
gpt-5-4-prompting
✓ Official★ 23,600by openai · part of openai/codex-plugin-cc
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
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This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.
by openai
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
npx skills add https://github.com/openai/codex-plugin-cc --skill gpt-5-4-prompting
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GPT-5.4 Prompting
Use this skill when codex:codex-rescue needs to ask Codex or another GPT-5.4-based workflow for help.
Prompt Codex like an operator, not a collaborator. Keep prompts compact and block-structured with XML tags. State the task, the output contract, the follow-through defaults, and the small set of extra constraints that matter.
Core rules:
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Prefer one clear task per Codex run. Split unrelated asks into separate runs.
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Tell Codex what done looks like. Do not assume it will infer the desired end state.
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Add explicit grounding and verification rules for any task where unsupported guesses would hurt quality.
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Prefer better prompt contracts over raising reasoning or adding long natural-language explanations.
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Use XML tags consistently so the prompt has stable internal structure.
Default prompt recipe:
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<task>: the concrete job and the relevant repository or failure context. -
<structured_output_contract>or<compact_output_contract>: exact shape, ordering, and brevity requirements. -
<default_follow_through_policy>: what Codex should do by default instead of asking routine questions. -
<verification_loop>or<completeness_contract>: required for debugging, implementation, or risky fixes. -
<grounding_rules>or<citation_rules>: required for review, research, or anything that could drift into unsupported claims.
When to add blocks:
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Coding or debugging: add
completeness_contract,verification_loop, andmissing_context_gating. -
Review or adversarial review: add
grounding_rules,structured_output_contract, anddig_deeper_nudge. -
Research or recommendation tasks: add
research_modeandcitation_rules. -
Write-capable tasks: add
action_safetyso Codex stays narrow and avoids unrelated refactors.
How to choose prompt shape:
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Use built-in
revieworadversarial-reviewcommands when the job is reviewing local git changes. Those prompts already carry the review contract. -
Use
taskwhen the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly. -
Use
task --resume-lastfor follow-up instructions on the same Codex thread. Send only the delta instruction instead of restating the whole prompt unless the direction changed materially.
Working rules:
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Prefer explicit prompt contracts over vague nudges.
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Use stable XML tag names that match the block names from the reference file.
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Do not raise reasoning or complexity first. Tighten the prompt and verification rules before escalating.
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Ask Codex for brief, outcome-based progress updates only when the task is long-running or tool-heavy.
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Keep claims anchored to observed evidence. If something is a hypothesis, say so.
Prompt assembly checklist:
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Define the exact task and scope in
<task>. -
Choose the smallest output contract that still makes the answer easy to use.
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Decide whether Codex should keep going by default or stop for missing high-risk details.
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Add verification, grounding, and safety tags only where the task needs them.
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Remove redundant instructions before sending the prompt.
Reusable blocks live in references/prompt-blocks.md. Concrete end-to-end templates live in references/codex-prompt-recipes.md. Common failure modes to avoid live in references/codex-prompt-antipatterns.md.
npx skills add https://github.com/openai/codex-plugin-cc --skill gpt-5-4-promptingRun this in your project — your agent picks the skill up automatically.
No common issues documented yet. If you hit a problem, the repository's GitHub Issues page is the best place to look.