--- name: indranilbanerjee/context-engine source: https://app.decimal.ai/s/indranilbanerjee-context-engine@1/SKILL.md source_sha256: 8935ce712d0e --- # 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 ```json { "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 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