agentsop-output-format-by-model

Select output wire formats per task across coder-agents and pipelines.

287|16|Updated May 20, 2026
One-click install
npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-output-format-by-model
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agentsop-output-format-by-model
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-output-format-by-model
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-output-format-by-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you stop losing model quality by wrapping outputs in the wrong format (e.g., forcing code into JSON), and instead choose an output wire format that matches the content, the consumer, and the target model.

Core Features & Use Cases

  • Per-task format selection, not reflex: pick plain text, diff, markdown, JSON, tool-call envelopes, or grammar-constrained formats based on the generated content and how it will be consumed.
  • Empirical decision rules: uses benchmark findings (like Aider’s code-in-JSON vs unified-diff differences) and research on format-restricted reasoning to guide tradeoffs.
  • Concrete SOP + anti-patterns: provides a step-by-step workflow for deciding format across code edits, tool calls, SQL payloads, extraction schemas, and mixed-content tasks, including what to avoid.

Quick Start

Ask an AI to redesign your agent’s output schema by telling it what the model must produce, who consumes it next (parser, human, compiler, tool dispatcher), and which model you are using.

Frequently Asked Questions about agentsop-output-format-by-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Why does putting code inside JSON degrade language model output quality?

Choosing the right structured output format involves matching the wire format to your content and consumer. Select between plain-text with diff, markdown, typed JSON fields, or tool_use envelopes based on whether a parser, human, or tool dispatcher consumes the output next.

When should I use JSON schema typed fields versus plain-text diffs for coder agents?

Use plain-text with diffs for code edits to avoid nesting code inside JSON, which degrades quality. Apply JSON typed fields or tool_use envelopes with scalar arguments when a downstream tool dispatcher requires structured extraction rather than raw code generation.

How do I decide the best output format for an LLM tool-call harness?

Decide the output format by evaluating the generated content and its downstream consumer. Follow an empirical workflow to choose between markdown, JSON tool_use envelopes, or grammar-constrained output, ensuring the wire format matches the target model's strengths.

Does forcing structured JSON outputs limit the reasoning ability of language models?

Forcing structured JSON outputs can limit reasoning ability due to format-restricted constraints. Grammar-constrained output or markdown often yields better results for complex generation tasks, while JSON should be reserved for typed field extraction and tool-call payloads.

Can I use grammar-constrained output for RAG extraction pipelines?

Grammar-constrained output is applicable for RAG and extraction pipelines to enforce schema compliance. It provides an alternative to JSON typed fields when you need strict format adherence without imposing the wire-format tax that degrades model performance.