codex-2nd-opinion/SKILL.md
This skill should be used when the user asks to "get a second opinion", "ask codex", "what does GPT think", "compare with codex", "run codex", "2nd opinion", "second opinion on this code", or wants an independent analysis from OpenAI Codex CLI. Also triggered by the /codex-2nd-opinion command.
npx skillsauth add pmatos/skills codex-2nd-opinionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Invoke OpenAI Codex CLI to get an independent analysis on any discussion, plan, code, or thought. Present both perspectives fairly with a structured comparison.
The model and reasoning effort are NOT hardcoded — they come from the user's Codex configuration (~/.codex/config.toml, project-level .codex/config.toml, or Codex defaults). This matches the philosophy used by OpenAI's own codex-plugin-cc: pass through only when the user explicitly asks for a specific model or effort, otherwise let Codex's own config decide.
Collect everything needed for a self-contained prompt to Codex (which has ZERO conversation context):
Run mktemp /tmp/codex-2op-XXXXXX via Bash. Remember the returned path (e.g. /tmp/codex-2op-a8Kx3m) — you will substitute it into later steps.
Write a fully self-contained prompt to the temp file created above. This file must include:
Structure the prompt clearly with markdown headings. End the prompt with:
Please provide your independent analysis. Be specific and reference the code directly.
Execute the following command. Codex writes its response to stdout, which the Bash tool captures directly. Use a 600000ms (10 minute) timeout on the Bash tool call — high-reasoning runs can take several minutes.
codex exec \
--full-auto --sandbox read-only --ephemeral \
- < /tmp/codex-2op-XXXXXX # substitute the actual mktemp path here
Do NOT pass -m / --model or -c model_reasoning_effort=... by default. Codex reads its model and reasoning effort from ~/.codex/config.toml (and any project-level .codex/config.toml). Letting it resolve its own config means the skill works unmodified for any user.
Only add model/effort flags when the user explicitly asks for a specific one in the triggering message. Accepted effort values: none, minimal, low, medium, high, xhigh. Examples:
-c model_reasoning_effort=high.-m gpt-5.5.If the command fails (non-zero exit code or empty stdout):
codex is installed and on $PATH (install with npm install -g @openai/codex).codex login (ChatGPT account) or OPENAI_API_KEY is set for API-key auth.~/.codex/config.toml.Present the Bash tool output (Codex's response) under a clear heading:
## Codex's Analysis
If Codex's response mentions the model it ran under (it usually does in its header lines), include that in parentheses, e.g. ## Codex's Analysis (gpt-5.5, high reasoning). Otherwise keep the heading generic — don't guess.
Present the full response without editorializing. Then provide a structured, honest comparison. Follow these rules strictly:
Use this structure:
## Comparison
### Points of Agreement
[Where both analyses align]
### Points of Disagreement
[Present BOTH sides fairly — do not editorialize in Claude's favor]
### Honest Assessment
[Where is Codex stronger? Where is Claude stronger? Be genuinely honest.]
### Recommended Path Forward
[Best synthesis of both analyses]
Then ask the user: "How would you like to proceed? I can follow either analysis, combine specific parts, or take a different direction entirely."
Remove the temporary input file:
rm -f /tmp/codex-2op-XXXXXX # substitute the actual mktemp path here
data-ai
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tools
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development
This skill should be used when the user asks to "plan this", "make a plan", "create an implementation plan", "how should I implement", "design the implementation", "plan the refactor", "plan the migration", "plan the feature", "break this down into steps", "implementation strategy", "deep plan", "thorough plan", or wants a thorough, multi-phase implementation plan with codebase exploration before writing any code.