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Provide the approved label process, carrier or drop-off details, and the real refund timing. The source text can format these instructions but cannot look them up.
BeginnerAnthropic
xlsx
xlsx is an ecommerce AI skill for Anthropic, built for teams working with Codex, Claude Code. Use it to decide the next budget, product, or channel investment. Do not…
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What this Skill does
You need operating data to decide the next budget, product, or channel investment. Do not change every step at once: test Creating new Excel spreadsheets from scratch with formulas, formatting, and data… alongside Reading and analyzing spreadsheet data using pandas for… Start with a small test around “Creating new Excel spreadsheets from scratch with formulas, formatting, and data validation”, then check whether “Reading and analyzing spreadsheet data using pandas for statistical analysis and visualization” fits the way your team actually works.
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 Excel expert. Use pandas for data analysis, openpyxl for formulas and formatting. ALWAYS use Excel formulas — never hardcode calculated values. After creating/modifying, run scripts/recalc.py to recalculate and scan for #REF!, #DIV/0!, #VALUE!, #N/A, #NAME?. Financial models: blue text for inputs, black for formulas, green for internal links, red for external links, yellow background for key assumptions. Format years as text, currency with $#,##0 and unit headers, zeros as "-", percentages as 0.0%. Place ALL assumptions in separate cells with cell references. Document hardcoded values with Source annotations. Ship with ZERO formula errors. Preserve existing template conventions.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 xlsx can help you turn store, pricing, and performance data into operating decisions. Use this when the input boundary, owner, and one primary measure from data completeness, calculation accuracy, contribution margin, and decision follow-through 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 xlsx can help you turn store, pricing, and performance data into operating decisions. Use this when the input boundary, owner, and one primary measure from data completeness, calculation accuracy, contribution margin, and decision follow-through 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 xlsx can help you turn store, pricing, and performance data into operating decisions.
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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 consistent exports, metric definitions, cost data, date ranges, and known data gaps 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 xlsx to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a decision-ready analysis 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 xlsx to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a decision-ready analysis 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 xlsx 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 data completeness, calculation accuracy, contribution margin, and decision follow-through 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 data completeness, calculation accuracy, contribution margin, and decision follow-through 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.
Risks and operating notes
Do not invent labels, links, locations, or timelines
This text can draft return instructions, but it does not verify a carrier label, local drop-off point, parcel status, or refund-processing time. Use only details supplied by your actual return system.
Anthropic
AI research and product company publishing reusable skills and reference workflows for agent-based work.
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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