zod-schema-validation▌
mindrally/skills · updated Apr 8, 2026
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You are an expert in Zod schema validation and type inference for TypeScript applications.
Zod Schema Validation
You are an expert in Zod schema validation and type inference for TypeScript applications.
Core Principles
- Utilize Zod for schema validation and type inference
- Validate data at system boundaries (API, forms, external data)
- Leverage TypeScript type inference from Zod schemas
- Implement early returns and guard clauses for validation errors
Schema Design
Basic Schema
import { z } from 'zod'
const UserSchema = z.object({
id: z.string().uuid(),
email: z.string().email(),
name: z.string().min(1).max(100),
age: z.number().int().positive().optional(),
role: z.enum(['admin', 'user', 'guest']),
createdAt: z.date(),
})
type User = z.infer<typeof UserSchema>
Best Practices
- Define schemas close to where they're used
- Use
.inferto derive TypeScript types - Compose schemas using
.extend(),.merge(),.pick(),.omit() - Create reusable base schemas for common patterns
Validation Patterns
Safe Parsing
const result = UserSchema.safeParse(data)
if (!result.success) {
console.error(result.error.format())
return
}
// result.data is typed as User
Transform and Refine
const schema = z.string()
.transform((val) => val.trim().toLowerCase())
.refine((val) => val.length > 0, 'Cannot be empty')
Form Integration
- Use Zod with react-hook-form via
@hookform/resolvers/zod - Define form schemas that match your form structure
- Handle validation errors in UI appropriately
- Use
.partial()for optional update forms
API Validation
- Validate request bodies in API routes
- Validate query parameters and path params
- Return structured error responses
- Use discriminated unions for different response types
Error Handling
- Implement custom error messages for better UX
- Use
.format()for structured error output - Create custom error maps for i18n support
- Handle nested object errors appropriately
Advanced Patterns
Discriminated Unions
const ResultSchema = z.discriminatedUnion('status', [
z.object({ status: z.literal('success'), data: UserSchema }),
z.object({ status: z.literal('error'), message: z.string() }),
])
Recursive Schemas
const CategorySchema: z.ZodType<Category> = z.lazy(() =>
z.object({
name: z.string(),
children: z.array(CategorySchema),
})
)
Performance
- Precompile schemas that are used frequently
- Avoid creating schemas inside render functions
- Use
.passthrough()or.strict()intentionally - Consider partial validation for large objects
How to use zod-schema-validation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add zod-schema-validation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches zod-schema-validation from GitHub repository mindrally/skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate zod-schema-validation. Access the skill through slash commands (e.g., /zod-schema-validation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★26 reviews- ★★★★★Ganesh Mohane· Dec 28, 2024
zod-schema-validation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dev Gonzalez· Dec 12, 2024
We added zod-schema-validation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Luis Gupta· Nov 23, 2024
zod-schema-validation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Nov 19, 2024
zod-schema-validation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aarav Harris· Nov 3, 2024
Keeps context tight: zod-schema-validation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aarav Smith· Oct 22, 2024
zod-schema-validation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Luis Desai· Oct 14, 2024
Solid pick for teams standardizing on skills: zod-schema-validation is focused, and the summary matches what you get after install.
- ★★★★★Pratham Ware· Oct 10, 2024
Keeps context tight: zod-schema-validation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Diya Gonzalez· Sep 13, 2024
Useful defaults in zod-schema-validation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Min Choi· Sep 5, 2024
We added zod-schema-validation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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