npx skills add sickn33/agentic-awesome-skills --skill llm-structured-output
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.
Installation
npx skills add shipshitdev/skills --skill llm-structured-output
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More details
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
master
Skill metadata
Parsed from SKILL.md frontmatter.
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.md2,625 B -
docs
SUMMARY.md254 B
History
- First seen on skills.sh
- 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