cycle-count-scheduler

AI-driven cycle counting schedule and variance analysis skill to maintain inventory accuracy with minimal operational disruption

509 stars

Best use case

cycle-count-scheduler is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

AI-driven cycle counting schedule and variance analysis skill to maintain inventory accuracy with minimal operational disruption

Teams using cycle-count-scheduler should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/cycle-count-scheduler/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/business/logistics/skills/cycle-count-scheduler/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/cycle-count-scheduler/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How cycle-count-scheduler Compares

Feature / Agentcycle-count-schedulerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

AI-driven cycle counting schedule and variance analysis skill to maintain inventory accuracy with minimal operational disruption

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Cycle Count Scheduler

## 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

```yaml
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

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