or senior peer, providing technical credibility. Third priority is a cross-team collaborator, showing you work well across boundaries. Avoid getting recommendations from friends or classmates who never worked with you, as they provide no professional credibility.
How to Ask for Recommendations
Student: Asking for recommendations feels awkward. How do I do it without being weird?
Swati: Most people feel awkward because they send a generic LinkedIn request: "Hi, can you write me a recommendation?" That's hard to respond to because the person doesn't know what to write.
Instead, make it easy for them by providing context:
The recommendation request template:
"Hey [Name], I'm currently updating my LinkedIn profile as I explore new opportunities. Would you be willing to write a short recommendation? I know these take time, so I wanted to make it easy - a few things you could mention if helpful:
- The project we worked on together (the [specific project name])
- What my role was on that project (I [specific contribution])
- One thing you think I did particularly well
No pressure at all if you're too busy - I completely understand. And I'd be happy to write one for you in return."
This works because you've reduced the cognitive load - they know exactly what to write about, they have specifics to reference, and you've offered reciprocity.
When to ask for recommendations:
Best time: Right after completing a successful project together. The work is fresh, the positive feelings are strong, and the person can write specific details without trying to remember. "Hey, we just shipped the payment migration - would you be willing to write a recommendation about our work together while it's fresh?"
Second best: When you're about to change roles. If you're leaving a team, ask before you leave. Once you're gone, people get busy with their own work and the request becomes lower priority. "Since I'm moving to the new team next month, I'd love to get a recommendation from you while we've been working closely together."
Worst time: Two years after you last worked together. The person barely remembers the specifics, and the recommendation will reflect that vagueness.
What good recommendations contain:
The best recommendations are specific, concise, and credible. They contain three elements: a statement of the relationship (how the recommender knows you), a specific observation about your work (a project, a behavior, a skill demonstrated), and a genuine endorsement (would they work with you again, would they hire you, what's unique about you).
A strong recommendation might say something like: "I was [Name]'s engineering manager for 18 months. During that time, they led the redesign of our payment processing pipeline, reducing transaction failures by 60%. What stood out wasn't just the technical execution - it was how they brought the team along. They documented every design decision, ran inclusive design reviews, and made sure the on-call team understood the system before it went live. I'd hire them again without hesitation."
A weak recommendation says: "Great engineer, wonderful person to work with, always delivers high-quality work. Highly recommended!" This tells the reader nothing specific and could have been written by someone who never actually worked with the person.
What to do after receiving a recommendation:
First, thank the person - a simple message saying "I really appreciate the recommendation, it means a lot" goes a long way. Second, return the favor if you can genuinely write something positive about their work. Reciprocal recommendations are normal and expected on LinkedIn.
If you receive a recommendation that's too generic ("great person, highly recommended"), don't publish it. LinkedIn gives you the option to accept or hide each recommendation. It's better to have 2 specific recommendations than 2 specific ones and 3 generic ones - the generic ones actually dilute the impact of the good ones because they look like favor-exchanges rather than genuine endorsements.
How many recommendations to aim for per role: 1 to 2 from your most recent role, and 1 from your previous role, is a strong baseline. A recruiter who sees 3 thoughtful recommendations from credible people gets significant social proof. Beyond 5 to 6 total, additional recommendations have diminishing returns - the recruiter won't read them all.
Important: Recommendations from managers at FAANG or well-known companies carry outsized weight. A recommendation from a Google engineering manager or an Amazon principal engineer creates a strong credibility signal because the reader trusts that person's judgment. If you have former managers or colleagues at recognized companies, prioritize asking them for recommendations.
Warning: Never write your own recommendation and ask someone to post it. This is surprisingly common and it always reads as inauthentic. Recommendations written by the candidate have a distinctive "too perfect" quality - they hit every talking point, use the candidate's own vocabulary, and sound suspiciously aligned with the candidate's positioning. Hiring managers and experienced recruiters can spot these. Provide guidance and context, but let the recommender write in their own words.
The Complete Social Proof Layer
When a recruiter checks your LinkedIn profile, the social proof elements work together:
- Skills with endorsements confirm that others validate your claimed abilities
- Recommendations provide narrative evidence of your impact from people who watched you work
- Mutual connections show that you're part of a professional community
These elements can't be faked at scale. Unlike your headline and About section (which you write yourself), endorsements and recommendations come from other people. That third-party validation is why recruiters check these sections - they're looking for signals that your self-description is backed up by external evidence.
Interview: Recommendations Pre-Answer "Tell Me About Your Strengths"
When an interviewer asks about your strengths, having LinkedIn recommendations that describe those strengths creates a powerful reinforcement loop. "One of my strengths is making complex systems understandable for the team - in fact, my former tech lead mentioned this specifically in a LinkedIn recommendation." This isn't name-dropping - it's citing evidence. It tells the interviewer that your self-assessment is validated by others.
Exercise: Starter: Review your LinkedIn skills list. Remove anything you can't discuss confidently in an interview. Pin your top 3 skills that align with your target role. If you have more than 25 skills listed, trim to 15-20.
Intermediate: Identify 3 people who could write meaningful recommendations - ideally a former manager, a senior peer, and a cross-functional collaborator. Send each one a personalized request using the template in this lesson. Give them 2 weeks to respond. Don't follow up more than once.
Advanced: Audit your full social proof layer. Do you have endorsements on your top 3 skills from people who've actually observed your work? Do you have at least 2 recommendations from credible sources? Do you have mutual connections at your target companies? Create an action plan to close any gaps - endorsement exchanges for skills, recommendation requests for credibility, and connection requests (we'll cover networking in Module 06) for mutual connections.
Student: The social proof layer is something I've completely neglected. I have zero recommendations and my endorsements are random.
Swati: You're not alone - most engineers ignore this. But it takes about 30 minutes of effort (curating skills + sending 3 recommendation requests) to go from zero social proof to meaningful social proof. And the impact is disproportionate - a single strong recommendation from a respected manager can tip a recruiter's decision from "maybe" to "reach out."
In the final topic of this module, Olena and I are covering LinkedIn content strategy - how to build visibility on the platform through posting, without becoming a "LinkedIn influencer."
Let's go.