matteotitta LinkedIn Profile Optimization
Audit a client's LinkedIn profile and generate optimized copy for every section — headline, about, banner, experience, featured, recommendations. Combines Apify scraping for structured profile data with user-provided screenshots for visual assessment, then applies proven profile optimization frameworks.
Why this matters: LinkedIn's algorithm reads your entire profile as text context to decide content distribution. A misaligned profile suppresses reach regardless of content quality. This skill ensures profile-content alignment before any content program begins.
Source: Nick Broekema (Content Design) — profile optimization methodology.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with output-tenets.md, output-simplicity.md, ai-speak-anti-patterns.md. Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]].
Refinements applied: R1 (profile copy is end-customer-facing — no source tags), R3 (headline + about capability-led, never "thrilled to be"), R6 (featured / CTAs → DM or sign-up primary), R9 (verb-led section headings — "What I do / How I help / Who I work with").
Claude Code Triggers
Invoke this skill when user says:
- "Optimize [client name]'s LinkedIn profile"
- "LinkedIn profile audit for [person]"
- "Rewrite [person]'s LinkedIn headline/about/bio"
- "Profile optimization for [client]"
- "Help [name] improve their LinkedIn profile"
- "[Client] needs a better LinkedIn presence"
Do NOT invoke when:
- User wants to write a LinkedIn post → Use
linkedin-expert-posts/linkedin-personal-posts/linkedin-sales-posts - User wants LinkedIn comments → Use
linkedin-comment - User wants a LinkedIn infographic/carousel → Use
linkedin-infographics/linkedin-carousels
Inputs
Required
| Input | Description | Source |
|---|---|---|
| LinkedIn profile | URL or full name + company | User provides |
| ICP description | Who is this profile trying to attract? | User provides or from icp-behavioural |
Optional (improve quality)
| Input | How It Helps |
|---|---|
| TOV guidelines | Voice patterns to match in copy generation |
| Company context | Positioning, value props, proof points to reference |
| ICP profile | Detailed pain points and buyer language |
| Screenshots | Visual assessment of banner and profile picture |
| Current positioning | Messaging anchors and differentiators |
| Proof points | Specific metrics, results, client names for banner/about |
If inputs are missing: Ask for LinkedIn URL and ICP description at minimum. Request screenshots for banner/profile pic assessment.
Audit scoring table (8 sections)
This table is the load-bearing decision surface for the audit phase. Every audit fills it in.
| Section | Max Score | Key Criteria |
|---|---|---|
| Profile picture | /10 | Color (not b/w), smile, eye contact, zoom, branded background, contrast |
| Banner | /15 | Branded whitespace, clear category, proof points, ICP resonance |
| Headline | /15 | Formula fit, ICP clarity, desire/outcome, buyer language |
| About | /20 | PAIS structure, hook strength, CTA, specificity, proof |
| Featured | /10 | Links (not posts), CTA quality, friction level (3 max) |
| Experience | /15 | Current role depth, story, ideal client described, results |
| Recommendations | /10 | Problem-solution-outcome structure, relevance, recency |
| Bio-link | /5 | Presence, CTA coverage, number of links |
| TOTAL | /100 |
Status flags: ✓ Good (>70%) | ⚠ Needs work (40-70%) | ✗ Critical (<40%)
Process
3-phase flow: Profile Data Gathering → Profile Audit (using the 8-section scoring table above) → Optimized Copy Generation. Full step-by-step in the premium reference.
Endorsement Strategy
Skill endorsements signal ICP relevance to LinkedIn's search algorithm and 360brew's semantic map. Include this as a quick-win recommendation in all profile audits.
Approach:
- Endorse 10–15 relevant contacts proactively. Most reciprocate within 1-2 weeks.
- Focus on contacts who are ICP-adjacent (peers, past colleagues, complementary service providers).
- Prioritise skills that match ICP search terms: GTM, B2B SaaS, positioning, go-to-market, product marketing, content strategy, pipeline generation.
- Remove irrelevant skills (e.g. "Microsoft Excel", "Photoshop") that dilute semantic topic signal.
- Keep the top 3 pinned skills directly aligned to the ONE offer (see
linkedin-content-guideoffer statement).
Why it matters: LinkedIn's member embedding system weighs skill endorsements as signals of expertise in specific topic clusters. Endorsements from relevant contacts reinforce your semantic profile faster than self-selected skills alone.
Profile Clarity Tenets (Coach Feedback, March 2026)
Voice-locked rules — these stay in body. Source: Nick Broekema / Content Design, March 2026.
- Headline = one sentence — Who you help + what they get + how fast. Not a laundry list of capabilities.
- Banner = single static message — Not a carousel of rotating promises. One clear line.
- About section: mobile readability — Shorter lines, breathing room, dynamics. No big blocks of text on mobile.
- About section: less is more — Keep ICP language but cut without losing meaning. Wordy = weaker.
- Profile-recommendation alignment — Does the profile reflect what clients consistently say? If all recs say "fast, high quality," the profile should lead with speed + quality.
- ICP specificity — Does the profile name the actual ICP role + company stage? Don't be everything to everyone.
Anti-Hallucination Guardrails
- Never invent client metrics, results, or proof points. Only use data provided or mark as
[PLACEHOLDER: need real metric] - Don't fabricate recommendations or testimonials. If none exist, note the gap and provide the request template
- No invented company descriptions. Use scraped data or ask for clarification
- Mark assumptions clearly. Use "Example:" prefix for illustrative scenarios
- Verify proof points are real. Ask user to confirm before including specific numbers in banner/about
MCP Data Integration
Level: 0 — Context (heavy data gathering)
Pulls fresh
| Source | What to pull | Tool | When |
|---|---|---|---|
| Apify | LinkedIn profile data (headline, about, experience, skills, recommendations) | search-actors → fetch-actor-details → call-actor |
Always |
| Firecrawl | Company website (for messaging alignment) | firecrawl_scrape |
If company-context not available |
Apify workflow
- Search for LinkedIn profile scraper:
search-actorswith query "LinkedIn profile scraper" - Get actor details:
fetch-actor-detailsfor the selected actor - Run actor:
call-actorwith the LinkedIn profile URL as input - Get results:
get-actor-outputfor structured profile data
Fallback (no Apify/scraping)
If scraping fails or is unavailable:
- Ask user to copy-paste each profile section manually
- Request screenshots for visual elements
- Proceed with manual data — all frameworks still apply
Quality
Pre-delivery checklist covers audit quality (verbatim evidence, score sums correct), copy quality (formula adherence, PAIS completeness, proof points real), voice quality (matches tov-guidelines), and the Profile Clarity Tenets restated for review. Worked example + anti-examples in the premium reference.
Final ship gate
Run /premortem --output before ship. See /premortem skill for the 5 execution domains (will-it-resonate / will-it-convert / will-it-stay-on-brand / will-stakeholder-push-back / will-it-degrade-over-time) and output template.
Trivial-case escape: ## Premortem\nNo failure modes — trivial change satisfies the contract for genuinely trivial outputs.
Persuasion & stickiness pass
Output complies with persuasion-and-stickiness.md — Cialdini's 7 persuasion levers + Heath's SUCCESs. Deploy the 1-2 Cialdini levers that fit the reader's barrier (never all seven; every lever must be TRUE), run the SUCCESs diagnostic (Simple / Unexpected / Concrete / Credible / Emotional / Stories) over the near-final draft, then the rule's pre-ship gate.