Kjell Tore Guttormsen
0c9c02a2b1
Closes the S8 re-review (BLOCK 3/4/1). The S8 fix patched only the 2 strings S7 named; the re-review found 6 more same-class survivors. Per the systemic read, this is a comprehensive sweep, not a per-line patch. Reconciled every retired engagement-coefficient + model-fact survivor against the canonical references/algorithm-signals-reference.md (order, not coefficients; comment ≈ 2x a like; no model name/params): - glossary.md: coefficient table + Save-Signal '10x weight' → canonical ordering (citation now true) - engagement-frameworks.md, analytics-interpreter.md, content-optimizer.md, pipeline.md, engagement-coach.md: the 10x/8x/7-9x/2.5x/0.2x system (incl. 4 survivors the re-review did not cite) → ordering - playbook: '15x more algorithmic boost' + video '5x more conversations' → directional, sourced - profile.md + linkedin-voice/SKILL.md: '150B parameter foundation model' → '2026 relevance-ranking model' - quality-scorecard.md: '360Brew Validation' → topic-relevance framing - setup.md: 'thought leadership plugin' → 'LinkedIn Studio plugin' Lint (MAJOR 4): rebuilt scripts/test-runner.sh STALE_STATS to forbid EVERY retired-class phrasing (not the 2 S7 strings) + widened scope to assets/checklists/. Targets retired phrasings (7-9x, (10x), '10x weight', '5x more conversations'), NOT bare 10x/15x/5x (legit 5x5x5 / cadence / pixel-dims / '10x your reach' hyperbole). Proven non-vacuous: catches all 10 retired strings, ignores all 10 legit uses. Tests (MAJOR 7): added no-anchor fall-through tests for recordFirstHourPlan + recordOutreachContact (date scalar not written/reported, section still appended). MINOR 8: reflowed newsletter.md content-repurposer wiring onto one line. test-runner.sh 66/0/0; node --test 94/94 (was 92, +2). NO push until /trekreview re-clears the gate. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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| name | description |
|---|---|
| linkedin-voice | LinkedIn voice training, profile optimization, content differentiation, and authenticity checking. Covers voice profile building, drift detection, topic-relevance profile alignment, originality scoring, and maintaining authentic presence on LinkedIn. This skill should be used when the user wants to optimize their LinkedIn profile, train their voice, check content originality, detect voice drift, build a voice profile, or ensure their content is differentiated from commodity content. Triggers on: "optimize my LinkedIn profile", "topic-relevance", "profile optimization", "analyze my voice", "build voice profile", "voice audit", "does this sound like me", "voice drift", "is this original", "differentiation check", "originality check", "commodity content", "unique angle", "am I authentic", "my writing style", "train my voice", "headline optimization". |
Voice and Profile Domain
This skill covers voice identity, profile optimization for the topic-relevance ranking, content differentiation, and authenticity maintenance.
Commands
| Command | Purpose | When to Use |
|---|---|---|
/linkedin:profile |
profile/topic-relevance optimization | Profile setup and audit |
Agents
| Agent | Model | Responsibility |
|---|---|---|
voice-trainer |
Sonnet | Voice profile building + drift detection |
differentiation-checker |
Sonnet | Originality scoring + commodity detection |
Profile/Topic Relevance Validation
This is the most significant LinkedIn algorithm change since the platform launched.
The Fundamental Shift
In the older feed model: Post something -> Goes to 10% of your audience -> LinkedIn tracks engagement -> Decides if more people should see it.
In the 2026 relevance model: profile/topic relevance is weighed alongside engagement — content matched to your demonstrated expertise is distributed more widely (including beyond your network), so an off-topic post from a misaligned profile tends to underperform.
The Profile/Topic Relevance Factors
The 2026 relevance-ranking model evaluates five criteria before your post reaches anyone:
| Criteria | What It Checks | Impact if Missing |
|---|---|---|
| About Section | Does it establish expertise on this topic? | High - first signal of credibility |
| Experience Section | Do you have relevant background with impact statements? | High - proves you've done the work |
| Content History | Have you posted about this topic before? | Medium - consistency signal |
| Network | Are you connected to other professionals in this space? | Medium - social proof |
| Engagement Patterns | Do you comment on posts about this topic? | Medium - active participation |
If these don't align with your post topic, your reach gets throttled. Hard.
Strategic Implications
Before you post again, audit your profile:
Ask yourself: "If LinkedIn's AI read this, would it believe I'm an expert on the topics I post about?"
If the answer is no, fix that first.
For detailed algorithm mechanics, see references/algorithm-signals-reference.md.
Profile Optimization Checklist
About Section (CRITICAL)
Your About section is the first signal telling topic-relevance what you're qualified to discuss.
Structure for optimization:
First 2-3 lines (visible without "see more"):
- Front-load your specific expertise claim
- Use domain-specific terminology
- State WHO you help with WHAT problem
Full About section:
[Specific expertise claim with domain terminology]
[WHO you help + specific RESULT you deliver]
[Your story - brief, relevant to your expertise]
[Credentials that validate your expertise]
[Frameworks/approaches you use]
[How to connect/work with you]
Experience Section (HIGH IMPACT)
Transform each role with impact statements, not task lists:
- "Deployed first Copilot Studio agent handling 40% of internal inquiries"
- "Built RAG solution processing 12,000+ feedback entries"
- "Achieved documented 968% ROI on AI initiatives"
Headline Formula
WHO you help + RESULT you deliver
Strong: "Helping public sector leaders implement AI that actually works | AI Advisor @ [your organization]"
Voice Training
Building a Voice Profile
The voice-trainer agent analyzes your writing samples to identify:
- Sentence structure patterns -- Short/long mix, fragments, questions
- Word choice signatures -- Technical depth, jargon level, unique phrases
- Hook style -- How you naturally open posts
- Storytelling approach -- How you construct narratives
- Tone signature -- Formal/informal, humorous/serious, provocative/measured
Voice Drift Detection
Over time, content can drift from your authentic voice -- especially when using AI tools.
Warning signs:
- Posts feel "corporate" or "polished but generic"
- Comments don't match your post voice
- Engagement drops despite consistent posting
- You wouldn't say this out loud
Prevention:
- Quarterly voice audits (use voice-trainer agent)
- Read posts aloud before publishing
- Maintain voice samples in
assets/voice-samples/ - Compare drafts against your voice profile
Voice Samples
Rule: Always read assets/voice-samples/ before generating content. This directory contains reference posts that represent the user's authentic voice.
Content Differentiation
The Originality Framework
The differentiation-checker agent evaluates content across five dimensions:
- Angle uniqueness -- Is this perspective novel?
- Evidence quality -- Are you citing unique sources/experiences?
- Framework originality -- Are you creating or borrowing frameworks?
- Voice distinctiveness -- Would readers know this is you without the byline?
- Value density -- Is every sentence earning its place?
Commodity Content Detection
Red flags for commodity content:
- Could be written by anyone in your field
- Contains only widely-known advice
- Uses the same examples everyone uses
- Lacks personal experience or data
- No contrarian or unique angle
Fix strategies:
- Add personal data/experience
- Take a contrarian position (and defend it)
- Combine two seemingly unrelated domains
- Go deeper than surface-level advice
- Share what you learned from failure, not just success
Common Patterns
User: "Does this sound like me?"
- Load voice profile and samples
- Compare draft against voice signatures
- Identify specific drift points
- Suggest targeted edits to restore voice
User: "Is this original enough to post?"
- Run differentiation check
- Search for similar published content
- Score across five dimensions
- Suggest strategies to increase uniqueness
Reference Files
| File | When to Read |
|---|---|
references/algorithm-signals-reference.md |
Profile optimization, topic-relevance |
references/linkedin-visual-style.md |
Visual identity consistency |
assets/voice-samples/ |
Voice reference (always read before content creation) |
config/user-profile.template.md |
User personalization setup |