gpt-5-4-prompting

gpt-5-4-prompting is a skill for Claude Code from greenpolo/cc-multi-cli-plugin. It costs 40 tokens per session (778 once invoked), scanned A, a copy of gpt-5-4-prompting, Apache-2.0.

Guidance for writing requests to Codex and other GPT-5.4-based coding workflows inside the Claude Code plugin. It focuses on making the task, expected output, and checks explicit.

In plain words
What is it for?
It helps compose prompts for coding, code review, diagnosis, and research tasks with clear output formats and verification requirements.
Why use it?
It reduces vague hand-offs and makes the returned work easier to verify against the requested result.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions Claude Code; mentions Codex.

Part of the multi plugin — 8 skills, 4 commands, 10 agents, 3 hooks shipped together

Good fit It helps compose prompts for coding, code review, diagnosis, and research tasks with clear output formats and verification requirements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/greenpolo/cc-multi-cli-plugin/gpt-5-4-prompting
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add greenpolo/cc-multi-cli-plugin --skill gpt-5-4-prompting
Clone the repo
git clone --depth 1 https://github.com/greenpolo/cc-multi-cli-plugin

Made for: Claude Code.

Or install multi, the plugin that ships this one along with the rest of its 8 skills, 4 commands, 10 agents, 3 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for gpt-5-4-prompting

README.md
[![agentmods](https://agentmods.dev/badge/skills/greenpolo/cc-multi-cli-plugin/gpt-5-4-prompting.svg)](https://agentmods.dev/skills/greenpolo/cc-multi-cli-plugin/gpt-5-4-prompting)
Your own site
<a href="https://agentmods.dev/skills/greenpolo/cc-multi-cli-plugin/gpt-5-4-prompting"><img src="https://agentmods.dev/badge/skills/greenpolo/cc-multi-cli-plugin/gpt-5-4-prompting.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 778 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00778
Opus 5 $0.00020 $0.00389
Sonnet 5 $0.00008 $0.00156
Haiku 4.5 $0.00004 $0.00078

Measured 8d ago against content hash 48222490b629, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

gpt-5-4-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to gpt-5-4-prompting — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/multi/skills/gpt-5-4-prompting/SKILL.md · 55 lines

What it actually says

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:

  • Prefer one clear task per Codex run. Split unrelated asks into separate runs.
  • Tell Codex what done looks like. Do not assume it will infer the desired end state.
  • Add explicit grounding and verification rules for any task where unsupported guesses would hurt quality.
  • Prefer better prompt contracts over raising reasoning or adding long natural-language explanations.
  • Use XML tags consistently so the prompt has stable internal structure.

Default prompt recipe:

  • <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:

  • Coding or debugging: add completeness_contract, verification_loop, and missing_context_gating.
  • Review or adversarial review: add grounding_rules, structured_output_contract, and dig_deeper_nudge.
  • Research or recommendation tasks: add research_mode and citation_rules.
  • Write-capable tasks: add action_safety so Codex stays narrow and avoids unrelated refactors.

How to choose prompt shape:

  • Use built-in review or adversarial-review commands when the job is reviewing local git changes. Those prompts already carry the review contract.
  • Use task when the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly.
  • Use task --resume-last for 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:

  • Prefer explicit prompt contracts over vague nudges.
  • Use stable XML tag names that match the block names from the reference file.
  • Do not raise reasoning or complexity first. Tighten the prompt and verification rules before escalating.
  • Ask Codex for brief, outcome-based progress updates only when the task is long-running or tool-heavy.
  • Keep claims anchored to observed evidence. If something is a hypothesis, say so.

Prompt assembly checklist:

  1. Define the exact task and scope in <task>.
  2. Choose the smallest output contract that still makes the answer easy to use.
  3. Decide whether Codex should keep going by default or stop for missing high-risk details.
  4. Add verification, grounding, and safety tags only where the task needs them.
  5. 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.

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 55 lines · 40 tokens per session scan A 48222490b629

Subscribe to this mod's changes

gpt-5-4-prompting is a skill published in the GitHub repository greenpolo/cc-multi-cli-plugin (89 stars, last pushed 22d ago), licensed Apache-2.0. It adds 40 tokens to every session and 778 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gpt-5-4-prompting, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

ccc-prompt-fix

Fix and sharpen a prompt. Diagnoses it against the 6 prompt-quality patterns, returns a tightened rewrite with the reasoning, and suggests the right library prompt for your task.

KevinZai/commander · 41 tokens

composer-prompting

Internal guidance for shaping a well-specified coding task into a tight Cursor/Composer prompt before delegating it via /cursor:delegate.

freema/cursor-plugin-cc · 31 tokens

structured-prompt-writer

Structured AI prompt writing tool with 395+ built-in prompt templates. Supports both detailed mode and simple mode. Used for creating professional AI persona prompts, system prompts, or task prompts. Use this skill when the user needs to: (1) create a new AI prompt (2) design an AI persona (3) write a system prompt…

opencue/cuecards · 0 tokens

ccc-data

For large datasets and data files, the Files API can ingest CSVs, JSON, Parquet, and other formats directly — avoiding token limits for bulk data analysis. Use data-ingestion from ccc-research for document-scale inputs.

KevinZai/commander · 35 tokens

agy-prompting

Internal helper — how to tighten a user request into a sharp prompt for the Antigravity CLI (agy / Gemini 3.x with native web search and agentic tools).

MarcosNahuel/antigravity-plugin-cc · 40 tokens

prompt-engineer

Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Codex, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.

opencue/cuecards · 54 tokens