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gpt-5-4-prompting

Compose XML-structured Codex and GPT-5.4 prompts with output contracts and verification loops.

Updated Apr 9, 2026
Search Tags:#verification#codex#diagnosis#code-review#prompting#prompt-engineering#gpt-5.4
One-click install
npx skills add https://github.com/treejh/ai-agent-skills --skill gpt-5-4-prompting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpt-5-4-prompting
Source: https://github.com/treejh/ai-agent-skills/tree/main/plugins/codex/skills/gpt-5-4-prompting
Command: npx skills add https://github.com/treejh/ai-agent-skills --skill gpt-5-4-prompting

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Ambiguous or sprawling prompts to Codex/GPT-5.4 produce unreliable, unsupported, or overly verbose outputs that waste time and require repeated iterations. This skill provides a compact, block-structured prompting approach that forces clear task definitions, explicit output contracts, and verification rules so operator-style prompts yield predictable, actionable results.

Core Features & Use Cases

  • XML block prompt recipe: Standardized tags for task, output contract, follow-through policy, verification, grounding, and safety to make prompts machine-parseable and stable.
  • Verification and completeness contracts: Built-in patterns for verification_loop, completeness_contract, and missing_context_gating to avoid unsupported guesses during debugging, fixes, or reviews.
  • Reusable templates and anti-patterns: Ready-to-use diagnosis, narrow-fix, root-cause review, research, and prompt-patching templates with examples and common antipatterns for reliable prompt construction.
  • Use Cases: Diagnose failing tests, perform focused code reviews, implement small fixes safely, and craft research/recommendation prompts that require citations and grounding.

Quick Start

Use the gpt-5-4-prompting guidance to craft a compact XML-block prompt that asks Codex to diagnose a failing test and return root cause, evidence, and the smallest safe next step.

Frequently Asked Questions about gpt-5-4-prompting

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

FAQPage Schema
How do I write Codex prompts that return verifiable code review outputs?▼

Codex prompts need XML-structured tags for output contracts, verification loops, and grounding rules to yield verifiable code review results. This forces explicit task definitions and narrowly scoped responses so outputs are actionable and machine-parseable.

What's the best way to structure GPT-5.4 prompts for debugging failing tests?▼

GPT-5.4 debugging prompts should use block-structured XML tags with completeness contracts and missing context gating. This ensures the model returns root cause, evidence, and the smallest safe next step instead of unsupported guesses.

Why do my sprawling Codex prompts produce unreliable or verbose outputs?▼

Sprawling Codex prompts lack explicit output contracts and verification rules, causing unreliable outputs. Compact XML-block prompting standardizes task definitions, follow-through policies, and safety rules to produce predictable, actionable results.

Can I use XML block prompting for research tasks that require citations?▼

XML block prompting supports research and recommendation tasks by embedding grounding rules and citation requirements directly in the prompt. This ensures GPT-5.4 returns grounded, narrowly scoped findings rather than unsupported summaries.

Do I need a specific framework to apply XML-structured prompt engineering?▼

No framework is required. XML-structured prompt engineering uses plain text tags for task, output contract, verification, and safety, making it directly compatible with Codex and GPT-5.4 operator-style workflows without dependencies.

What are the limitations of compact prompt recipes for code fixes?▼

Compact prompt recipes are designed for narrowly scoped fixes, diagnosis, and code review. They may not suit large-scale architectural changes, as they enforce strict output contracts and verification loops optimized for small, safe, verifiable modifications.

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