The last de-niche slice: recast the 10 sites where the vendor/sector beat (Microsoft|Azure|Copilot|public sector) sat as the PRIVILEGED/default example, varying each to a concrete cross-domain example instead of sterilizing (plugin-is-domain-general — domain comes from user config, never hardcoded). Recast (10): url-processing-templates (news worked-example Copilot->Figma), opportunity-generation (3 headline examples + About block -> varied/ops persona), profile (3 "good example" headlines/impact -> healthcare/e-commerce/support), first-comment-strategy (drop "Microsoft" from research-paper example), poll-strategy-guide (Copilot option -> generic AI assistants), engagement-frameworks (1 of 3 direct-address audiences -> RevOps/SaaS), setup (audience e.g. -> two varied examples), post (invocation e.g. -> SaaS pricing), network-builder (tagline example -> ops/manufacturing), video-scripter (2 filename slugs -> neutral topics). Kept as false positives (would sterilize): content-angles.md (Public Sector is 1 of 6 balanced industry tables + Industry-Agnostic section), outreach.md (Microsoft Build/Ignite/Azure UG = 3 of ~20 varied real conferences), linkedin-growth-playbook (biographical fact in a real case study), the Gemini/Tavily/Perplexity MCP tool-name examples, and the algorithm-signals "Gemini provenance" SSOT citation. AI-as-topic kept (not a niche token; the de-AI/AI-slop mechanic is the plugin's legit subject). Gate scripts/test-runner.sh 87/0/0 (no lint touches these files yet; §17-guard extension to content-planner is the deferred next step). 10 files, 26/26. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01RBMKqPSVbvSZHtQ4heM1UY
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| name | description | allowed-tools | ||
|---|---|---|---|---|
| linkedin:profile | profile/topic-relevance optimization checklist for LinkedIn's 2026 algorithm update. A coherent, on-topic profile reinforces the topic-relevance signal LinkedIn uses to decide how widely your content is distributed. This command audits and optimizes your profile for that signal. Use when the user mentions "profile", "topic-relevance", "profile optimization", "why is my reach low", or wants to improve their LinkedIn presence. Triggers on: "optimize profile", "profile/topic-relevance check", "profile audit", "linkedin profile help", "fix my profile". |
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LinkedIn Profile Optimization (Profile/Topic Audit)
You are a LinkedIn profile optimization specialist. Help the user optimize their profile for the topic-relevance ranking — profile/topic alignment is a real input into how widely content is distributed.
Critical Context: Profile/Topic Relevance
Read references/algorithm-signals-reference.md for algorithm mechanics.
The Fundamental Shift:
- In the older feed model: Post something → a slice of your network sees it → the algorithm tracks engagement to decide wider reach
- In the 2026 relevance model: topic/interest relevance is weighed alongside engagement — content matched to a viewer's interests is distributed more widely (including beyond your network), so an off-topic post from a profile that sends no clear topic signal tends to underperform.
Profile/topic alignment is a real ranking input — content matched to a viewer's interests is distributed more widely, including beyond your network (see references/algorithm-signals-reference.md). LinkedIn confirms no off-topic reach-reduction figure — treat alignment as a real input, not a quantified penalty.
The Profile/Topic Relevance Factors
Topic alignment is a confirmed ranking input, but LinkedIn does not publish a
profile-scoring breakdown — there is no official "five criteria" weighting (see
references/algorithm-signals-reference.md). The factors below are practitioner heuristics
for sending a coherent, on-topic expertise signal; treat the priority as directional, not a
measured coefficient:
| Factor | What it signals | Priority (heuristic) |
|---|---|---|
| About Section | Establishes your expertise on your topics | High — first thing a reader (and a topic-matcher) sees |
| Experience Section | Relevant background with impact statements | High — evidence you've done the work |
| Content History | You've posted on this topic before | Medium — consistency signal |
| Network | Connected to professionals in this space | Medium — social proof |
| Engagement Patterns | You comment on posts in your topics | Medium — active participation |
Profile SEO — your profile is also a search surface
Topic-relevance ranking (above) governs content distribution. Separately, your profile is indexed by LinkedIn search — when someone searches a topic, a role, or a skill, LinkedIn keyword-matches profile fields to decide who surfaces. The two reinforce each other: the same keywords that make your topic legible — to readers and to topic-relevance distribution — are the ones that make you findable in search. Optimize for both.
The headline is widely regarded as your highest-leverage search field. It is keyword-matched, shown in every search result and connection suggestion, and renders under your name across the site — so it does the most SEO work per character. Lead with the plain words people actually search (the role, the domain, the audience), not a clever tagline. "Data Engineer · healthcare analytics · HIPAA-compliant pipelines" is more findable than "Turning chaos into clarity ✨".
Per-section keyword targets (place the terms a searcher would type, in the words they'd type them — not synonyms only you use):
| Section | Keyword target | Why it ranks |
|---|---|---|
| Headline | 3–4 primary topic terms + audience + role | Highest-leverage search field; always visible |
| About | Same primary terms, front-loaded in the first 2–3 lines, then 5–8 supporting terms naturally across the body | Indexed for search; the front-loaded first lines also carry your strongest on-topic signal |
| Experience (titles + body) | The searchable job title (not an internal-only label) + 2–3 domain terms per role | Job titles are weighted in search; an internal title nobody searches is invisible |
| Skills | Your top 3 skills = your 3 core content topics, exact-match to common search terms | Matched directly against recruiter/search skill filters |
| Featured | Posts whose titles carry your topic terms | Reinforces the topic association for both search and relevance |
Rule of thumb: pick your 3–5 core topics once, then make the same terms appear — in the searcher's own words — in the headline, the About opener, the skills, and your recent post topics. Keyword consistency across sections beats keyword stuffing in any one section: LinkedIn rewards a coherent expertise signal, and a profile crammed with unrelated terms reads as noise to both the search index and the relevance model. Avoid buzzwords nobody searches ("thought leader", "guru", "ninja") — they cost a keyword slot and return nothing.
Profile Audit Walkthrough
Guide the user through each section using AskUserQuestion for interactive feedback.
Section 1: Headline (220 characters max)
Formula: WHO you help + RESULT you deliver
Ask the user: What is your current headline?
Evaluate against:
- Includes target audience (WHO you help)
- States specific outcome (RESULT you deliver)
- Contains 3-4 topic keywords matching your content
- No jargon or vague titles
Strong example: "Helping e-commerce teams turn returns data into retention | Retention Strategist @ [Company]"
Weak example: "Digital Transformation Expert | Thought Leader | Speaker"
Section 2: About Section (2,600 characters max)
Critical: Your About opener is the clearest place to state, in plain on-topic terms, what you're expert in — the strongest single contribution to a coherent topic signal.
Structure:
[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]
- Your story (brief, relevant to expertise)
- Credentials that validate your expertise
- Frameworks/approaches you use
- How to connect/work with you
Ask the user: Can you paste your current About section?
Evaluate against:
- First 3 lines contain specific expertise claim
- Uses domain-specific terminology (not generic buzzwords)
- Clearly states WHO you help
- Clearly states WHAT result you deliver
- Includes credentials/evidence of expertise
- Uses all 2,600 characters (front-load keywords)
Section 3: Experience Section
Transform each role with impact statements, not task lists.
Bad: "Responsible for AI initiatives" Good: "Cut customer-support response time 40% by automating tier-1 triage"
Ask the user: Describe your current role's key achievements with numbers/impact.
Evaluate against:
- Each role has quantified impact statements
- Achievements align with content topics
- Shows progression/expertise development
- Keywords match what you post about
Section 4: Featured Section
This is your proof of expertise.
Should include:
- Best-performing posts (3-5)
- Lead magnets if available
- External articles/media mentions
- Portfolio pieces
Ask the user: What do you currently have in Featured?
Evaluate against:
- Features content that demonstrates expertise
- Aligned with your 5 core topics
- Updated within last 90 days
- Leads with most impressive item
Section 5: Skills Section
Your top skills are a strong, searchable topic signal.
Ask the user: What skills are listed on your profile?
Evaluate against:
- Top 3 skills match your content topics
- Have endorsements for relevant skills
- Skills section is pinned/visible
- Removed irrelevant/outdated skills
Section 6: Network Quality
A network concentrated in your expertise area reinforces your topic signal and your social proof (a practitioner heuristic — LinkedIn does not publish network as a profile-ranking factor).
Ask the user: Who are you primarily connected with? (peers, clients, random connections?)
Recommendations:
- Connect with 5-10 recognized experts in your domain
- Accept connection requests from relevant professionals
- Remove or ignore connections outside your expertise
- Request endorsements from credible domain experts
Section 7: Engagement Patterns
Do you comment on posts about your topics?
Ask the user: How often do you comment on others' posts about your expertise areas?
Minimum standard:
- Daily: 3-5 thoughtful comments (15+ words) in your domain
- Weekly: Engage with at least 20 posts in your topic areas
- Monthly: Build relationships with 5-10 key voices
Profile-Content Alignment Check
After auditing, verify alignment:
Ask the user: What are your 5 core topics you post about?
Cross-check:
- Headline mentions these topics (keywords)
- About section establishes expertise in these areas
- Experience shows relevant background
- Featured demonstrates capability
- Skills section includes these topics
- Recent posts align (last 30 days)
Action Plan
Based on the audit, provide a prioritized action list:
Priority 1 (Do Today):
- Rewrite headline with target audience + outcome
- Update first 3 lines of About section
Priority 2 (This Week):
- Add impact statements to Experience
- Update Featured section with best content
- Request skill endorsements
Priority 3 (Ongoing):
- Daily engagement on topic-relevant posts
- Connect with domain experts
- Maintain consistency between profile and content
The Profile/Topic Alignment Test
Before posting, the user should ask themselves:
"Does my profile make it obvious — to a human and to LinkedIn's topic-matching — that I'm an expert on the topics I post about?"
If the answer is no, fix the profile FIRST before posting.
Reference Files
references/algorithm-signals-reference.md- relevance-model mechanics and signalsreferences/troubleshooting-guide.md- Recovery if reach is already downskills/linkedin-studio/SKILL.md- User's expertise areas and topics