Validate function inputs for types, shapes, ranges, and formats.
npx skills add https://github.com/mvillmow/ProjectOdyssey --skill validate-inputs
Please help me install this Agent Skill. Skill: validate-inputs Source: https://github.com/mvillmow/ProjectOdyssey/tree/main/.claude/skills/tier-2/validate-inputs Command: npx skills add https://github.com/mvillmow/ProjectOdyssey --skill validate-inputs
Unvalidated function inputs can lead to runtime errors, unexpected behavior, and security vulnerabilities, making code unreliable and difficult to debug.
Use the validate-inputs skill to outline a Python function for validating a tensor's type, shape, and dtype.
High-intent search queries and answers about installing and using this skill.
Input validation enforces correct data types, shapes, ranges, and formats at function entry points. Implement constraint checks on parameters—verify type, shape, and range—emit clear error messages on failure, and integrate validation with tests and documentation to catch errors early and prevent runtime failures and security vulnerabilities.
Defensive programming uses input validation to reject malformed requests before processing. Define validation rules for all API parameters, return informative error messages for invalid data, and document constraints clearly so clients understand requirements and your service remains reliable and secure.
Validate tensor type, shape, and dtype and configuration parameter ranges before use. Constraint checks catch shape mismatches and out-of-range values immediately, preventing silent corruption or unexpected behavior downstream and making debugging straightforward.
Input validation is defensive programming that stops invalid data at entry, reducing error propagation through your codebase. Clear validation messages pinpoint problems for faster debugging, prevent security vulnerabilities from unvalidated external input, and integrate naturally with testing and documentation.
Yes. Apply input validation consistently across API endpoints, internal functions, and tensor operations to enforce correctness at every boundary. The same validation patterns—type, shape, range checks with informative messages—work for external requests and function calls, strengthening overall data integrity.
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