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.
apm install @majiayu000/consult-codex
[](https://apm-p1ls2dz87-atlamors-projects.vercel.app/packages/@majiayu000/consult-codex)---
name: consult-codex
description: 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.
---
# 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 a temp file to avoid shell quoting issues:
**Step 1:** Write the enhanced prompt to a temp file using the Write tool:
```
Write to $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt with the ENHANCED_PROMPT content
```
**Step 2:** Execute Codex with the temp file and have at least 10 minute timeout as Codex can take a while to respond:
**macOS:**
```bash
zsh -i -c 'codex -p readonly exec "$(cat $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt)" --json 2>&1'
```
**Linux:**
```bash
bash -i -c 'codex -p readonly exec "$(cat $CLAUDE_PROJECT_DIR/tmp/codex-prompt.txt)" --json 2>&1'
```
This approach avoids all shell quoting issues regardless of prompt content.
- **For Code-Searcher:** Use Task tool with `subagent_type: "code-searcher"` with the same enhanced prompt
This parallel execution significantly improves response time.
### 2a. Parse Codex `--json` Output Files (jq Recipes)
Codex CLI with `--json` typically emits **newline-delimited JSON events** (JSONL). Some environments may prefix lines with terminal escape sequences; these recipes strip everything before the first `{` and then `fromjson?` safely.
Set a variable first:
```bash
FILE="/private/tmp/claude/.../tasks/<task_id>.output" # or a symlinked *.output to agent-*.jsonl
```
**List event types (top-level `.type`)**
```bash
jq -Rr 'sub("^[^{]*";"") | fromjson? | .type // empty' "$FILE" | sort | uniq -c | sort -nr
```
**List item types (nested `.item.type` on `item.completed`)**
```bash
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)**
```bash
jq -Rr '
sub("^[^{]*";"")
| fromjson?
| select(.type=="item.completed" and (.item.type? | IN("reasoning","agent_message")))
| "===== \(.item.type) \(.item.id) =====\n\(.item.text // "")\n"
' "$FILE"
```
**Extract just the final `agent_message` (useful for summaries)**
```bash
jq -Rr '
sub("^[^{]*";"")
| fromjson?
| select(.type=="item.completed" and .item.type?=="agent_message")
| .item.text // empty
' "$FILE" | tail -n 1
```
**Build a clean JSON array for downstream tools**
```bash
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 // "")}
]
' "$FILE"
```
**Extract command executions (command + exit code), avoiding huge stdout/stderr**
Codex JSON schemas vary slightly; this tries multiple common field names.
```bash
jq -Rr '
sub("^[^{]*";"")
| fromjson?
| select(.type=="item.completed" and .item.type?=="command_execution")
| [
(.item.id // ""),
(.item.command // .item.cmd // .item.command_line // "<no command field>"),
(.item.exit_code // .item.exitCode // "<no exit>")
]
| @tsv
' "$FILE"
```
**Discover actual fields present in `command_execution` for your environment**
```bash
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:
```bash
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-5.2) 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]