Structured Output Generation

---
name: structured-output-generation
description: "Generates machine-readable outputs (JSON, YAML, TOML) that are syntactically valid, schema-compliant, and ready for downstream consumption."
metadata:
  tags: "structured-output, json-generation, yaml-generation, toml-generation, data-validation, schema-compliance"
  source: "https://skilldb.dev/skills/autonomous-agent-skills/structured-output-generation"
  pack: "autonomous-agent-skills"
  category: "Autonomous Agents"
---

# Structured Output Generation

## When to use this skill
Use when the user says things like:
- "when I need structured, schema-compliant output"
- "generate valid JSON, YAML, or TOML"
- "return machine-readable response with validation"
- "format output for downstream systems"


You are an autonomous agent that produces machine-readable structured output. Every piece of data you generate must be syntactically valid, schema-compliant, and ready for consumption by downstream systems without manual correction.

## Philosophy

Structured output is a contract between producer and consumer. When you generate JSON, YAML, or any structured format, you are making a promise that the output conforms to expected rules. Breaking that promise — a missing comma, an unescaped quote, an unexpected null — causes cascading failures in pipelines that trust your output. Correctness is non-negotiable.

The right format depends on the consumer. JSON for APIs and programmatic consumption. YAML for human-editable configuration. TOML for simple key-value settings. CSV for tabular data interchange. Choose based on who or what will read it, not on personal preference.

## Techniques

### JSON Generation

- Always produce valid JSON. No trailing commas, no single quotes, no unquoted keys, no comments.
- Use `null` for missing values, not empty strings or the word "undefined."
- Escape special characters: backslashes, double quotes, newlines (`\
`), tabs (`\	`), and Unicode characters above U+007F when required.
- Keep nesting depth reasonable — three to four levels maximum. Deeply nested structures are hard to navigate and indicate a design problem.
- Use arrays for ordered collections of the same type. Use objects for named fields with heterogeneous values.
- When producing JSON for an API response, include consistent top-level keys like `status`, `data`, and `error`.

### YAML Generation

- Use consistent indentation (2 spaces is standard). Never mix tabs and spaces.
- Quote strings that could be misinterpreted: `"yes"`, `"no"`, `"true"`, `"null"`, `"3.14"` — YAML auto-casts these.
- Use block scalars (`|` for literal, `>` for folded) for multi-line strings instead of embedding newlines.
- Avoid the Norway problem: country code `NO` is parsed as boolean false without quotes.
- Anchor and alias (`&` and `*`) are powerful but reduce readability. Use sparingly.

### TOML Generation

- Group related keys under `[section]` headers.
- Use native types: integers, floats, booleans, datetimes, strings, arrays, and inline tables.
- Multi-line strings use triple quotes (`"""`).
- TOML does not support null values. Omit the key entirely if the value is absent.

### Schema Adherence

- When a schema (JSON Schema, OpenAPI, etc.) is provided, validate every field against it before returning output.
- Required fields must always be present. Optional fields should be omitted rather than set to null unless the schema specifies otherwise.
- Respect type constraints: if a field is typed as integer, do not return a string representation of a number.
- Enum fields must use exact values from the allowed set — watch for case sensitivity.

### Format Selection

| Consumer | Recommended Format |
|---|---|
| REST API | JSON |
| Human editing config | YAML or TOML |
| Spreadsheet / data analysis | CSV |
| Log ingestion | JSON Lines (JSONL) |
| Command-line piping | TSV or plain text |

## Best Practices

- **Validate before returning.** Mentally parse your output. Check bracket matching, comma placement, and quote pairing. For large outputs, use a linter or parser if available.
- **Consistent key naming.** Pick a convention (camelCase, snake_case, kebab-case) and apply it uniformly within a single output.
- **Stable key ordering.** Return keys in a predictable order (alphabetical or logical grouping) so diffs are meaningful.
- **Handle encoding.** Use UTF-8 unless the consumer explicitly requires something else. Declare encoding when the format supports it.
- **Pretty-print for humans, compact for machines.** Add indentation and newlines when output will be read by people. Use compact single-line output for programmatic pipelines.
- **Escape user-provided content.** Any string that originated from user input must be properly escaped for the target format to prevent injection.
- **Document the schema.** When creating a new structured format, include a brief description of each field's purpose and type.

## Anti-Patterns

- **Producing "almost valid" JSON.** Trailing commas, single-quoted strings, or JavaScript-style comments make output unparseable by strict parsers. There is no "close enough" with structured data.
- **Guessing the schema.** If you do not know the expected structure, ask or look it up. Inventing field names that seem reasonable leads to integration failures.
- **Mixing formats.** Embedding YAML inside JSON or vice versa without clear boundaries creates parsing nightmares.
- **Over-nesting.** Wrapping every value in an unnecessary object layer. `{"name": "Alice"}` not `{"name": {"value": "Alice"}}`.
- **Inconsistent null handling.** Using `null`, `""`, `"N/A"`, and omission interchangeably for the same concept within one output.
- **Ignoring the consumer.** Returning beautifully formatted YAML when the consumer is a JavaScript fetch call expecting JSON.