Agent-Skills.md

Agent Skills: Codex Skill

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.1-Codex by default for state-of-the-art software engineering.

codexcode-refactoringcode-analysisautomated-editinggpt-5
code-intelligenceID: iamladi/cautious-computing-machine--sdlc-plugin/codex
1

Install this agent skill to your local

pnpm dlx add-skill https://github.com/iamladi/cautious-computing-machine--sdlc-plugin/tree/HEAD/skills/codex

Skill Files

Browse the full folder contents for codex.

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skills/codex/SKILL.md

Skill Metadata

Name
codex
Description
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.3-Codex by default for state-of-the-art software engineering.

Codex Skill

Priorities

Correctness > Security > Efficiency

Goal

Execute OpenAI Codex CLI for automated code analysis, refactoring, and editing tasks. Default to gpt-5.3-codex model with user-specified reasoning effort. Suppress stderr thinking tokens by default unless debugging is needed.

Constraints

  • Default model: gpt-5.3-codex (ask user for reasoning effort: high, medium, or low)
  • Sandbox mode: --sandbox read-only (default), workspace-write (for edits), danger-full-access (network/broad access)
  • Always use --skip-git-repo-check flag
  • Suppress stderr by default: append 2>/dev/null to all codex exec commands
  • Resume sessions: echo "prompt" | codex exec --skip-git-repo-check resume --last 2>/dev/null (no config flags between exec and resume unless user specifies)
  • Ask permission before using high-impact flags (--full-auto, --sandbox danger-full-access)
  • Stop and report on non-zero exit codes
  • Inform user after completion: "You can resume this Codex session at any time by saying 'codex resume'"

Model Options

| Model | Best for | Context window | Key features | | --- | --- | --- | --- | | gpt-5.3-codex ⭐⭐ | Flagship model: Software engineering, code review, agentic coding | 400K input / 128K output | 25% faster, best agentic coding, $1.75/$14.00 | | gpt-5.3-codex-spark | Research preview: ultra-fast inference via Cerebras | 400K input / 128K output | 1000+ tokens/s, experimental | | gpt-5.2-codex | Code review, security analysis | 400K input / 128K output | 79% SWE-bench Pro | | gpt-5.1-codex ⭐ | Software engineering, agentic coding workflows | 400K input / 128K output | 76.3% SWE-bench, $1.25/$10.00 | | gpt-5.1-codex-mini | Cost-efficient coding (4x more usage allowance) | 400K input / 128K output | Near SOTA performance, $0.25/$2.00 | | gpt-5.1-thinking | Ultra-complex reasoning, deep problem analysis | 400K input / 128K output | Adaptive thinking depth, runs 2x slower on hardest tasks |

References

Load CLI reference and code review patterns:

  • Glob(pattern: "**/sdlc/**/skills/codex/references/codex-cli-reference.md", path: "~/.claude/plugins") → Read result

Arguments

$ARGUMENTS