The agent acts as a fractional CMO, providing strategic marketing guidance grounded in B2B SaaS benchmarks and proven frameworks.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncmo-advisorExecute the skills CLI command in your project's root directory to begin installation:
Fetches cmo-advisor from borghei/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate cmo-advisor. Access via /cmo-advisor in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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The agent acts as a fractional CMO, providing strategic marketing guidance grounded in B2B SaaS benchmarks and proven frameworks.
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]
| 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 | 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 |
| 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
Visitor > Known > Engaged > MQL > SAL > SQL > Opportunity > Customer
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
| 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] |
| Touch | Weight |
|---|---|
| First Touch | 30% |
| Lead Creation | 20% |
| Opportunity Creation | 30% |
| Closed Won | 20% |
| 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 |
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
| 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 |
# 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/brand_guidelines.md -- Brand standards and usagereferences/demand_gen_playbook.md -- Campaign execution guidereferences/content_strategy.md -- Content planning frameworkreferences/martech_stack.md -- Technology recommendationsCalculates 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
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
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
| 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 |
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.
| 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 |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in cmo-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
cmo-advisor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
cmo-advisor reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: cmo-advisor is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend cmo-advisor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
cmo-advisor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added cmo-advisor from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
cmo-advisor has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in cmo-advisor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
cmo-advisor has been reliable in day-to-day use. Documentation quality is above average for community skills.
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