Structures the analysis before making a recommendation
The source asks for analysis, classification, ranking, or scoring before it reaches a conclusion or next action.
AdvancedAlireza Rezvani
Capacity Planner
Capacity Planner is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to prioritize stocking,…
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What this Skill does
You need to prioritize stocking, replenishment, or purchasing decisions. Do not change every step at once: test Annual ops capacity planning with Erlang-C sizing against P50/P90/P99 demand… alongside Quarterly re-sizing when demand shifts >15% or attrition spikes, then consider… Start with a small test around “Annual ops capacity planning with Erlang-C sizing against P50/P90/P99 demand distributions”, then check whether “Quarterly re-sizing when demand shifts >15% or attrition spikes” fits the way your team actually works.
What makes it different
Before you use it
Source file or structured data
This instruction refers to file or tabular input. Prepare the requested file and confirm that the model you use can read it.
Python runtime
The source includes a Python command or script. A compatible local Python environment is required for that part of the workflow.
No installation needed
Copy and paste into a model chat
- Expand and copy the complete original Skill.md below.
- Open a new conversation in a compatible AI model, then paste it into the chat box.
- Add verified task details, run one low-risk example, and review the result before using it in store operations.
Original Skill.md
The complete source is shown below. Copy it from the top right to use it.
You are an ops leader sizing queued-work teams. Intake P50/P90/P99 daily ticket volume. Run capacity_modeler.py with demand, AHT, SLA target, FTE, and shrinkage per --profile. Read 80%-utilization row as sizing point. Run utilization_analyzer.py to flag sustained >85% utilization (throughput-collapse risk) and spread >30pp (UNBALANCED). Run hiring_sequencer.py for 12-month plan with ramp, attrition, and manager triggers at 7 ICs/manager. Walk the forcing-question library one at a time. Never plan to 100% utilization, treat ramp as instant, or size to P50 only. If you only have averages, stop and pull the distribution — single-point demand estimates are the most expensive anti-pattern in ops.Get started
Starter prompts for the main use cases—copy and use them directly.
01Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Capacity Planner can help you plan inventory, purchasing, capacity, suppliers, and replenishment. Use this when the input boundary, owner, and one primary measure from forecast error, stockout rate, excess stock, cash tied up, and service level are written down.
Show prompt and variablesHide prompt and variables
Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Capacity Planner can help you plan inventory, purchasing, capacity, suppliers, and replenishment. Use this when the input boundary, owner, and one primary measure from forecast error, stockout rate, excess stock, cash tied up, and service level are written down.
Use the Skill above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where Capacity Planner can help you plan inventory, purchasing, capacity, suppliers, and replenishment.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
02Prepare the input and guardrails
Collect only the clean SKU history, lead times, current stock, purchase constraints, and margin assumptions needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Show prompt and variablesHide prompt and variables
Prepare the input and guardrails
Collect only the clean SKU history, lead times, current stock, purchase constraints, and margin assumptions needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Skill above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the clean SKU history, lead times, current stock, purchase constraints, and margin assumptions needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
03Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Capacity Planner to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a inventory or purchasing recommendation that a responsible operator can inspect, and it stayed inside the approved boundary.
Show prompt and variablesHide prompt and variables
Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Capacity Planner to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a inventory or purchasing recommendation that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Skill above to help me with this task: Inspect the source Skill, then run it.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Read the source, installation method, and permission notes before adding Capacity Planner to a separate test project. Keep commands and Skill text exactly as published.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
04Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when forecast error, stockout rate, excess stock, cash tied up, and service level has a pre-test baseline, and errors and exceptions are logged separately.
Show prompt and variablesHide prompt and variables
Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when forecast error, stockout rate, excess stock, cash tied up, and service level has a pre-test baseline, and errors and exceptions are logged separately.
Use the Skill above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
Alireza Rezvani
HealthTech CTO and open-source maintainer focused on applied AI, agentic coding, and practical skills for product, research, growth, and operations teams.
Risk, permissions, and limitations
Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.
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