Run RFM (Recency, Frequency, Monetary) analysis on client data.

Scores clients based on purchase behavior, segments them into 11 categories, extracts ICP patterns from top performers, and detects win/loss pattern signals.

Args: source: Data source — "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live HubSpot data, "sample" for built-in demo data. industry_preset: Scoring preset — "b2b_service", "saas", "manufacturing", or "default".

Returns: JSON with scored clients, segment distribution, ICP patterns, signals, and tier recommendations.

Score a prospect against the Artefact 14.5-point ICP model with constraint context.

Evaluates Firmographic Fit (5 pts), Behavioral Fit (5 pts), and Strategic Fit (4.5 pts). Returns tier classification, score breakdown, recommended engagement strategy, and how this prospect relates to your scaling constraints.

Provide EITHER company_id (HubSpot ID, requires HUBSPOT_API_KEY) OR company_data (JSON string).

Args: company_id: HubSpot company ID to fetch and score. company_data: JSON string with company attributes. Example keys: industry, annual_revenue, employee_count, geography, tech_stack (list), growth_signals (list), content_engagement ("active"|"occasional"|"none"), purchase_history ("regular"|"occasional"|"never"), decision_maker_access ("c_suite"|"director"|"manager"|"indirect"|"none"), budget_authority ("dedicated"|"shared"|"possible"|"none"), strategic_alignment ("strong"|"partial"|"misaligned"). scoring_config: Optional JSON string to override default scoring parameters.

Returns: JSON with total score, tier, breakdown, constraint context, and recommended action.

Analyze pipeline health with velocity metrics, signal detection, and exit criteria testing.

Calculates overall health score (0-100), identifies bottleneck stages, measures stage-to-stage conversion rates, flags stalled deals, detects pipeline signals, and optionally tests deals against exit criteria.

Args: pipeline_id: Optional HubSpot pipeline ID to filter. Default: all pipelines. source: "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live data, "sample" for built-in demo data. exit_criteria: Optional JSON string with exit criteria to test against. List of objects: [{stage, test_name, required_field, is_blocking}].

Returns: JSON with health score, velocity, conversion rates, at-risk deals, signals, and optional exit criteria test results.

Scan pipeline data for all 6 signal types and return structured findings.

Detects: win_loss_pattern, conversion_drop_off, velocity_anomaly, attribution_shift, data_quality, and pipeline concentration signals.

Each signal includes signal_type, signal_strength (0-1), evidence, and recommended_action — enabling evidence-backed GTM decisions.

Args: source: "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live data, "sample" for built-in demo data. pipeline_id: Optional HubSpot pipeline ID to filter.

Returns: JSON with detected signals, summary, critical signals, and signal taxonomy.

Identify the dominant scaling constraint bottlenecking revenue.

Analyzes pipeline coverage, conversion rates, velocity, and deal characteristics to determine which of 4 constraints is dominant: Lead Generation, Conversion, Delivery, or Profitability.

Returns the Revenue Formula breakdown (Traffic × CR1 × CR2 × ... × ACV × 1/Churn) with gap-to-benchmark for each lever and the weakest link.

Args: source: "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live data, "sample" for built-in demo data. pipeline_id: Optional HubSpot pipeline ID to filter. quota: Optional quarterly revenue quota for pipeline coverage calculation.

Returns: JSON with dominant constraint, severity scores, revenue formula, and recommended focus.

Analyze a Value Engine: Growth, Fulfillment, or Innovation.

Each engine has its own stages, metrics, and health scoring:

• Growth: Create Demand → Capture Demand → Convert. Pipeline-based metrics.

• Fulfillment: Onboard → Deliver → Activate → Review → Renew → Expand.

• Innovation: Gather → Prioritize → Build/Test → Launch.

Args: engine_type: Which engine — "growth", "fulfillment", or "innovation". source: "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live data, "sample" for built-in demo data. pipeline_id: Optional HubSpot pipeline ID to filter.

Returns: JSON with engine definition, health score, metrics, signals, and recommendations.

Draft a structured GTM commit proposal following the GTM OS anatomy.

Creates a version-controlled change proposal with: Intent, Diff, Impact Surface, Risk Level, Evidence, and Measurement Plan. Does NOT apply the change — outputs a proposal for human review.

Args: entity_type: What's being changed — "icp", "persona", "positioning", "pipeline_stage", "exit_criteria", "gtm_motion", "scoring_model", "playbook". change_description: Human-readable description of the proposed change. current_state: Optional description of current state (before). proposed_state: Optional description of proposed state (after). signal_type: Optional signal type that triggered this change (win_loss_pattern, conversion_drop_off, velocity_anomaly, spiced_frequency, attribution_shift, data_quality). signal_data: Optional JSON string with structured evidence from signal detection.

Returns: JSON with structured commit proposal and next steps.