ads-linkedin▌
agricidaniel/claude-ads · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Thought Leader Ads use employee/executive personal posts as sponsored content:
LinkedIn Ads Deep Analysis
Process
- Collect LinkedIn Ads data (Campaign Manager export, Insight Tag status)
- Read
ads/references/linkedin-audit.mdfor full 25-check audit - Read
ads/references/benchmarks.mdfor LinkedIn-specific benchmarks - Read
ads/references/scoring-system.mdfor weighted scoring - Evaluate all applicable checks as PASS, WARNING, or FAIL
- Calculate LinkedIn Ads Health Score (0-100)
- Generate findings report with action plan
What to Analyze
Technical Setup (25% weight)
- Insight Tag installed and firing on all pages (L01)
- Conversions API (CAPI) active, launched 2025 (L02)
- Conversion events configured for full funnel
- Revenue attribution tracking enabled
Audience Targeting (25% weight)
- Job title targeting uses specific titles, not just functions (L03)
- Company size filtering matches ICP (L04)
- Seniority level appropriate for offer (L05)
- Matched Audiences active: retargeting + contact lists (L06)
- ABM company lists uploaded (up to 300,000 companies) (L07)
- Audience expansion OFF for precision campaigns, ON for scale (L08)
- Predictive audiences tested, replaced Lookalikes Feb 2024 (L09)
Creative Quality (20% weight)
- Thought Leader Ads active, ≥30% budget allocation for B2B (L10)
- Ad format diversity: ≥2 formats tested (L11)
- Video ads tested (L12)
- Creative refresh every 4-6 weeks (L13)
Lead Gen & Performance (15% weight)
- Lead Gen Form ≤5 fields (13% CVR benchmark) (L14)
- Lead Gen Form synced to CRM in real-time (L15)
- Campaign objective matches funnel stage (L18)
- A/B testing active: creative or audience (L19)
- Message ad frequency ≤1 per 30-45 days (L20)
Bidding & Budget (15% weight)
- Bid strategy: CPS for Messages, Max Delivery for Content (L16)
- Daily budget ≥$50 for Sponsored Content (L17)
- CTR ≥0.44% for Sponsored Content (L21)
- CPC within benchmark: $5-7 average, senior $6.40+ (L22)
- Lead-to-opportunity rate tracked, not just CPL (L23)
- Attribution: 30-day click / 7-day view configured (L24)
- Demographics report reviewed monthly (L25)
Thought Leader Ads (TLA) Assessment
Thought Leader Ads use employee/executive personal posts as sponsored content:
- CPC typically $2.29-$4.14 vs $13.23 for standard Sponsored Content
- CTR typically 2-3x higher than corporate-branded ads
- Best for: B2B thought leadership, brand awareness, engagement
Evaluate:
- Are TLAs being used? (If not, HIGH priority recommendation)
- Are they getting ≥30% of total LinkedIn budget?
- Are the right employees selected (industry credibility, active posters)?
- Is post content authentic and valuable (not salesy)?
ABM Strategy Assessment
For B2B Enterprise accounts:
- Company list uploaded and segmented by tier (Tier 1, 2, 3)
- Custom content per tier (personalized messaging)
- Account penetration tracking (contacts reached per target account)
- Integration with CRM/ABM platform (Demandbase, 6sense, etc.)
LinkedIn Context
| Setting | Value |
|---|---|
| Minimum audience size | 500 (for ads to run) |
| Lead Gen Form CVR benchmark | 13% |
| TLA CPC range | $2.29-$4.14 |
| Standard SC CPC | $13.23 average |
| Hierarchy rename | Oct 2025 (Campaign Group → Campaign → Ad) |
| Predictive Audiences | Replaced Lookalikes Feb 2024 |
Key Thresholds
| Metric | Pass | Warning | Fail |
|---|---|---|---|
| CTR (Sponsored Content) | ≥0.44% | 0.30-0.44% | <0.30% |
| CPC (average) | ≤$7.00 | $7-10 | >$10.00 |
| Lead Gen CVR | ≥10% | 5-10% | <5% |
| Message frequency | ≤1/30 days | 1/15-30 days | >1/15 days |
| TLA budget share | ≥30% | 15-30% | <15% |
Output
LinkedIn Ads Health Score
LinkedIn Ads Health Score: XX/100 (Grade: X)
Technical Setup: XX/100 ████████░░ (25%)
Audience: XX/100 ██████████ (25%)
Creative: XX/100 ███████░░░ (20%)
Lead Gen: XX/100 █████░░░░░ (15%)
Budget & Bidding: XX/100 ████████░░ (15%)
Deliverables
LINKEDIN-ADS-REPORT.md: Full 25-check findings with pass/warning/fail- TLA adoption roadmap (if not using)
- ABM strategy recommendations (for B2B)
- Lead Gen Form optimization priorities
- Quick Wins sorted by impact
How to use ads-linkedin on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ads-linkedin
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ads-linkedin from GitHub repository agricidaniel/claude-ads and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ads-linkedin. Access the skill through slash commands (e.g., /ads-linkedin) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★44 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Solid pick for teams standardizing on skills: ads-linkedin is focused, and the summary matches what you get after install.
- ★★★★★Amelia Smith· Dec 28, 2024
ads-linkedin is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Carlos Robinson· Dec 24, 2024
Keeps context tight: ads-linkedin is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dhruvi Jain· Dec 20, 2024
ads-linkedin reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Olivia Zhang· Dec 12, 2024
We added ads-linkedin from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Benjamin Johnson· Nov 19, 2024
ads-linkedin fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Xiao Choi· Nov 15, 2024
We added ads-linkedin from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Oshnikdeep· Nov 11, 2024
I recommend ads-linkedin for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Benjamin Smith· Nov 3, 2024
Keeps context tight: ads-linkedin is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amelia Garcia· Oct 22, 2024
ads-linkedin is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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