consult-codex

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:

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]