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").

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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

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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.

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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%)

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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.

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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-guide offer 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.

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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.

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Anti-Hallucination Guardrails

1. Never invent client metrics, results, or proof points. Only use data provided or mark as [PLACEHOLDER: need real metric]
2. Don't fabricate recommendations or testimonials. If none exist, note the gap and provide the request template
3. No invented company descriptions. Use scraped data or ask for clarification
4. Mark assumptions clearly. Use "Example:" prefix for illustrative scenarios
5. Verify proof points are real. Ask user to confirm before including specific numbers in banner/about

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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

1. Search for LinkedIn profile scraper: search-actors with query "LinkedIn profile scraper"
2. Get actor details: fetch-actor-details for the selected actor
3. Run actor: call-actor with the LinkedIn profile URL as input
4. Get results: get-actor-output for 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

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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.

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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.

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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.

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