The same rubric we ran on 60 profiles (41 of them were listing the wrong skills) — now run against yours.
Pick the role you want to surface for in recruiter searches. The rubric scores against this target.
LinkedIn lets you pin 3 skills to the top of your Skills section. Recruiter's title filter scores these heaviest.
Copy your full Skills list. The audit checks for adjacency coverage AND legacy red-flag skills.
(Kubernetes OR EKS OR GKE) AND Terraform. If your Skills section misses the right primary term, you're filtered out before any ranking happens.Get the complete Social Media AI Mastery pack — prompts for headlines, About sections, Skills/endorsements strategy, experience bullets, and OTW notes.
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For each target role we maintain a static, client-side graph of (a) core skills — the 3-6 terms recruiters use as the primary filter; (b) adjacency skills — 10-15 related terms that signal range without diluting; (c) legacy red-flag skills — terms that signal you're 5+ years out of date or focused on a different stack. Lookups are case-insensitive with common aliases (e.g. "Node" ↔ "Node.js", "k8s" ↔ "Kubernetes"). Graph data was sampled from 1,200+ LinkedIn job postings in Q1 2026 (US/UK/EU/Canada). No API calls — your input never leaves the browser.
The 4 checks isolate the highest-impact recruiter-side filter signals: pinned top-3 alignment, adjacency breadth, legacy noise, and endorsement concentration. Other dimensions (Skill Assessments badges, "skills used at" experience entries, certifications) layer onto these but don't change the binary "does my profile surface in a Recruiter search" outcome. We rank by leverage, not completeness.
Delete them. The 60-profile audit found 28 of 41 failing profiles had legacy skills sitting in the Skills section despite zero recent endorsements on them. Recruiters scanning the Skills section saw a profile mixing 2026 stack with 2018 stack — a noisier signal than a smaller, focused list. LinkedIn lets you reorder and delete from the Skills edit panel; the 8-minute fix flow below treats deletion as default.
They help only if your endorsements there are concentrated and recent (last 12 months). A pivot profile with 50 endorsements on old-stack skills and 3 on new-stack is read as a profile pretending to pivot. Either move all endorsement traffic to the new stack (ask 3-5 recent colleagues), or delete the legacy skills entirely. The "mixed legacy + new" pattern was the third-highest failure cluster in the audit.
Two routes. Slower: post 3-4 LinkedIn updates with Skills tagged on each — colleagues who react often auto-suggest endorsements on the tagged skill. Faster: send 8-10 individual DMs asking for endorsements on 2-3 specific target-role skills (don't ask for general endorsement — name the skill). The 60-profile passing-cohort averaged 12-18 endorsements concentrated on top-3, not 50+ scattered.