process-simulation-modeler
Discrete event simulation skill for process modeling, scenario testing, and optimization
Profile →npx skills add a5c-ai/babysitter --skill process-simulation-modeler --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Process Simulation Modeler ## Overview The Process Simulation Modeler skill provides comprehensive capabilities for discrete event simulation. It supports process flow modeling, resource allocation analysis, scenario comparison, and capacity optimization. ## Capabilities - Process flow modeling - Resource allocation simulation - Queue behavior analysis - Scenario comparison - What-if analysis - Capacity optimization - Layout simulation - Monte Carlo simulation ## Used By Processes - LEAN-004: Kanban System Design - CAP-001: Capacity Requirements Planning - TOC-002: Drum-Buffer-Rope Scheduling ## Tools and Libraries - AnyLogic - FlexSim - Simio - SimPy ## Usage ```yaml skill: process-simulation-modeler inputs: model_type: "discrete_event" # discrete_event | continuous | agent_based process_flow: - step: "Arrival" distribution: "exponential" rate: 10 # per hour - step: "Processing" distribution: "normal" mean: 5 std_dev: 1 - step: "Inspection" distribution: "uniform" min: 2 max: 4 resources: - name: "Operator" quantity: 2 - name: "Inspector" quantity: 1 simulation_parameters: run_length: 480 # minutes replications: 30 warm_up: 60 # minutes outputs: - simulation_model - performance_
- Overview
- Capabilities
- Used By Processes
- Tools and Libraries
- Usage
- Simulation Components
- Entities
- Resources
- Queues
- Processes
- Statistical Distributions
- Performance Metrics
- Scenario Analysis Process
- Monte Carlo Simulation
What does the process-simulation-modeler skill do?
Discrete event simulation skill for process modeling, scenario testing, and optimization
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill process-simulation-modeler --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.