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
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:
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:
completeness_contract, verification_loop, and missing_context_gating.grounding_rules, structured_output_contract, and dig_deeper_nudge.research_mode and citation_rules.action_safety so Codex stays narrow and avoids unrelated refactors.How to choose prompt shape:
review or adversarial-review commands when the job is reviewing local git changes. Those prompts already carry the review contract.task when the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly.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:
Prompt assembly checklist:
<task>.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.
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Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
Use this skill when building or modifying Minecraft server plugins for Paper, Spigot, or Bukkit, including plugin.yml setup, commands, listeners, schedulers, player state, team or arena systems, persistent progression, economy or profile data, configuration files, Adventure text, and version-safe API usage. Trigger for requests like "build a Minecraft plugin", "add a Paper command", "fix a Bukkit listener", "create plugin.yml", "implement a minigame mechanic", "add a perk or quest system", or "debug server plugin behavior".
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
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Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with \"auto review loop llm\" or \"llm review\".
Take openai/gpt-5-4-prompting from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.