consult-codex
Compare OpenAI Codex GPT-5.2 and code-searcher responses for comprehensive dual-AI code analysis. Use when you need multiple AI perspectives on code questions.
Best use case
consult-codex is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compare OpenAI Codex GPT-5.2 and code-searcher responses for comprehensive dual-AI code analysis. Use when you need multiple AI perspectives on code questions.
Teams using consult-codex should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/consult-codex/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How consult-codex Compares
| Feature / Agent | consult-codex | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Compare OpenAI Codex GPT-5.2 and code-searcher responses for comprehensive dual-AI code analysis. Use when you need multiple AI perspectives on code questions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Dual-AI Consultation: Codex GPT-5.2 vs Code-Searcher You orchestrate consultation between OpenAI's Codex GPT-5.2 and Claude's code-searcher to provide comprehensive analysis with comparison. ## When to Use This Skill **High value queries:** - Complex code analysis requiring multiple perspectives - Debugging difficult issues - Architecture/design questions - Code review requests - Finding specific implementations across a codebase **Lower value (single AI may suffice):** - Simple syntax questions - Basic file lookups - Straightforward documentation queries ## Workflow When the user asks a code question: ### 1. Build Enhanced Prompt Wrap the user's question with structured output requirements: ``` [USER_QUESTION] === Analysis Guidelines === **Structure your response with:** 1. **Summary:** 2-3 sentence overview 2. **Key Findings:** bullet points of discoveries 3. **Evidence:** file paths with line numbers (format: `file:line` or `file:start-end`) 4. **Confidence:** High/Medium/Low with reasoning 5. **Limitations:** what couldn't be determined **Line Number Requirements:** - ALWAYS include specific line numbers when referencing code - Use format: `path/to/file.ext:42` or `path/to/file.ext:42-58` - For multiple references: list each with its line number - Include brief code snippets for key findings **Examples of good citations:** - "The authentication check at `src/auth/validate.ts:127-134`" - "Configuration loaded from `config/settings.json:15`" - "Error handling in `lib/errors.ts:45, 67-72, 98`" ``` ### 2. Invoke Both Analyses in Parallel Launch both simultaneously in a single message with multiple tool calls: - **For Codex GPT-5.2:** Use Bash tool directly (NOT Task with codex-cli agent - the agent intercepts queries): **macOS:** ```bash zsh -i -c "codex -p readonly exec 'ENHANCED_PROMPT' --json" ``` **Linux:** ```bash bash -i -c "codex -p readonly exec 'ENHANCED_PROMPT' --json" ``` Replace `ENHANCED_PROMPT` with the actual prompt (escape single quotes as `'\''`). - **For Code-Searcher:** Use Task tool with `subagent_type: "code-searcher"` with the same enhanced prompt This parallel execution significantly improves response time. ### 3. Handle Errors - If one agent fails or times out, still present the successful agent's response - Note the failure in the comparison: "Agent X failed to respond: [error message]" - Provide analysis based on the available response ### 4. Create Comparison Analysis Use this exact format: --- ## Codex (GPT-5.2) Response [Raw output from codex-cli agent] --- ## Code-Searcher (Claude) Response [Raw output from code-searcher agent] --- ## Comparison Table | Aspect | Codex (GPT-5.2) | Code-Searcher (Claude) | |--------|-----------------|------------------------| | File paths | [Specific/Generic/None] | [Specific/Generic/None] | | Line numbers | [Provided/Missing] | [Provided/Missing] | | Code snippets | [Yes/No + details] | [Yes/No + details] | | Unique findings | [List any] | [List any] | | Accuracy | [Note discrepancies] | [Note discrepancies] | | Strengths | [Summary] | [Summary] | ## Agreement Level - **High Agreement:** Both AIs reached similar conclusions - Higher confidence in findings - **Partial Agreement:** Some overlap with unique findings - Investigate differences - **Disagreement:** Contradicting findings - Manual verification recommended [State which level applies and explain] ## Key Differences - **Codex GPT-5.2:** [unique findings, strengths, approach] - **Code-Searcher:** [unique findings, strengths, approach] ## Synthesized Summary [Combine the best insights from both sources into unified analysis. Prioritize findings that are: 1. Corroborated by both agents 2. Supported by specific file:line citations 3. Include verifiable code snippets] ## Recommendation [Which source was more helpful for this specific query and why. Consider: - Accuracy of file paths and line numbers - Quality of code snippets provided - Completeness of analysis - Unique insights offered]
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