Context Engine — Shared Marketing Intelligence

When to Use This Skill

User is setting up a new brand or project for marketing
User switches between brands/clients (agency use case)
Any other marketing skill needs brand context, industry data, compliance rules, or platform specs
User asks about industry benchmarks, platform requirements, or regulatory compliance

Required Context

This skill loads and manages:

Brand Profile — identity, voice, audiences, competitors, goals (from ~/.claude-marketing/brands/)
Industry Profiles — benchmarks, KPIs, channel effectiveness per industry (see industry-profiles.md)
Compliance Rules — geographic privacy laws + industry regulations (see compliance-rules.md)
Platform Specs — character limits, image sizes, algorithm signals per platform (see platform-specs.md)
Scoring Rubrics — standardized evaluation criteria for all content types (see scoring-rubrics.md)

Brand Profile Management

Loading a Brand

Check ~/.claude-marketing/brands/_active-brand.json for the currently active brand
If active brand exists, load ~/.claude-marketing/brands/{slug}/profile.json
If no active brand, prompt: "No active brand configured. Run /dm:brand-setup to create one, or tell me about your brand and I'll help set it up."

Brand Profile Schema

{
  "brand_name": "",
  "brand_slug": "",
  "created_at": "",
  "updated_at": "",
  "schema_version": "1.0.0",
  "identity": {
    "tagline": "",
    "mission": "",
    "vision": "",
    "values": [],
    "unique_selling_proposition": "",
    "positioning_statement": "",
    "elevator_pitch": ""
  },
  "business_model": {
    "type": "",
    "revenue_model": "",
    "price_range": "",
    "sales_cycle_length": "",
    "average_deal_size": "",
    "customer_lifetime_value": ""
  },
  "industry": {
    "primary": "",
    "secondary": [],
    "regulated": false,
    "regulation_codes": [],
    "compliance_notes": ""
  },
  "target_markets": [],
  "brand_voice": {
    "formality": 5,
    "energy": 5,
    "humor": 3,
    "authority": 5,
    "personality_traits": [],
    "tone_keywords": [],
    "avoid_words": [],
    "prefer_words": [],
    "this_not_that": [],
    "sample_content": []
  },
  "channels": {
    "active": [],
    "primary": "",
    "handles": {}
  },
  "competitors": [],
  "goals": {
    "primary_objective": "",
    "kpis": [],
    "budget_range": "",
    "team_size": ""
  }
}

Switching Brands

When user says "switch to [brand name]":

Run: python "scripts/setup.py" --switch-brand SLUG
The script handles fuzzy matching, validation, and updates _active-brand.json
Confirm: "Switched to [brand_name]. All marketing outputs will now use this brand's voice, compliance rules, and context."

Or use: /dm:switch-brand

How Other Modules Use This Skill

Every module should:

Check if an active brand exists before producing marketing outputs
Load relevant industry profile for benchmarks and channel recommendations
Auto-apply compliance rules based on brand's target_markets and industry.regulation_codes
Reference platform specs when creating platform-specific content
Use scoring rubrics when evaluating or grading content quality
Use adaptive scoring — run adaptive-scorer.py to get brand-specific weights before content scoring
Save campaign data — use campaign-tracker.py to persist plans, performance, and insights
Check past campaigns — before making recommendations, check if similar campaigns exist in brand history

Business Model Types

The following types trigger different funnel models, KPI frameworks, and channel strategies:

B2B_SaaS — MRR/ARR focused, product-led or sales-led growth
B2C_eCommerce — ROAS focused, product catalog marketing
B2C_DTC — Direct-to-consumer brand building + performance
B2B_Services — Thought leadership, long sales cycles
Local_Business — Google Business Profile, local SEO, reviews
Agency — Multi-client management, white-label outputs
Creator — Personal brand, audience building, monetization
Enterprise — ABM, buying committees, complex sales
Non_Profit — Donor acquisition, awareness, advocacy
Marketplace — Two-sided acquisition, liquidity, trust

Brand Voice Scoring

The brand voice scorer (brand-voice-scorer.py) automatically normalizes profile data:

Reads brand_voice.formality (1-10 int scale) → converts to 0.0-1.0 float internally
Maps brand_voice.prefer_words → preferred_words, brand_voice.avoid_words → avoided_words
Supports both the full profile schema (from brand-setup) and legacy direct schemas

Data Persistence

Campaign data, performance snapshots, and marketing insights persist across sessions:

~/.claude-marketing/brands/{slug}/
├── campaigns/              # Campaign plans and post-mortems
│   ├── _index.json         # Campaign index for quick lookup
│   └── {id}.json           # Individual campaign data
├── performance/            # Performance snapshots over time
│   └── {campaign}-{date}.json
├── insights.json           # Marketing learnings (last 200)
├── content-library/        # Saved content pieces
└── voice-samples/          # Brand voice reference content

Use campaign-tracker.py for all persistence operations.

MCP Integrations

When MCP servers are configured (in .mcp.json), modules can pull real data:

Google Analytics → actual traffic/conversion data for performance reports
Google Search Console → real ranking data for SEO audits
Google Ads / Meta → live campaign performance for paid advertising
HubSpot → CRM data for funnel analysis
Mailchimp → email campaign metrics
Google Sheets → export reports and calendars

All MCP servers connect to the USER'S OWN accounts via their API keys.

Reference Files

industry-profiles.md — 20+ industry profiles with benchmarks, channels, compliance, content types
compliance-rules.md — Geographic privacy laws (16 jurisdictions) + industry regulations (10+ sectors)
platform-specs.md — Social media, email, and ad platform specifications
scoring-rubrics.md — Content quality, ad creative, email, and landing page scoring criteria
intelligence-layer.md — How the adaptive intelligence system works (scoring, learning, persistence)

context-engine