Agent skill
output-error-missing-schemas
Fix missing schema definitions in Output SDK steps. Use when seeing type errors, undefined properties at step boundaries, validation failures, or when step inputs/outputs aren't being properly typed.
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163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/output-error-missing-schemas
SKILL.md
Fix Missing Schema Definitions
Overview
This skill helps diagnose and fix issues caused by steps that lack explicit inputSchema or outputSchema definitions. Schemas are essential for type safety, validation, and proper data serialization between steps.
When to Use This Skill
You're seeing:
- Type errors at step boundaries
- Undefined properties in step inputs/outputs
- Validation failures when passing data between steps
- TypeScript errors about incompatible types
- Runtime errors about unexpected data shapes
Root Cause
Steps without explicit schemas:
- Don't validate input data at runtime
- Don't provide TypeScript type inference
- May serialize/deserialize data incorrectly
- Can pass undefined or malformed data silently
Symptoms
Missing Input Schema
typescript
// WRONG: No input validation
export const processData = step({
name: 'processData',
// inputSchema: missing!
outputSchema: z.object({ result: z.string() }),
fn: async (input) => {
return { result: input.value }; // input.value might be undefined!
}
});
Missing Output Schema
typescript
// WRONG: No output validation
export const fetchData = step({
name: 'fetchData',
inputSchema: z.object({ id: z.string() }),
// outputSchema: missing!
fn: async (input) => {
return { data: await getFromApi(input.id) }; // Output shape not validated
}
});
Both Schemas Missing
typescript
// WRONG: No validation at all
export const transformData = step({
name: 'transformData',
// No schemas!
fn: async (input) => {
return transform(input);
}
});
Solution
Always define both inputSchema and outputSchema for every step:
Complete Step Definition
typescript
import { z, step } from '@output.ai/core';
export const processData = step({
name: 'processData',
inputSchema: z.object({
id: z.string(),
value: z.number(),
optional: z.string().optional(),
}),
outputSchema: z.object({
result: z.string(),
processedAt: z.number(),
}),
fn: async (input) => {
// input is fully typed: { id: string, value: number, optional?: string }
return {
result: `Processed ${input.id}`,
processedAt: Date.now(),
};
// output is validated against outputSchema
},
});
Schema Definition Best Practices
Use Descriptive Schemas
typescript
// Good: Clear, descriptive schema
inputSchema: z.object({
userId: z.string().uuid(),
email: z.string().email(),
age: z.number().int().positive(),
})
Handle Optional Fields
typescript
inputSchema: z.object({
required: z.string(),
optional: z.string().optional(),
withDefault: z.string().default('fallback'),
})
Use Schema Composition
typescript
// Define reusable schemas
const userSchema = z.object({
id: z.string(),
name: z.string(),
});
const addressSchema = z.object({
street: z.string(),
city: z.string(),
});
// Compose in step
inputSchema: z.object({
user: userSchema,
address: addressSchema,
})
Handle Arrays and Nested Objects
typescript
inputSchema: z.object({
items: z.array(z.object({
id: z.string(),
quantity: z.number(),
})),
metadata: z.record(z.string()),
})
Finding Steps Without Schemas
Search your codebase:
bash
# Find step definitions
grep -rn "step({" src/workflows/
# Look for steps without inputSchema
grep -A5 "step({" src/workflows/ | grep -B2 "fn:"
# Check if schemas are present
grep -rn "inputSchema:" src/workflows/
grep -rn "outputSchema:" src/workflows/
Review each step definition to ensure both schemas are present.
Benefits of Explicit Schemas
- Runtime Validation: Catches data errors early
- Type Safety: Full TypeScript inference in step functions
- Documentation: Schemas document expected data shapes
- Serialization: Ensures proper data serialization between steps
- Error Messages: Clear validation errors when data is wrong
Common Schema Patterns
API Response Steps
typescript
export const fetchUser = step({
name: 'fetchUser',
inputSchema: z.object({
userId: z.string(),
}),
outputSchema: z.object({
user: z.object({
id: z.string(),
name: z.string(),
email: z.string(),
}).nullable(), // Handle not found
found: z.boolean(),
}),
fn: async (input) => {
const user = await api.getUser(input.userId);
return { user, found: user !== null };
},
});
Transformation Steps
typescript
export const transformData = step({
name: 'transformData',
inputSchema: z.object({
raw: z.array(z.unknown()),
}),
outputSchema: z.object({
processed: z.array(z.object({
id: z.string(),
value: z.number(),
})),
count: z.number(),
}),
fn: async (input) => {
const processed = input.raw.map(transformItem);
return { processed, count: processed.length };
},
});
Void Output Steps
For steps that don't return meaningful data:
typescript
export const logEvent = step({
name: 'logEvent',
inputSchema: z.object({
event: z.string(),
data: z.record(z.unknown()),
}),
outputSchema: z.object({
logged: z.literal(true),
}),
fn: async (input) => {
await logger.log(input.event, input.data);
return { logged: true };
},
});
Verification
After adding schemas:
- TypeScript check:
npm run output:workflow:buildshould pass without type errors - Runtime test:
npx output workflow run <name> '<input>'should validate correctly - Invalid input test: Pass invalid data and verify validation errors appear
Related Issues
- For Zod import issues, see
output-error-zod-import - For type mismatches despite schemas, verify schema matches actual data
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