Describe the input, output, Python environment, and failure cases. MiniMax Code helps draft or revise a script with setup notes and tests so you can inspect its behavior before using real data.
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Sample prompts
Illustrative code-workspace concepts show practical scripts, not proof of execution, benchmarks, or production readiness.
A script plan validates columns, normalizes dates, and writes a separate cleaned dataset with a review log.
Plan the brief, develop the work, and review the details with AI Python Code Generator.
Task Definition
State the data shape, file rules, expected result, and failure cases. Clear constraints help turn a broad request into an implementation you can evaluate. Provide a small, non-sensitive example and expected result, including empty values, duplicates, encoding, or malformed records where relevant.
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Implementation
Ask for readable functions, dependency notes, run commands, and explanations of important choices. Use your actual environment and version constraints rather than assuming every library is available. Specify dependency and network restrictions, and keep credentials in the approved environment rather than embedding them in the script.
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Verification
Start with small fixtures and review edge cases, permissions, and failure behavior. Use copies or dry runs for risky operations and inspect the output before production use. For file or database changes, require a preview or dry run, explicit targets, and a recoverable path before execution.
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A comparison of working approaches, not a measured performance claim. Review the output before using it.
Start with the right inputs and keep a human review before the final handoff.
Describe inputs, outputs, Python version, environment, and constraints. Include a small non-sensitive example.
Ask MiniMax Code for readable code, dependency guidance, and explicit assumptions. Review the approach before running it.
Execute the code in your environment with small fixtures. Check normal cases, invalid data, and expected error handling.
Inspect output, performance, security, and failure behavior. Connect the script to real data only after the review.
Practical answers about inputs, scope, review, and where the work happens.