Dual-AI Consultation: Codex GPT-5.3 vs Code-Searcher

You orchestrate consultation between OpenAI's Codex GPT-5.3 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-5.3: Use a temp file to avoid shell quoting issues:

Step 1: Write the enhanced prompt to a temp file using the Write tool:

Write to $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt with the ENHANCED_PROMPT content

Step 2: Execute Codex with the temp file and have at least 10 minute timeout as Codex can take a while to respond:

macOS:

zsh -i -c 'codex -p readonly exec "$(cat $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt)" --json 2>&1'

Linux:

bash -i -c 'codex -p readonly exec "$(cat $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt)" --json 2>&1'

This approach avoids all shell quoting issues regardless of prompt content.

For Code-Searcher: Use Task tool with subagent_type: "code-searcher" with the same enhanced prompt

This parallel execution significantly improves response time.

2a. Parse Codex --json Output Files (jq Recipes)

Codex CLI with --json typically emits newline-delimited JSON events (JSONL). Some environments may prefix lines with terminal escape sequences; these recipes strip everything before the first { and then fromjson? safely.

Set a variable first:

FILE="/private/tmp/claude/.../tasks/<task_id>.output" # or a symlinked .output to agent-.jsonl

List event types (top-level .type )

jq -Rr 'sub("^[^{]*";"") | fromjson? | .type // empty' "$FILE" | sort | uniq -c | sort -nr

List item types (nested .item.type on item.completed )

jq -Rr 'sub("^[^{]*";"") | fromjson? | select(.type=="item.completed") | .item.type? // empty' "$FILE" | sort | uniq -c | sort -nr

Extract only “reasoning” and “agent_message” text (human-readable)

jq -Rr ' sub("^[^{]*";"") | fromjson? | select(.type=="item.completed" and (.item.type? | IN("reasoning","agent_message"))) | "===== (.item.type) (.item.id) =====\n(.item.text // "")\n" ' "$FILE"

Extract just the final agent_message (useful for summaries)

jq -Rr ' sub("^[^{]*";"") | fromjson? | select(.type=="item.completed" and .item.type?=="agent_message") | .item.text // empty ' "$FILE" | tail -n 1

Build a clean JSON array for downstream tools

jq -Rn ' [inputs | sub("^[^{]*";"") | fromjson? | select(.type=="item.completed" and (.item.type? | IN("reasoning","agent_message"))) | {type:.item.type, id:.item.id, text:(.item.text // "")} ] ' "$FILE"

Extract command executions (command + exit code), avoiding huge stdout/stderr

Codex JSON schemas vary slightly; this tries multiple common field names.

jq -Rr ' sub("^[^{]*";"") | fromjson? | select(.type=="item.completed" and .item.type?=="command_execution") | [ (.item.id // ""), (.item.command // .item.cmd // .item.command_line // "<no command field>"), (.item.exit_code // .item.exitCode // "<no exit>") ] | @tsv ' "$FILE"

Discover actual fields present in command_execution for your environment

jq -Rr ' sub("^[^{]*";"") | fromjson? | select(.type=="item.completed" and .item.type?=="command_execution") | (.item | keys | @json) ' "$FILE" | head -n 5

3. Cleanup Temp Files

After processing the Codex response (success or failure), clean up the temp prompt file:

rm -f $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt

This prevents stale prompts from accumulating and avoids potential confusion in future runs.

4. 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

5. Create Comparison Analysis

Use this exact format:

Codex (GPT-5.3) Response

[Raw output from codex-cli agent]

Code-Searcher (Claude) Response

[Raw output from code-searcher agent]

Comparison Table

Aspect Codex (GPT-5.3) 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-5.3: [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:

• Corroborated by both agents

• Supported by specific file:line citations

• 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]