cmo-advisor

borghei/claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/borghei/claude-skills --skill cmo-advisor
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summary

The agent acts as a fractional CMO, providing strategic marketing guidance grounded in B2B SaaS benchmarks and proven frameworks.

skill.md

CMO Advisor

The agent acts as a fractional CMO, providing strategic marketing guidance grounded in B2B SaaS benchmarks and proven frameworks.

Workflow

  1. Gather context -- Identify company stage, ICP, current ARR, and marketing team size. Validate that at least stage and ICP are defined before proceeding.
  2. Audit current performance -- Collect funnel metrics (visitors, MQLs, SQLs, pipeline, revenue). Flag any stage where conversion is below the benchmarks in the Channel Performance table.
  3. Define positioning -- Draft a positioning statement using the template below. Confirm differentiation against the top two competitors.
  4. Build channel plan -- Select channels from the Channel Performance Framework, allocate budget using the B2B SaaS Budget Allocation split, and set per-channel CAC targets.
  5. Design lead scoring -- Configure the Lead Scoring Model and set the MQL threshold. Validate that the threshold produces a manageable volume for the sales team.
  6. Create campaign plan -- Fill in the Campaign Planning Template for the first priority campaign. Include success metrics and required assets.
  7. Establish measurement cadence -- Set daily, weekly, monthly, and quarterly review rhythms using the Reporting Cadence below.

Positioning Statement Template

For [target customer]
Who [statement of need or opportunity]
[Product name] is a [product category]
That [statement of key benefit]
Unlike [primary competitive alternative]
Our product [statement of primary differentiation]

Marketing Budget Allocation (B2B SaaS Typical)

Function % of Budget
Demand Generation 35-45%
Content & Brand 15-20%
Marketing Ops & Tech 15-20%
Events & Field 10-15%
People & Overhead 15-20%

Channel Performance Framework

Channel CAC Volume Quality Scalability
Organic Search $ High Medium Medium
Paid Search $$ Medium High High
Social Organic $ Medium Low Medium
Social Paid $$ High Medium High
Content $ High High Medium
Events $$$ Low High Low
Partnerships $$ Medium High Medium

Lead Scoring Model

Action Points
Website visit 1
Content download 5
Email open 1
Email click 3
Webinar registration 10
Webinar attendance 15
Demo request 25
Pricing page visit 10

MQL Threshold: 50 points

Lead Stages

Visitor > Known > Engaged > MQL > SAL > SQL > Opportunity > Customer

Campaign Planning Template

CAMPAIGN: [Name]
OBJECTIVE: [Specific goal]
AUDIENCE: [Target segment]
CHANNELS: [Distribution channels]
TIMELINE: [Start - End dates]
BUDGET: [Total investment]

KEY MESSAGES:
- Primary: [Main message]
- Secondary: [Supporting points]

SUCCESS METRICS:
- Leads: [Target]
- Pipeline: [Target]
- Cost per lead: [Target]

ASSETS REQUIRED:
- [ ] Landing page
- [ ] Email sequence
- [ ] Ad creative
- [ ] Content pieces

Messaging Framework

Audience Pain Point Solution Proof Point
Buyer 1 [Problem] [How we help] [Evidence]
Buyer 2 [Problem] [How we help] [Evidence]
User 1 [Problem] [How we help] [Evidence]

Reporting Cadence

  • Daily: Campaign performance (spend, clicks, conversions)
  • Weekly: Pipeline and stage-over-stage conversion
  • Monthly: Full funnel analysis, MQL-to-SQL conversion, CAC trend
  • Quarterly: Channel ROI review, budget reallocation decisions

Multi-Touch Attribution Model

Touch Weight
First Touch 30%
Lead Creation 20%
Opportunity Creation 30%
Closed Won 20%

Content Types by Funnel Stage

Stage Formats
Awareness Blog posts, social content, podcasts, industry reports
Consideration Ebooks/guides, webinars, case studies, comparison guides
Decision Product demos, ROI calculators, testimonials, implementation guides

Example: Series-B SaaS Demand-Gen Plan

A Series-B SaaS company ($8M ARR, 12-person marketing team) targeting mid-market DevOps buyers:

Budget: $2.4M annual ($200K/mo)
Allocation:
  Demand Gen (40%):  $960K -- Paid search ($300K), LinkedIn Ads ($250K),
                               Content syndication ($200K), Events ($210K)
  Content & Brand (18%): $432K
  Ops & Tech (17%):      $408K
  People (25%):          $600K

Targets:
  MQLs/month: 400  |  SQL conversion: 25%  |  Pipeline/quarter: $6M
  Blended CAC: $18K  |  CAC Payback: 14 months

Marketing Org by Stage

Stage Roles
Series A (5-10) Head of Marketing, Content/Brand, Demand Gen, Marketing Ops
Series B (10-20) CMO, Director Brand, Director Demand Gen, Manager Content, Manager Ops, ICs
Series C+ (20+) CMO, VP Brand, VP Demand Gen, VP Revenue Marketing, VP Marketing Ops, Specialized teams

Scripts

# Campaign performance analyzer
python scripts/campaign_analyzer.py --campaign Q1-ABM

# Lead scoring calculator
python scripts/lead_scoring.py --leads leads.csv

# Content calendar generator
python scripts/content_calendar.py --pillars topics.yaml

# Attribution reporter
python scripts/attribution.py --period monthly

References

  • references/brand_guidelines.md -- Brand standards and usage
  • references/demand_gen_playbook.md -- Campaign execution guide
  • references/content_strategy.md -- Content planning framework
  • references/martech_stack.md -- Technology recommendations

Tool Reference

marketing_roi_calculator.py

Calculates per-channel ROI, blended CAC, Marketing Efficiency Ratio (MER), pipeline contribution, and multi-touch attribution. Produces board-ready marketing performance reports.

# Run with demo data (6-channel mix)
python scripts/marketing_roi_calculator.py

# From JSON with channel data
python scripts/marketing_roi_calculator.py --input marketing_data.json

# JSON output
python scripts/marketing_roi_calculator.py --json

brand_health_tracker.py

Monitors brand health across 5 dimensions: awareness, perception, differentiation, engagement, and loyalty. Tracks competitive share of voice.

# Run with demo data
python scripts/brand_health_tracker.py

# From JSON with brand metrics
python scripts/brand_health_tracker.py --input brand_data.json

# JSON output
python scripts/brand_health_tracker.py --json

channel_mix_optimizer.py

Optimizes marketing budget allocation across channels based on ROI, efficiency frontiers, and diminishing returns. Projects impact of reallocation.

# Run with demo data (ROI optimization)
python scripts/channel_mix_optimizer.py

# Optimize for pipeline
python scripts/channel_mix_optimizer.py --goal pipeline

# Set total budget
python scripts/channel_mix_optimizer.py --budget 800000

# From JSON with channel performance
python scripts/channel_mix_optimizer.py --input channels.json

# JSON output
python scripts/channel_mix_optimizer.py --json

Troubleshooting

Problem Likely Cause Fix
Blended CAC increasing quarter over quarter Channel saturation or scaling into less efficient channels Run channel_mix_optimizer.py; cut lowest-ROI channels; increase investment in highest-ROI
Marketing sourced pipeline below 40% of total Over-reliance on outbound/sales-sourced; marketing underinvesting in demand gen Shift budget: target 40-60% marketing-sourced pipeline; invest in content + paid channels
Brand awareness below 30% in target market Insufficient top-of-funnel investment; brand treated as afterthought Allocate 15-20% of budget to brand; measure aided awareness quarterly
MQL-to-SQL conversion below 20% Lead scoring threshold too low or ICP mismatch Recalibrate MQL threshold; audit scoring model; tighten ICP definition
Marketing Efficiency Ratio (MER) below 1.0x Spending more on marketing than generating in new ARR Audit channel mix; pause negative-ROI channels; focus on proven converters
No brand tracking in place Half of B2B SaaS companies don't track brand at all Implement quarterly brand health survey using brand_health_tracker.py framework

Success Criteria

  • Marketing Efficiency Ratio (MER) above 1.5x -- every $1 of marketing generates $1.50+ in new ARR
  • Blended CAC below target for company stage (Series A: $15K, Series B: $25K, Series C: $35K)
  • Pipeline coverage at 3-4x of quarterly new ARR target (measured monthly)
  • Marketing-sourced pipeline contribution above 40% of total pipeline
  • CAC payback under 18 months (under 12 months for top-quartile performance)
  • Brand health score improving quarter-over-quarter (tracked via brand_health_tracker.py)
  • Channel mix optimization reviewed quarterly with budget reallocation acting on data

Scope & Limitations

In Scope: Marketing ROI calculation, channel performance analysis, brand health tracking, lead scoring, campaign planning, budget allocation optimization, multi-touch attribution, competitive share of voice.

Out of Scope: Content creation, creative design, social media posting, email campaign execution, event logistics, PR execution, website development.

Limitations: Marketing ROI calculator uses provided attribution data -- accuracy depends on attribution model quality. Brand health tracker relies on survey data which may have sampling bias. Channel mix optimizer uses historical performance with diminishing returns modeling -- future performance may differ due to market changes. MER calculation requires accurate new ARR attribution which many companies struggle to measure precisely.


Integration Points

Skill Integration
cro-advisor Pipeline contribution alignment; marketing-sourced vs sales-sourced targets
cfo-advisor Marketing budget as % of revenue; CAC payback for unit economics
ceo-advisor Brand positioning alignment with company vision
cpo-advisor Product marketing alignment; feature launch campaigns
board-deck-builder Growth/marketing section with CAC, pipeline, channel performance
chief-of-staff Routes market strategy and brand questions
competitive-intel Competitive positioning; share of voice vs competitors
how to use cmo-advisor

How to use cmo-advisor on Cursor

AI-first code editor with Composer

1

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 cmo-advisor
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/borghei/claude-skills --skill cmo-advisor

The skills CLI fetches cmo-advisor from GitHub repository borghei/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/cmo-advisor

Reload or restart Cursor to activate cmo-advisor. Access the skill through slash commands (e.g., /cmo-advisor) 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.

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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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.566 reviews
  • Charlotte Zhang· Dec 28, 2024

    Useful defaults in cmo-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chinedu Liu· Dec 24, 2024

    cmo-advisor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ishan Malhotra· Dec 24, 2024

    cmo-advisor reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chinedu Desai· Dec 24, 2024

    Keeps context tight: cmo-advisor is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Charlotte Wang· Dec 16, 2024

    I recommend cmo-advisor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Dhruvi Jain· Dec 12, 2024

    cmo-advisor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Neel Srinivasan· Dec 12, 2024

    We added cmo-advisor from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min Torres· Dec 4, 2024

    cmo-advisor has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Neel Jain· Nov 23, 2024

    Useful defaults in cmo-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Camila Martinez· Nov 19, 2024

    cmo-advisor has been reliable in day-to-day use. Documentation quality is above average for community skills.

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