Output Format Controller

The Problem

AI models default to conversational prose, but most real-world tasks need structured output — JSON for APIs, markdown for documentation, tables for comparison, or code blocks for implementation. Without explicit format instructions, you get a mix of explanation and content that requires manual extraction and reformatting.

The Prompt

Respond ONLY in the following format. Do not add any explanation, commentary, or text outside this structure.

FORMAT: [json | markdown | table | csv | yaml | code]

SCHEMA:
[Define the exact structure, field names, types, and constraints]

RULES:
- No prose or explanations outside the format
- Use null for unknown values, never invent data
- [Add format-specific rules: indentation, delimiter, language]

INPUT:
[Your data or question]

Example Output

{
  "analysis": {
    "framework": "React 19",
    "breaking_changes": 3,
    "migration_effort": "medium",
    "issues": [
      {"file": "App.tsx", "line": 12, "type": "deprecated-api", "fix": "Replace ReactDOM.render with createRoot"},
      {"file": "hooks/useAuth.ts", "line": 34, "type": "type-change", "fix": "Update useEffect cleanup return type"},
      {"file": "components/Modal.tsx", "line": 8, "type": "removed-prop", "fix": "Remove legacy string ref, use useRef"}
    ]
  }
}

When to Use

Use this skill when AI output feeds into another system — API responses, CI pipelines, documentation generators, or spreadsheets. It is also valuable when you need to compare multiple outputs consistently or when you are building prompt chains where one output becomes the next input.

Pro Tips

• Show the exact schema — instead of saying “return JSON,” provide a sample object with field names and types so the model has zero ambiguity.
• Use code fences — wrap your format specification in triple backticks to prevent the model from treating it as instructions to follow loosely.
• Add a “STRICT” keyword — models respond well to explicit “STRICT: no text outside the JSON block” instructions.
• Validate programmatically — for JSON/YAML output, pipe the response through a parser to catch malformed output before using it downstream.