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:
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-5.3) 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]

consult-codex

NPX Install

Tags

SKILL.md Content

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

When to Use This Skill

Workflow

1. Build Enhanced Prompt

2. Invoke Both Analyses in Parallel

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

3. Cleanup Temp Files

4. Handle Errors

5. Create Comparison Analysis

Codex (GPT-5.3) Response

Code-Searcher (Claude) Response

Comparison Table

Agreement Level

Key Differences

Synthesized Summary

Recommendation