Awesome-omni-skill 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.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/consult-codex" ~/.claude/skills/diegosouzapw-awesome-omni-skill-consult-codex && rm -rf "$T"
manifest: skills/data-ai/consult-codex/SKILL.md
source content

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:

    zsh -i -c "codex -p readonly exec 'ENHANCED_PROMPT' --json"
    

    Linux:

    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

AspectCodex (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]