Context Engine

Context Engine — Shared Marketing Intelligence

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

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

jsonCopy code
{
  "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:

Copy code
~/.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

Core context & specs

• industry-profiles.md — 20+ industry profiles with benchmarks, channels, compliance, content types
• platform-specs.md — Social media, email, and ad platform specifications
• platform-publishing-specs.md — API-level publishing requirements and content formats per platform (payloads, field mapping, validation)
• google-seo-reference.md — Concise Google SEO quick reference (crawling/indexing/serving, surfaces, schema status, algorithm dates)
• schema-templates.json — Ready-to-use JSON-LD schema templates with Google support/deprecation status
• india-market-context.md — India regional market context: regulation (DPDP), platforms, and market dynamics

Methodology frameworks

• engagement-flow-methodology.md — The 12-Part sequential engagement methodology every command, skill, and agent reads back to
• four-core-documents-spec.md — Full spec of the four Part 3 Core Documents (61 steps) that form the strategic spine
• decision-matrix-rerun.md — Which Part 3/4 documents to re-run as v2 after Part 5 client validation
• two-views-model.md — Keeping v1 (unbiased research) and v2 (client-validated) views authoritative for different questions
• update-back-rule.md — Corrections land in the source document, not just the deliverable that caught the error
• stone-vs-opinion.md — Confidence tagging of intake facts: verifiable Stone vs client Opinion
• living-instruction-file-spec.md — Spec for the per-engagement Living Project Instruction File (single source of truth)
• 30-60-90-framework.md — Default first-quarter phasing: Foundation / Optimization / Scale milestones
• actionable-persona-format.md — Six-question persona format that replaces biographical narratives
• b2b-decision-making-unit.md — B2B buying-committee roles overlay for every B2B persona
• five-digital-markets.md — Strategic taxonomy of the five digital market types; market type determines channel
• channel-families.md — Operational grouping of the 17 Part 9 channels into seven families
• in-market-out-market.md — Budget split logic between in-market (3–5%) and out-market (95–97%) audiences
• fixed-vs-variable-budget.md — Separating committed monthly spend from data-backed variable spend
• unit-economics-framework.md — CAC/LTV foundation every channel and budget decision checks back to
• three-scenario-forecasting.md — Every projection presented as conservative/expected/optimistic scenarios
• decision-framework.md — Multi-dimensional decision framework: name, weight, and score every dimension
• competitor-3-question-output.md — The three questions every competitor analysis must answer per competitor

Execution guides

• execution-workflows.md — Standard operating procedures for publishing, sending, and launching marketing actions
• seo-execution-guide.md — SEO execution via CMS APIs, search console ops, schema deployment, rank monitoring
• geo-execution-guide.md — Generative Engine Optimization: AI visibility monitoring, entities, citations
• multilingual-execution-guide.md — End-to-end multilingual campaign pipeline: translation services, RTL/Indic/CJK, SEO
• transcreation-framework.md — Transcreation vs translation vs localization, with process and QA scoring
• crm-integration-guide.md — CRM connection patterns, object mapping, and data sync (Salesforce, HubSpot, etc.)
• custom-mcp-guide.md — Adding or building MCP servers beyond the opt-in connector catalog
• self-healing-ops-guide.md — Automated campaign monitoring and correction within safety guardrails
• approval-framework.md — Risk classification determining auto-execute vs explicit-approval flows
• agency-operations-guide.md — Multi-client SOPs: onboarding, portfolio health, credential isolation, white-labeling
• team-roles-framework.md — Team roles, permissions, approval chains, and capacity planning
• guidelines-framework.md — How brand guidelines, restrictions, and style rules are structured and enforced

Compliance & EU

• compliance-rules.md — Geographic privacy laws (16 jurisdictions) + industry regulations (10+ sectors)
• eu-code-of-practice.md — EU Code of Practice on AI-generated content + AI Act Article 50 obligations for marketers

Templates & rubrics

• scoring-rubrics.md — Content quality, ad creative, email, and landing page scoring criteria
• eval-rubrics.md — Detailed scoring rubrics for the six eval dimensions used by eval-runner.py
• eval-framework-guide.md — Architecture and usage of the automated six-dimension content QA pipeline
• growth-plan-template.md — Flagship Part 8 client-facing Growth Plan deliverable template
• yearly-planner-template.md — Part 8 twelve-month operating calendar template
• monthly-report-template.md — Decision-driving monthly client report structure
• reporting-cadence.md — Matching metric review frequency (daily→quarterly) to decision velocity
• advanced-reporting-guide.md — PDF report generation, dashboards, attribution, cohort and variance reporting

Intelligence & memory

• intelligence-layer.md — How the adaptive intelligence system works (scoring, learning, persistence)
• memory-architecture.md — The 5-layer persistent brand knowledge system
• compound-intelligence-guide.md — Intelligence graph that makes each decision better than the last
• creative-intelligence-guide.md — Creative fatigue prediction, content decay, and refresh prioritization
• market-intelligence-guide.md — Macro signal detection: economic indicators, market timing, regulatory tracking
• competitive-monitoring-guide.md — Ongoing competitor change detection, social listening, share of voice
• narrative-warfare-guide.md — Narrative territory mapping, counter-narratives, and category creation
• journey-growth-guide.md — Journey state machines, growth loops, dark funnel analysis, journey simulation
• marketing-science-guide.md — Causal inference, Bayesian MMM, incrementality, and experimentation rigor
• synthetic-audience-guide.md — AI-simulated audience research, focus groups, and message testing with calibration