Cycle Count Scheduler
SkillProductivityAI-driven cycle counting schedule and variance analysis skill to maintain inventory accuracy with minimal operational disruption
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Cycle Count Scheduler skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/logistics/skills/cycle-count-scheduler/SKILL.md and read by ahel’s review.
Overview
The Cycle Count Scheduler is an AI-driven skill that optimizes cycle counting schedules and performs variance analysis to maintain inventory accuracy with minimal operational disruption. It uses ABC classification, statistical sampling, and historical accuracy data to prioritize counting activities and identify root causes of inventory discrepancies.
Capabilities
- ABC-Based Count Frequency Determination: Set count frequencies based on inventory value, velocity, and criticality classifications
- Statistical Sampling Design: Design statistically valid sampling plans that provide accuracy confidence with minimal counting effort
- Count Schedule Optimization: Schedule counts during low-activity periods to minimize operational disruption
- Variance Threshold Alerting: Monitor count variances against thresholds and trigger alerts for significant discrepancies
- Root Cause Analysis Automation: Analyze variance patterns to identify systemic issues and recommend corrective actions
- Perpetual vs. Physical Reconciliation: Compare perpetual inventory records with physical counts and manage adjustments
- Audit Trail Documentation: Maintain complete documentation of counts, variances, and adjustments for compliance
Tools and Libraries
- WMS APIs
- Statistical Sampling Libraries
- Inventory Audit Tools
- Analytics Platforms
Used By Processes
- Cycle Counting Program
- ABC-XYZ Analysis
- FIFO-LIFO Inventory Control
Usage
skill: cycle-count-scheduler
inputs:
inventory_profile:
total_skus: 5000
abc_distribution:
A_items: 500
B_items: 1500
C_items: 3000
count_parameters:
target_accuracy: 99.5
counting_capacity_skus_per_day: 100
available_count_days_per_week: 5
current_accuracy:
A_items: 98.5
B_items: 97.8
C_items: 96.2
outputs:
count_schedule:
- classification: "A"
count_frequency: "weekly"
skus_per_week: 100
priority_skus: ["SKU001", "SKU002", "SKU003"]
- classification: "B"
count_frequency: "monthly"
skus_per_week: 75
- classification: "C"
count_frequency: "quarterly"
skus_per_week: 60
weekly_schedule:
monday: { zone: "ZONE_A", skus: 45 }
tuesday: { zone: "ZONE_A", skus: 45 }
wednesday: { zone: "ZONE_B", skus: 50 }
thursday: { zone: "ZONE_B", skus: 50 }
friday: { zone: "ZONE_C", skus: 45 }
projected_accuracy_improvement:
A_items: 99.8
B_items: 99.2
C_items: 98.5
Integration Points
- Warehouse Management Systems (WMS)
- Inventory Management Systems
- Financial Systems (for adjustments)
- Compliance/Audit Systems
- Mobile Counting Devices
Performance Metrics
- Inventory record accuracy (IRA)
- Count variance rate
- Adjustment dollar value
- Count productivity (SKUs per hour)
- Time to count completion
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
cycle-count-scheduler- Source
- github.com/a5c-ai/babysitter
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