matteotitta 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.
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
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
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
Algorithm Priority Signals (Ranked)
Voice-locked ranking — this is the load-bearing decision data. Stays in body.
- Saves — highest weight; a post with 200 saves dramatically outperforms 1,000 likes
- Comment threads — multi-party comments get 5.2× amplification
- Dwell time — time spent reading correlates strongly with redistribution
- Profile-content alignment — misalignment suppresses distribution for all posts
- Shares/reposts — weighted 4× in TWE scoring
- Likes — lowest weight of all engagement types
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.
Anti-Hallucination Guardrails
- Don't invent performance data. All benchmarks must trace to Shield Analytics, AuthoredUp, or 360brew — sources cited in the premium reference.
- Don't predict exact impression counts. Use the benchmark ranges as context, not guarantees.
- Don't flag content as "will fail". Score as WARN/FAIL/PASS with specific fixes — never predict zero performance.
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.