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Plan an incremental library migration with rollback checkpoints
Break a dependency replacement into reviewable stages with compatibility checks, exit criteria and realistic rollback limits.
Generate a dialect-specific regular expression with match boundaries, annotated examples and stated limits.
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Version 1 · 3 variables · 3 required
Fill in the values below — the customized prompt updates as you type. marks a required value.
The precise matching requirement. Default: Validate ticket IDs consisting of exactly three uppercase ASCII letters, a hyphen, and four digits.
Regex dialect and host-language context. Default: Python re; full-string validation with re.fullmatch.
Positive and negative examples with intended results. Default: Accept: ABC-1234, XYZ-0000. Reject: AB-1234, abc-1234, ABC-12345, prefixABC-1234.
Build a regular expression for Validate ticket IDs consisting of exactly three uppercase ASCII letters, a hyphen, and four digits..
Engine and use: Python re; full-string validation with re.fullmatch.
Examples: Accept: ABC-1234, XYZ-0000. Reject: AB-1234, abc-1234, ABC-12345, prefixABC-1234.
Clarify full-string validation versus substring extraction, case sensitivity, multiline handling, and allowed character classes from the inputs. If examples conflict with the rule, identify the conflict before choosing behavior.
Return the regex in both raw form and the escaping required by the specified host language. Explain each meaningful part briefly. Provide a case table covering every supplied example plus three informative boundaries, with expected match status and captures.
Avoid claiming broad validation of complex formats such as every valid email address when the rule is narrower. Mention relevant Unicode or backtracking limits. Mark the case outcomes as expected until run in the actual engine; do not claim tested performance.No public examples yet for version 1
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Break a dependency replacement into reviewable stages with compatibility checks, exit criteria and realistic rollback limits.
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Define realistic traffic, workload data, measurements and stopping rules for an authorized performance test.
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Find source-visible labeling, semantics and keyboard risks, while separating code findings from checks that require a browser.
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Map feature assets, trust boundaries and data flows to concrete threats, existing controls and targeted verification tasks.
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Turn an API contract into a small client design that handles retryable failures, deadlines and idempotency deliberately.
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Interpret supplied plan evidence and prioritize query or index experiments without inventing costs or speedups.
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