ChatGPT prompt for Python programming

I need expert Python help for this task: [TASK DESCRIPTION].

Context:
- Python version and runtime: [PYTHON ENVIRONMENT]
- Existing code, errors, or relevant interfaces: [CURRENT CODE OR ERROR]
- Inputs, expected outputs, constraints, and performance needs: [REQUIREMENTS]
- Libraries I may use, libraries I must avoid, and deployment context: [DEPENDENCIES AND CONSTRAINTS]

Work as a senior Python engineer. First, restate the problem as a precise contract: inputs, outputs, invariants, error behavior, and any assumptions. If a required detail is absent, ask up to 3 focused clarifying questions; otherwise proceed.

Then produce:
1. A recommended approach in 3-6 bullets, including why it fits the stated constraints.
2. Complete runnable Python code, not pseudocode. Include imports, type hints where they improve correctness, docstrings for public functions, and clear names.
3. A brief explanation of the important control flow and data structures.
4. A pytest test block covering the normal case, boundary cases, invalid inputs, and the most likely regression case. Use only test data that follows from my notes.
5. Complexity analysis for time and space when relevant.
6. Integration notes: dependencies, configuration, file placement, and how to run the code.

Use standard-library tools before adding a dependency unless a named library materially improves correctness. Do not silently catch broad Exception, use mutable default arguments, or rely on undocumented behavior. Preserve the public API in my existing code unless I explicitly authorize a change.

Before answering, self-check that every function call and import is defined or identified as a dependency, and that the code handles empty, null-like, malformed, and unusually large inputs where applicable. Flag any requirement, claim, or expected result that cannot be supported by the information I provided.

I need help debugging this Python failure: [TRACEBACK AND CODE]. My environment is [PYTHON VERSION AND PACKAGES], and the behavior I expected is [EXPECTED BEHAVIOR]. Identify the most likely root cause, distinguish it from secondary issues, and give the smallest safe patch. Explain why the failure occurs, then provide a pytest regression test that fails before the patch and passes after it. Do not invent missing code or configuration; ask up to 3 questions only if needed. Self-check that your proposed patch matches the traceback line and does not mask the exception.

Review this Python change for production risks: [CODE OR DIFF]. It runs in [RUNTIME CONTEXT] and must satisfy [REQUIREMENTS]. Return findings ordered by severity: correctness, security, data loss, performance, maintainability, then style. For each material finding, cite the relevant line or construct, describe a concrete failure scenario, and show a minimal correction. Also list missing tests. Do not recommend speculative rewrites. Self-check that every finding is supported by the supplied code and that proposed APIs exist in the stated environment.

Design a Python API for [FEATURE]. Consumers are [CALLERS], inputs and outputs are [DATA CONTRACT], and constraints are [CONSTRAINTS]. Propose module boundaries, public function or endpoint signatures, validation rules, error types, and a test plan. Include one small runnable skeleton using type hints, but do not implement unrequested infrastructure. Explain tradeoffs between the leading design and one simpler alternative. Ask up to 3 questions only for missing required facts. Self-check that the design handles versioning and invalid input without exposing internal exceptions.