Babysitter demand-sensing-integrator
Real-time demand sensing skill integrating POS data, market signals, and external factors for responsive planning
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/logistics/skills/demand-sensing-integrator" ~/.claude/skills/a5c-ai-babysitter-demand-sensing-integrator && rm -rf "$T"
manifest:
library/specializations/domains/business/logistics/skills/demand-sensing-integrator/SKILL.mdsource content
Demand Sensing Integrator
Overview
The Demand Sensing Integrator provides real-time demand sensing capabilities by integrating POS data, market signals, and external factors for responsive planning. It enables short-term forecast refinement and rapid inventory repositioning in response to changing demand patterns.
Capabilities
- POS Data Integration: Integrate point-of-sale data for real-time demand visibility
- Social Media Signal Processing: Monitor social media for demand indicators and trend detection
- Weather Impact Modeling: Incorporate weather forecasts and their impact on demand
- Event-Driven Demand Adjustment: Adjust demand expectations based on local and global events
- Short-Term Forecast Refinement: Refine near-term forecasts using real-time signals
- Inventory Repositioning Triggers: Generate alerts for inventory repositioning based on demand shifts
- Promotional Response Tracking: Track actual promotional response versus planned lift
Tools and Libraries
- POS Integration APIs
- Social Listening APIs
- Weather APIs
- ML Models (demand sensing)
Used By Processes
- Demand Forecasting
- Reorder Point Calculation
- Multi-Channel Fulfillment
Usage
skill: demand-sensing-integrator inputs: item: sku: "SKU001" category: "outdoor_furniture" locations: ["STORE001", "STORE002", "DC001"] real_time_data: pos_sales_last_7_days: - date: "2026-01-18" units: 45 - date: "2026-01-19" units: 52 - date: "2026-01-20" units: 48 - date: "2026-01-21" units: 65 - date: "2026-01-22" units: 78 - date: "2026-01-23" units: 82 - date: "2026-01-24" units: 95 external_factors: weather_forecast: location: "Northeast Region" forecast: "unseasonably_warm" temperature_variance: "+15F" duration_days: 7 events: - event: "home_improvement_show" location: "Boston" dates: ["2026-01-25", "2026-01-26", "2026-01-27"] expected_impact: 1.3 baseline_forecast: next_7_days: [50, 50, 55, 52, 48, 45, 45] outputs: demand_signals: trend: "accelerating" trend_strength: "strong" signals_detected: - signal: "weather_driven_demand" confidence: 92 impact_factor: 1.45 - signal: "event_proximity" confidence: 78 impact_factor: 1.15 - signal: "positive_sales_trend" confidence: 95 impact_factor: 1.25 adjusted_forecast: next_7_days: [105, 115, 125, 110, 95, 75, 65] adjustment_factor: 1.67 confidence: 85 inventory_alerts: - location: "STORE001" current_inventory: 25 projected_demand_7_days: 85 stockout_risk: "high" recommended_action: "expedite_replenishment" transfer_from: "DC001" quantity: 75 - location: "STORE002" current_inventory: 40 projected_demand_7_days: 70 stockout_risk: "medium" recommended_action: "increase_replenishment" promotional_tracking: active_promotions: [] organic_demand_increase: true
Integration Points
- Point of Sale Systems
- E-commerce Platforms
- Weather Services
- Social Media APIs
- Demand Planning Systems
Performance Metrics
- Forecast accuracy improvement
- Signal detection accuracy
- Inventory repositioning effectiveness
- Stockout prevention rate
- Response time to demand shifts