tools-only Marketing Skills - Claude Code Guidance
This guide covers the 4 production-ready marketing skills and their Python automation tools.
Marketing Skills Overview
Available Skills:
- content-creator/ - Content creation, brand voice, SEO optimization (2 Python tools)
- marketing-demand-acquisition/ - Demand generation and customer acquisition (1 Python tool)
- marketing-strategy-pmm/ - Product marketing and go-to-market strategy
- campaign-analytics/ - Multi-touch attribution, funnel conversion analysis, campaign ROI calculation (3 Python tools)
Total Tools: 6 Python automation tools, 9+ knowledge bases, 15+ templates
Python Automation Tools
1. Brand Voice Analyzer (content-creator/scripts/brand_voice_analyzer.py)
Purpose: Analyzes text for formality, tone, perspective, and readability
Features:
- Formality analysis (formal vs casual language patterns)
- Tone detection (professional, conversational, technical)
- Perspective analysis (1st person, 2nd person, 3rd person)
- Readability scoring (Flesch Reading Ease formula)
- Output formats: JSON or human-readable
Usage:
# Basic analysis
python content-creator/scripts/brand_voice_analyzer.py content.txt
# JSON output for integrations
python content-creator/scripts/brand_voice_analyzer.py content.txt json
Output Example:
Brand Voice Analysis Results:
- Formality Score: 65/100 (Semi-formal)
- Tone: Professional with conversational elements
- Perspective: 2nd person (you-focused)
- Readability: 72 (College level)
Implementation Notes:
- Pure Python (standard library only)
- No external API calls or ML models
- Fast local processing (<1 second for typical content)
- Works offline
2. SEO Optimizer (content-creator/scripts/seo_optimizer.py)
Purpose: Comprehensive SEO analysis with actionable recommendations
Features:
- Keyword density analysis (primary + secondary keywords)
- Content structure evaluation (headings, paragraphs, lists)
- Meta tag validation (title, description)
- Readability assessment
- SEO score calculation (0-100)
- Actionable improvement recommendations
Usage:
# Basic SEO analysis
python content-creator/scripts/seo_optimizer.py article.md "primary keyword"
# With secondary keywords
python content-creator/scripts/seo_optimizer.py article.md "primary keyword" "secondary,keywords,here"
# JSON output
python content-creator/scripts/seo_optimizer.py article.md "keyword" --json
Output Example:
SEO Analysis Results:
- Overall SEO Score: 78/100
- Primary Keyword Density: 1.8% (target: 1-2%)
- Secondary Keyword Usage: 5/7 keywords present
- Content Structure: Good (H2, H3 hierarchy)
- Meta Description: Present (145 chars)
Recommendations:
1. Add primary keyword to H2 heading
2. Increase secondary keyword "conversion" usage
3. Add alt text to 2 images
Implementation Notes:
- 419 lines of pure algorithmic analysis
- No LLM calls or external APIs
- CLI-first design for automation
- Supports markdown and HTML input
3. Demand Generation Analyzer (marketing-demand-acquisition/scripts/)
Purpose: Analyze demand generation campaigns and acquisition funnels
Features:
- Campaign performance analysis
- Acquisition channel evaluation
- Conversion funnel metrics
- ROI calculation
Usage:
python marketing-demand-acquisition/scripts/demand_gen_analyzer.py campaign-data.csv
Campaign Analytics Tools
4. Attribution Analyzer (campaign-analytics/scripts/attribution_analyzer.py)
Purpose: Multi-touch attribution modeling across marketing channels
Features:
- Five attribution models (first-touch, last-touch, linear, time-decay, position-based)
- Configurable time-decay half-life
- Per-channel credit allocation and revenue attribution
- Conversion and non-conversion journey analysis
Usage:
python campaign-analytics/scripts/attribution_analyzer.py campaign_data.json
python campaign-analytics/scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14
python campaign-analytics/scripts/attribution_analyzer.py campaign_data.json --format json
5. Funnel Analyzer (campaign-analytics/scripts/funnel_analyzer.py)
Purpose: Conversion funnel analysis with bottleneck detection
Features:
- Stage-to-stage conversion rates and drop-off percentages
- Automatic bottleneck identification (largest absolute and relative drops)
- Overall funnel conversion rate
- Segment comparison when multiple segments provided
Usage:
python campaign-analytics/scripts/funnel_analyzer.py funnel_data.json
python campaign-analytics/scripts/funnel_analyzer.py funnel_data.json --format json
6. Campaign ROI Calculator (campaign-analytics/scripts/campaign_roi_calculator.py)
Purpose: Calculate comprehensive campaign ROI metrics with benchmarking
Features:
- ROI, ROAS, CPA, CPL, CAC calculation
- CTR and conversion rate metrics
- Industry benchmark comparison
- Underperformance flagging
Usage:
python campaign-analytics/scripts/campaign_roi_calculator.py campaign_data.json
python campaign-analytics/scripts/campaign_roi_calculator.py campaign_data.json --format json
Knowledge Bases
Content Creator References
Location: content-creator/references/
brand_guidelines.md - Brand voice framework
- 5 personality archetypes (Expert, Friend, Innovator, Guide, Motivator)
- Voice characteristics matrix
- Brand consistency checklist
- Industry-specific adaptations
content_frameworks.md - 15+ content templates
- Blog post structures (how-to, listicle, case study)
- Email campaign frameworks
- Social media content patterns
- Video script templates
- Landing page copy structure
social_media_optimization.md - Platform-specific best practices
- LinkedIn: Professional tone, 1,300 chars, hashtag strategy
- Twitter/X: Concise, 280 chars, thread patterns
- Instagram: Visual-first, captions, hashtag limits
- Facebook: Conversational, engagement tactics
- TikTok: Short-form video, trending sounds
Campaign Analytics References
Location: campaign-analytics/references/
- attribution-models-guide.md - Deep dive into 5 attribution models with formulas, pros/cons, selection criteria
- campaign-metrics-benchmarks.md - Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS
- funnel-optimization-framework.md - Stage-by-stage optimization strategies, common bottlenecks, best practices
User Templates
Content Creator Assets
Location: content-creator/assets/
- Content calendar template
- SEO checklist
- Social media posting schedule
- Content brief template
- Campaign planning worksheet
Usage: Copy template and customize for your needs
Integration Patterns
Pattern 1: Content Quality Workflow
# 1. Create draft content
vim blog-post.md
# 2. Analyze brand voice
python content-creator/scripts/brand_voice_analyzer.py blog-post.md
# 3. Optimize for SEO
python content-creator/scripts/seo_optimizer.py blog-post.md "target keyword"
# 4. Refine based on feedback
# 5. Publish
Pattern 2: Campaign Planning
# 1. Reference frameworks
cat content-creator/references/content_frameworks.md
# 2. Draft campaign content
# 3. Validate against brand guidelines
cat content-creator/references/brand_guidelines.md
# 4. Analyze performance
python marketing-demand-acquisition/scripts/demand_gen_analyzer.py campaign-results.csv
Pattern 3: Campaign Performance Analysis
# 1. Analyze multi-touch attribution
python campaign-analytics/scripts/attribution_analyzer.py journey_data.json
# 2. Identify funnel bottlenecks
python campaign-analytics/scripts/funnel_analyzer.py funnel_data.json
# 3. Calculate campaign ROI
python campaign-analytics/scripts/campaign_roi_calculator.py campaign_data.json
# 4. Document findings using templates
# Reference: campaign-analytics/assets/campaign_report_template.md
Development Commands
# Content analysis
python content-creator/scripts/brand_voice_analyzer.py content.txt
python content-creator/scripts/brand_voice_analyzer.py content.txt json
# SEO optimization
python content-creator/scripts/seo_optimizer.py article.md "main keyword"
python content-creator/scripts/seo_optimizer.py article.md "main keyword" "secondary,keywords"
# Demand generation
python marketing-demand-acquisition/scripts/demand_gen_analyzer.py data.csv
# Campaign analytics
python campaign-analytics/scripts/attribution_analyzer.py campaign_data.json
python campaign-analytics/scripts/funnel_analyzer.py funnel_data.json
python campaign-analytics/scripts/campaign_roi_calculator.py campaign_data.json
Quality Standards
All marketing Python tools must:
- Use standard library only (no external dependencies)
- Support both JSON and human-readable output
- Provide clear error messages
- Return appropriate exit codes
- Process files locally (no API calls)
Roadmap
Current (Phase 1-2): 4 skills deployed
- ✅ Content creator (brand voice + SEO)
- ✅ Demand generation & acquisition
- ✅ Product marketing strategy
- ✅ Campaign analytics (attribution, funnel, ROI)
Phase 3 (Q2 2026): Marketing expansion
- SEO content optimizer (advanced)
- Social media manager (multi-platform)
- Email marketing automation
Phase 4 (Q3 2026): Growth marketing
- Growth hacking frameworks
- Viral content analyzer
- Influencer collaboration tools
See marketing_skills_roadmap.md for detailed expansion plans.
Related Skills
- Product Team: User research, persona generation →
../product-team/ - Engineering: Web analytics integration →
../engineering-team/ - C-Level: Strategic marketing planning →
../c-level-advisor/
Best Practices
- Brand Consistency - Always reference brand_guidelines.md before creating content
- SEO-First - Run seo_optimizer.py on all published content
- Data-Driven - Use analytics to inform content strategy
- Platform-Specific - Adapt content using social_media_optimization.md guidelines
- Iterative - Analyze, optimize, republish
Additional Resources
- Marketing Roadmap:
marketing_skills_roadmap.md - Team Overview:
README.md - Main Documentation:
../CLAUDE.md
Last Updated: February 2026 Skills Deployed: 4/4 marketing skills production-ready Total Tools: 6 Python automation tools