openai-gpt5-prompting

name: openai-gpt5-prompting description: Write, critique, and iterate prompts for OpenAI GPT-5 series models. Use for tasks like: (1) turning vague requirements into high-signal instructions, (2) selecting between quick vs deep reasoning behaviors, (3) creating robust structured-output prompts (JSON schemas, tool calls), (4) designing evaluation prompts and rubrics, and (5) debugging prompt failures (hallucinations, format drift, tool misuse). Trigger when the user mentions GPT-5, prompt engineering, system/developer messages, structured outputs, agent prompts, or wants best-practice templates.

GPT-5 Prompting (OpenAI)

Overview

Use this skill to produce prompts that are: unambiguous, testable, robust to edge cases, and aligned to the desired tradeoff between speed and reliability. Avoid hardcoding model IDs or limits; confirm current model names and features with $openai-docs when needed.

Prompt Pack (Use These Defaults)

System Message Template

Use a system message that defines role, priorities, safety bounds, and formatting constraints.

Required elements:

• Role: what the model is (and is not) responsible for.
• Output contract: exact format, sections, and any JSON schema constraints.
• Non-goals: what to avoid (guessing, inventing APIs, ignoring inputs).
• Clarification policy: what to ask if required info is missing.

Developer Message Template

Use a developer message for task-specific instructions, context, and constraints (without contradicting system).

Include:

• Task statement (1 sentence).
• Inputs provided and their meaning.
• Constraints and preferences (libraries, time, cost).
• Acceptance criteria (how you will judge success).

User Message Template

Make the user message concrete and data-heavy:

• Provide examples (good and bad).
• Provide edge cases.
• Provide “definition of done”.

Workflow: Draft -> Test -> Patch

1. Draft the prompt (system/developer/user separation).
2. Add 3 to 10 targeted test cases:

• Typical case, tricky case, adversarial case, empty/degenerate input.

3. Run a “prompt diff” patch cycle:

• Identify failure mode (ambiguity, missing constraints, competing goals).
• Patch the smallest instruction that fixes it.
• Re-run tests.

Structured Output Guidance

Prefer:

• Explicit JSON schema (or a strict example) plus “no extra keys”.
• Deterministic ordering only if needed.
• “If you cannot comply, return an error object with fields …” (never silently fallback).

Common Failure Modes (And Fixes)

1. Hallucinated facts:

• Add: “If unknown, say I don’t know and ask for X.”
• Add: cite-only-from-provided-sources rule when applicable.

2. Format drift:

• Add: strict schema, no prose, and a single top-level object.
• Add: “Validate output against schema before responding.”

3. Tool misuse:

• Add: tool selection rule (“Only call tools when …”), plus examples.
• Add: “Never fabricate tool outputs.”

References

See references/templates.md for copy-paste prompt templates and a test-case checklist.