context-engine
BusinessLoad brand context for marketing tasks. Use when: setting up brands, switching context, or needing industry benchmarks.
How to use this skill
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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.jsonfor 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 /digital-marketing-pro: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 "${CLAUDE_PLUGIN_ROOT}/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: /digital-marketing-pro: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_marketsandindustry.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.pyto get brand-specific weights before content scoring - Save campaign data — use
campaign-tracker.pyto 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 growthB2C_eCommerce— ROAS focused, product catalog marketingB2C_DTC— Direct-to-consumer brand building + performanceB2B_Services— Thought leadership, long sales cyclesLocal_Business— Google Business Profile, local SEO, reviewsAgency— Multi-client management, white-label outputsCreator— Personal brand, audience building, monetizationEnterprise— ABM, buying committees, complex salesNon_Profit— Donor acquisition, awareness, advocacyMarketplace— 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)