ultrathink - Take a deep breath. We're not here to write code. We're here to make a dent in the universe.

The Vision

Codex orchestration should feel inevitable: minimal risk, maximum clarity.

Your Work, Step by Step

•Select strategy: Model, sandbox, and reasoning effort.
•Prepare context: Inject the smallest, sharpest prompt.
•Execute: Run Codex with clear constraints.
•Validate output: Check for correctness and scope compliance.
•Summarize: Report findings and next steps.

Ultrathink Principles in Practice

•Think Different: Choose the safest path to insight.
•Obsess Over Details: Respect sandbox boundaries.
•Plan Like Da Vinci: Shape the prompt before execution.
•Craft, Don't Code: Keep commands precise.
•Iterate Relentlessly: Re-run with refined prompts.
•Simplify Ruthlessly: Reduce noise and scope.

Codex CLI Integration Skill (v2.37)

This skill enables Claude to orchestrate OpenAI's Codex CLI (v0.79+) with the gpt-5.2-codex model for code generation, review, analysis, and automated editing. Includes Context7 MCP integration for documentation access.

When to Use This Skill

Ideal Use Cases:

•Complex code analysis requiring deep understanding
•Large-scale refactoring across multiple files
•Automated code generation with safety controls
•Second opinion / cross-validation on code implementations
•Parallel processing of independent code tasks
•Session-based iterative development workflows

Quick Start

Prerequisites

Verify Codex CLI installation:

bash

codex --version  # Should show v0.50.0+

Authentication (first time):

bash

codex  # Interactive login via ChatGPT account
# Or: export CODEX_API_KEY=sk-...

Model Selection

Default model: gpt-5.2-codex - Optimized for software engineering tasks

Model	Use Case
`gpt-5.2-codex`	Default, optimized for code (recommended)
`gpt-5.2`	General purpose, complex reasoning
`gpt-5.2-codex-max`	Maximum context, large codebases
`o3`	Highest reasoning capability
`o4-mini`	Fast, simple tasks

Sandbox Modes

Mode	Permission	Use Case
`read-only`	Read files only (default)	Analysis, review
`workspace-write`	Read/write workspace	Code editing, refactoring
`danger-full-access`	Full system access	Install deps, network

Core Commands

Basic Execution

bash

# Read-only analysis (default)
codex exec -m gpt-5.2-codex "analyze src/auth for security issues"

# Code editing (workspace-write)
codex exec -m gpt-5.2-codex --full-auto "fix bug in login.py"

# With reasoning effort
codex exec -m gpt-5.2-codex --config model_reasoning_effort=high "complex analysis"

# Skip git check (non-git directories)
codex exec --skip-git-repo-check "analyze code"

Suppress Thinking Tokens

Add 2>/dev/null to suppress stderr (thinking tokens):

bash

codex exec -m gpt-5.2-codex "review code" 2>/dev/null

Session Resume

bash

# Resume last session (stdin for prompt - required due to CLI bug)
echo "continue with fixes" | codex exec resume --last 2>/dev/null

# Resume with full-auto
echo "apply fixes" | codex exec resume --last --full-auto 2>/dev/null

# Resume specific session
echo "follow-up" | codex exec resume SESSION_ID

Important: Resume inherits model, reasoning, and sandbox from original session.

JSON Output

bash

# JSON Lines output
codex exec --json -m gpt-5.2-codex "analyze code" > output.jsonl

# Extract session ID
SID=$(grep -o '"thread_id":"[^"]*"' output.jsonl | head -1 | cut -d'"' -f4)

# Extract agent message
grep '"type":"agent_message"' output.jsonl | jq -r '.item.text'

Orchestration Patterns

Pattern 1: Context Pre-injection

Claude collects information first, injects into prompt for faster execution:

bash

# Collect errors
ERRORS=$(npm run lint 2>&1 | grep error)

# Inject context
codex exec -m gpt-5.2-codex --full-auto "Fix these errors:
$ERRORS

Files: src/auth/login.ts, src/utils/token.ts
Constraint: Only modify listed files."

Pattern 2: Session Reuse

Related tasks reuse sessions for context preservation:

bash

# First: analyze
codex exec -m gpt-5.2-codex "analyze src/auth for issues"

# Continue: fix (reuses context)
echo "fix the issues you found" | codex exec resume --last --full-auto

When to reuse:

•Analyze → Fix (knows findings)
•Implement → Test (knows implementation)
•Test → Fix (knows failures)

Pattern 3: Parallel Execution

Independent tasks run simultaneously:

bash

# Parallel analysis
codex exec --json -m gpt-5.2-codex "analyze auth" > auth.jsonl 2>&1 &
codex exec --json -m gpt-5.2-codex "analyze api" > api.jsonl 2>&1 &
wait

# Parallel fixes with resume
AUTH_SID=$(grep -o '"thread_id":"[^"]*"' auth.jsonl | head -1 | cut -d'"' -f4)
echo "fix issues" | codex exec resume $AUTH_SID --full-auto &
# ...
wait

Parallelizable:

•Different directories/modules
•Different analysis dimensions (security/performance/quality)
•Read-only operations

Must serialize:

•Writing same files
•Dependent on prior results

Interactive Workflow

Before running Codex tasks, confirm with user:

•Model selection: gpt-5.2-codex or gpt-5.2?
•Reasoning effort: low, medium, or high?
•Sandbox mode: Based on task requirements

Decision Matrix

Task Type	Sandbox	Flags
Review/analysis	`read-only`	`--sandbox read-only 2>/dev/null`
Apply local edits	`workspace-write`	`--full-auto 2>/dev/null`
Network/deps	`danger-full-access`	`--sandbox danger-full-access --full-auto`
Resume session	Inherited	`echo "prompt" \| codex exec resume --last`

Code Review Workflow

Independent Review

Use Codex as second opinion on Claude's work:

bash

codex exec -m gpt-5.2-codex --sandbox read-only "Review src/payment/processor.py for:
1. Race conditions in transaction processing
2. Proper error handling and rollback
3. Security issues with payment data
4. Edge cases that could cause data loss
Provide specific line numbers and severity ratings."

Comprehensive Review

bash

# Security audit
codex exec -m gpt-5.2-codex --sandbox read-only --config model_reasoning_effort=high \
  "Perform security audit of src/auth. Check for:
  - Authentication/authorization issues
  - Input validation vulnerabilities
  - Cryptographic weaknesses
  - Sensitive data exposure"

# Performance review
codex exec -m gpt-5.2-codex --sandbox read-only \
  "Analyze src/database for performance:
  - N+1 query problems
  - Missing indexes
  - Blocking operations"

Pull Request Review

bash

codex exec -m gpt-5.2-codex --sandbox read-only \
  "Run 'git diff main...HEAD' to see changes.
  Review for:
  1. Breaking changes
  2. Performance implications
  3. Test coverage
  4. Security concerns
  Provide feedback by file with severity levels."

Prompt Design

Structure Formula

code

[Verb] + [Scope] + [Requirements] + [Output Format] + [Constraints]

Verb Selection

Read-only	Write
analyze, review, find, explain	fix, refactor, implement, add

Examples

Bad vs Good:

bash

# Bad: vague
codex exec "review code"

# Good: specific
codex exec -m gpt-5.2-codex --sandbox read-only \
  "Review src/auth for SQL injection, XSS.
  Output: markdown with severity levels.
  Format: file:line, description, fix suggestion."

Parallel Prompt Consistency

bash

# Consistent structure for aggregation
FORMAT="Output JSON: {category, items: [{file, line, description}]}"

codex exec -m gpt-5.2-codex "review security. $FORMAT" &
codex exec -m gpt-5.2-codex "review performance. $FORMAT" &
codex exec -m gpt-5.2-codex "review quality. $FORMAT" &
wait

Claude-Codex Engineering Loop

Dual-AI Workflow

•Claude plans → Architecture, requirements
•Codex validates plan → Check logic, edge cases
•Claude implements → Write code with tools
•Codex reviews → Bug detection, security
•Claude fixes → Apply corrections
•Codex re-validates → Confirm quality
•Repeat until standards met

Implementation

bash

# Phase 2: Codex validates Claude's plan
echo "Review this implementation plan for issues:
[Claude's plan here]

Check for:
- Logic errors
- Missing edge cases
- Architecture flaws
- Security concerns" | codex exec -m gpt-5.2-codex --sandbox read-only

# Phase 4: Codex reviews Claude's code
codex exec -m gpt-5.2-codex --sandbox read-only \
  "Review implementation in src/feature for:
  - Bugs
  - Performance issues
  - Best practices
  - Security vulnerabilities"

Error Handling

•Non-zero exit: Stop and report, ask for direction
•Warnings: Summarize and ask how to proceed
•High-impact flags: Ask permission before --full-auto, --sandbox danger-full-access

Post-Task Follow-up

After every Codex command:

•Summarize outcome
•Confirm next steps with user
•Offer: "Resume session with 'codex resume' for continued analysis"

Configuration

Profile Setup (~/.codex/config.toml)

toml

model = "gpt-5.2-codex"
model_reasoning_effort = "medium"

[profiles.review]
model = "gpt-5.2-codex"
model_reasoning_effort = "high"
sandbox_mode = "read-only"

[profiles.implement]
model = "gpt-5.2-codex"
model_reasoning_effort = "high"
sandbox_mode = "workspace-write"

Usage:

bash

codex exec --profile review "analyze code"

Quick Reference

Use Case	Command
Analysis	`codex exec -m gpt-5.2-codex "prompt" 2>/dev/null`
Edit files	`codex exec -m gpt-5.2-codex --full-auto "prompt" 2>/dev/null`
High reasoning	`--config model_reasoning_effort=high`
Resume last	`echo "prompt" \| codex exec resume --last`
JSON output	`codex exec --json "prompt" > out.jsonl`
Specific dir	`codex exec -C /path "prompt"`
Non-git dir	`--skip-git-repo-check`

Documentation Access via Context7 MCP

Both Claude and Codex have Context7 MCP configured. Use it to access OpenAI documentation:

Available Documentation Libraries

Library ID	Content	Snippets
`/websites/developers_openai_codex`	Codex CLI docs	614
`/websites/platform_openai`	OpenAI API docs	9,418
`/openai/openai-python`	Python SDK	429
`/openai/openai-node`	Node.js SDK	437

Query Documentation Before Execution

yaml

# Before running complex Codex commands, verify syntax
mcp__context7__query-docs:
  libraryId: "/websites/developers_openai_codex"
  query: "exec sandbox modes full-auto workspace-write"

# On errors, look up solutions
mcp__context7__query-docs:
  libraryId: "/websites/developers_openai_codex"
  query: "error troubleshooting session resume"

MCP Server Configuration Reference

toml

# ~/.codex/config.toml - Codex MCP configuration

# STDIO server (local command)
[mcp_servers.context7]
command = "npx"
args = ["-y", "@upstash/context7-mcp@latest"]

# Remote HTTP server
[mcp_servers.remote]
url = "https://example.com/mcp"
bearer_token_env_var = "API_TOKEN"

# With environment variables
[mcp_servers.server.env]
API_KEY = "value"

Verify MCP Servers

bash

# List configured MCP servers
codex mcp list

# Add new MCP server
codex mcp add context7 -- npx -y @upstash/context7-mcp

# Test MCP server
npx @modelcontextprotocol/inspector codex mcp-server

The Vision

Your Work, Step by Step

Ultrathink Principles in Practice

Codex CLI Integration Skill (v2.37)

When to Use This Skill

Quick Start

Prerequisites

Model Selection

Sandbox Modes

Core Commands

Basic Execution

Suppress Thinking Tokens

Session Resume

JSON Output

Orchestration Patterns

Pattern 1: Context Pre-injection

Pattern 2: Session Reuse

Pattern 3: Parallel Execution

Interactive Workflow

Decision Matrix

Code Review Workflow

Independent Review

Comprehensive Review

Pull Request Review

Prompt Design

Structure Formula

Verb Selection

Examples

Parallel Prompt Consistency

Claude-Codex Engineering Loop

Dual-AI Workflow

Implementation

Error Handling

Post-Task Follow-up

Configuration

Profile Setup (~/.codex/config.toml)

Quick Reference

Documentation Access via Context7 MCP

Available Documentation Libraries

Query Documentation Before Execution

MCP Server Configuration Reference

Verify MCP Servers

See Also