skills/input-validation-zod/SKILL.md
To validate incoming request data (body, query, params) against a strict schema before processing it, ensuring type safety and data integrity. Use when: When accepting any user input; When defining TypeScript interfaces for API requests; When preventing injection attacks and malformed data issues.
npx skillsauth add jyjeanne/ai-setup-forge input-validation-zodInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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To validate incoming request data (body, query, params) against a strict schema before processing it, ensuring type safety and data integrity.
z.object({ email: z.string().email() })).type CreateUserDto = z.infer<typeof UserSchema>).schema.parse(req.body).req.body with the parsed (sanitized) data..parse(), or explicit .strict()).A robust validation layer that rejects invalid requests early and provides typed request bodies to controllers.
development
Generate breadboard circuit mockups and visual diagrams using HTML5 Canvas drawing techniques. Use when asked to create circuit layouts, visualize electronic component placements, draw breadboard diagrams, mockup 6502 builds, generate retro computer schematics, or design vintage electronics projects. Supports 555 timers, W65C02S microprocessors, 28C256 EEPROMs, W65C22 VIA chips, 7400-series logic gates, LEDs, resistors, capacitors, switches, buttons, crystals, and wires.
development
Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "UX experiment", "collaborative design", or "outcome over output". Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.
development
Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.
tools
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.