How do I install the Structured Output skill?

Run npx skillmds@latest add zavelinski/structured-output in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Structured Output safe to use?

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Which AI agents work with Structured Output?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Structured Output free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Structured Output?

zavelinski (@zavelinski) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Structured Output by zavelinski — Agent Skill

Name: Structured Output
Author: zavelinski

Structured Output

When an answer will be consumed by code or another step (not a human), define a schema first and make the output conform to it, instead of free text you then regex. Use for tool arguments, API payloads, extraction/classification results, agent-to-agent handoffs. Trigger with /structured-output or "make this JSON", "constrain to a schema", "structured result".

zavelinski e14b39f 2.8 KB Updated Sep 17, 2026

File contents

structured-output

If a downstream step (code, a tool, another agent) will parse the answer, decide the shape FIRST and conform to it. Free-form text that you later regex is where parse errors and bad tool-args come from.

Why this exists (evidence)

Malformed tool calls / wrong arguments are a leading cause of agent failure (~31% of production failures). A declared schema removes a whole class of these: missing fields, wrong types, prose where JSON was expected.
Constrained / schema-guided decoding is the reliable fix: output that is validated against a schema is machine-consumable by construction, instead of "usually parseable".

When to use

Tool / function arguments and API request bodies.
Extraction & classification (pull fields from text -> typed object).
Agent-to-agent handoffs in a Workflow (one stage's output is the next stage's input).
NOT for human-facing prose; this is for machine-consumed output.

The method

Define the schema first: the exact fields, types, required vs optional, allowed enums. Keep it minimal, only what the consumer needs.
Emit only the structure: no prose around it, no markdown fences if the consumer wants raw JSON.
Validate before use: parse + check against the schema. On mismatch, do not "best-effort" it, regenerate to conform or surface the error.
Constrain when you can: in the API, pass a JSON schema / tool definition so the model is forced to the shape; in a Workflow, use the agent schema option so output is validated and retried automatically.

How to run it

API / SDK: define a tool or response JSON schema and let the platform enforce it.
Workflow: pass a JSON Schema to agent(prompt, { schema }), the runtime forces a structured tool call and validates, so the returned object is typed, no parsing.
Extraction: give the target object shape up front; assert every required field is present and typed before acting.

Composes with

tool-guard: structured-output prevents bad args at generation time; tool-guard validates at call time. Belt and suspenders.
orchestrate / multi-stage Workflows: typed handoffs between stages so stage N+1 never parses stage N's prose.

Honest limits

A schema guarantees SHAPE, not correctness: a well-formed object can still hold wrong values. Pair with verification for the values that matter.
Over-schematizing human-facing answers makes them robotic; scope to machine-consumed output.

---name: structured-outputdescription: When an answer will be consumed by code or another step (not a human), define a schema first and make the output conform to it, instead of free text you then regex. Use for tool arguments, API payloads, extraction/classification results, agent-to-agent handoffs. Trigger with /structured-output or "make this JSON", "constrain to a schema", "structured result".---# structured-outputIf a downstream step (code, a tool, another agent) will parse the answer, decide the shape FIRST and conform to it. Free-form text that you later regex is where parse errors and bad tool-args come from.## Why this exists (evidence)- Malformed tool calls / wrong arguments are a leading cause of agent failure (~31% of production failures). A declared schema removes a whole class of these: missing fields, wrong types, prose where JSON was expected.- Constrained / schema-guided decoding is the reliable fix: output that is validated against a schema is machine-consumable by construction, instead of "usually parseable".## When to use- Tool / function arguments and API request bodies.- Extraction & classification (pull fields from text -> typed object).- Agent-to-agent handoffs in a Workflow (one stage's output is the next stage's input).- NOT for human-facing prose; this is for machine-consumed output.## The method1. **Define the schema first:** the exact fields, types, required vs optional, allowed enums. Keep it minimal, only what the consumer needs.2. **Emit only the structure:** no prose around it, no markdown fences if the consumer wants raw JSON.3. **Validate before use:** parse + check against the schema. On mismatch, do not "best-effort" it, regenerate to conform or surface the error.4. **Constrain when you can:** in the API, pass a JSON schema / tool definition so the model is forced to the shape; in a Workflow, use the agent `schema` option so output is validated and retried automatically.## How to run it- API / SDK: define a tool or response JSON schema and let the platform enforce it.- Workflow: pass a JSON Schema to `agent(prompt, { schema })`, the runtime forces a structured tool call and validates, so the returned object is typed, no parsing.- Extraction: give the target object shape up front; assert every required field is present and typed before acting.## Composes with- `tool-guard`: structured-output prevents bad args at generation time; tool-guard validates at call time. Belt and suspenders.- `orchestrate` / multi-stage Workflows: typed handoffs between stages so stage N+1 never parses stage N's prose.## Honest limits- A schema guarantees SHAPE, not correctness: a well-formed object can still hold wrong values. Pair with verification for the values that matter.- Over-schematizing human-facing answers makes them robotic; scope to machine-consumed output.

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