LinkedIn Algo Audit

Check LinkedIn posts and profile sections against 2026 algorithm data. Standalone quality gate — runs independently of voice, pillar, or client context. Returns a structured audit with pass/warn/fail scores and specific fixes.

Data sources: Shield Analytics (50K posts, Dec 2025), AuthoredUp (3M+ posts, Jan 2026), 360brew GPU-RAR framework, Propelgrowth blog, Scripe 2026 updates.

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Claude Code Triggers

Invoke this skill when user says:

• "check this against the algo"
• "will this post perform?"
• "algo audit"
• "is my profile 360brew optimized?"
• "LinkedIn algorithm check"
• "optimize for the algorithm"
• "why is my content not getting reach?"

Do NOT invoke when:

• User wants voice review → use voice-reviewer
• User wants to write a post → use the appropriate post skill
• User wants overall content strategy → use linkedin-content-guide

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Inputs

Input	Description	Source
Post text or profile section	The content to audit	User provides or from last assistant message
Audit type	Post audit, Profile audit, or Full audit	User specifies or infer from content

Validation:

• Content is provided (post text or profile section)
• Audit type is determinable

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Algorithm Foundation: GPU-RAR (2026)

Voice-locked framework — this is the spine of the audit logic. Stays in body.

LinkedIn replaced thousands of individual ranking models with a single AI model that reads content semantically — like a language model, not a keyword matcher.

GPU-RAR Framework (360brew):

• G — Generate embeddings from your profile text and post content
• P — Profile match between content topic and your stated expertise
• U — User interest matching (member embedding against topic clusters)
• R — Relevance scoring against the specific audience segment
• A — Amplification based on early engagement signals
• R — Redistribution to new segments if content holds up

Key implications:

• Your profile is the AI's prompt about you — misaligned profile = suppressed distribution
• Semantic matching, not keyword stuffing — hashtags are now largely irrelevant
• 90-day categorization window — posting consistently on 2-3 topics builds an audience cluster
• Evergreen redistribution — strong content resurfaces weeks later to new matching segments

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Algorithm Priority Signals (Ranked)

Voice-locked ranking — this is the load-bearing decision data. Stays in body.

1. Saves — highest weight; a post with 200 saves dramatically outperforms 1,000 likes
2. Comment threads — multi-party comments get 5.2× amplification
3. Dwell time — time spent reading correlates strongly with redistribution
4. Profile-content alignment — misalignment suppresses distribution for all posts
5. Shares/reposts — weighted 4× in TWE scoring
6. Likes — lowest weight of all engagement types

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Process

Post audit (3 phases): Content-audience fit → Post structure signals (hook dwell, save potential, comment thread, format performance, reach killers) → Scoring summary.

Profile audit (3 phases): Keyword-profile alignment → Content history alignment (if known) → Scoring summary.

Full step-by-step + scoring criteria + reach killers list in the premium reference.

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Anti-Hallucination Guardrails

1. Don't invent performance data. All benchmarks must trace to Shield Analytics, AuthoredUp, or 360brew — sources cited in the premium reference.
2. Don't predict exact impression counts. Use the benchmark ranges as context, not guarantees.
3. Don't flag content as "will fail". Score as WARN/FAIL/PASS with specific fixes — never predict zero performance.

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Quality

Pre-delivery checks cover audit completeness (all sub-checks ran, fixes specific not generic), audit fairness (PASS = no blockers, not "great"), and benchmark currency. Algorithm benchmark tables (Shield, AuthoredUp, format performance, posting optimization) + anti-examples in the premium reference.

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