/context-engine

name: context-engine
description: "Load brand context for marketing tasks. Use when: setting up brands, switching context, or needing industry benchmarks."
argument-hint: "[brand-slug]"

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: 1. **Brand Profile** — identity, voice, audiences, competitors, goals (from `~/.claude-marketing/brands/`) 2. **Industry Profiles** — benchmarks, KPIs, channel effectiveness per industry (see `industry-profiles.md`) 3. **Compliance Rules** — geographic privacy laws + industry regulations (see `compliance-rules.md`) 4. **Platform Specs** — character limits, image sizes, algorithm signals per platform (see `platform-specs.md`) 5. **Scoring Rubrics** — standardized evaluation criteria for all content types (see `scoring-rubrics.md`)

Brand Profile Management

Loading a Brand

1. Check `~/.claude-marketing/brands/_active-brand.json` for the currently active brand 2. If active brand exists, load `~/.claude-marketing/brands/{slug}/profile.json` 3. 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]": 1. Run: `python "${CLAUDE_PLUGIN_ROOT}/scripts/setup.py" --switch-brand SLUG` 2. The script handles fuzzy matching, validation, and updates `_active-brand.json` 3. 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: 1. Check if an active brand exists before producing marketing outputs 2. Load relevant industry profile for benchmarks and channel recommendations 3. Auto-apply compliance rules based on brand's `target_markets` and `industry.regulation_codes` 4. Reference platform specs when creating platform-specific content 5. Use scoring rubrics when evaluating or grading content quality 6. Use **adaptive scoring** — run `adaptive-scorer.py` to get brand-specific weights before content scoring 7. **Save campaign data** — use `campaign-tracker.py` to persist plans, performance, and insights 8. **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 metri