shipshitdev/skills

llm-structured-output

Design prompts, schemas, validation, and recovery logic for reliable machine-readable model outputs. Use when generating JSON, typed objects, extraction results, tool arguments, or any output another system must parse safely.

First seen Apr 22, 2026

Installation

$ npx skills add shipshitdev/skills --skill llm-structured-output

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Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 35
Default branch master
Open issues 1
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
More metadata
version
1.0.0
tags
llm, structured-output, json, prompt-engineering

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,625 B
  • docs SUMMARY.md 254 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 78 installs

SKILL.md

LLM Structured Output

Build structured-output flows that downstream software can parse reliably.

Use This Skill For

  • JSON or typed-object output from a model
  • Data extraction pipelines
  • Function or tool argument generation
  • Prompt contracts that feed downstream automation
  • Validation and retry strategies for malformed model output

Workflow

1. Start With the Consumer

  • Identify exactly what the downstream system needs
  • Define required fields, optional fields, enums, and limits
  • Keep the schema as small as possible

2. Make the Contract Explicit

  • Provide the model with the expected structure
  • State field meanings and constraints clearly
  • Prefer deterministic formats over prose-plus-JSON hybrids
  • If a field is free-form, bound it with type, length, or examples

3. Validate Everything

  • Parse strictly
  • Reject unknown or malformed shapes when correctness matters
  • Validate enums, ranges, array sizes, and nested objects
  • Treat structured output as untrusted input until validated

4. Design Recovery Paths

  • Retry with the validation error when the output is close
  • Fall back to a smaller schema if the original is too complex
  • Log invalid outputs for inspection
  • Avoid silent coercion that hides model mistakes

5. Optimize for Reliability

  • Break large tasks into smaller structured steps
  • Separate reasoning from final machine-readable output when needed
  • Prefer schemas with stable keys and low ambiguity
  • Remove optional fields that are not actually useful

Rules

  • Do not ask for markdown fences around JSON unless the consumer needs them
  • Do not mix human-facing commentary into machine-facing payloads
  • Prefer enums over natural-language categories
  • Prefer arrays of objects over encoded strings
  • Make nullability intentional, not accidental

Common Failure Modes

  • Schema too broad for the task
  • Required fields that the model cannot infer
  • Free-text values that should be enums
  • Nested output with no examples or constraints
  • Parsers that accept bad data and fail later in the pipeline

Output

When using this skill, produce:

  • The target schema or shape
  • The prompt contract for generating it
  • The validation and retry plan
  • Any reliability risks or edge cases