logic-master

name	logic-master
description	Logic optimization and image-based code tasks with Codex CLI in --yolo mode. Use when (1) optimizing code logic or algorithms, (2) processing image inputs (screenshots, diagrams, mockups) to generate or modify code, (3) refactoring business logic, (4) debugging complex logic issues, (5) code generation from visual references, or (6) any task requiring image attachment for context.

Codex Logic Skill

Execute logic optimization and image-based code tasks using Codex CLI in --yolo mode.

When to Use

• Logic optimization: algorithm improvements, performance tuning
• Image-based tasks: generate code from screenshots, mockups, diagrams
• Refactoring: extract functions, simplify conditionals, reduce complexity
• Business logic: validation rules, data transformations, state machines
• Debugging: fix complex logic bugs, race conditions

When NOT to Use

• Visual/styling changes (use frontend-master skill instead)
• CSS, layout, colors, typography, animations

Execution Pattern

Basic Command Structure

# Direct prompt
codex exec --yolo "<instruction>"

# With file context via stdin
cat <file> | codex exec --yolo -

# With image attachment
codex exec --yolo --image <path-to-image> "<instruction>"

# With directory context
codex exec --yolo -C <directory> "<instruction>"

Required Flags

Flag	Purpose
--yolo	MANDATORY - Auto-approve and bypass sandbox
--image or -i	Attach image files for visual context
--json	Get JSONL event stream for parsing
-o or --output-last-message	Save final output to file
-C or --cd	Change working directory
--sandbox	Set access level (workspace-write recommended)
-c, --config	Override config value; for this skill, only model_reasoning_effort should be adjusted (예: -c model_reasoning_effort="xhigh")

Model Routing (Performance-oriented)

• **이미지 기반 작업(--image 사용)**은 반드시 gpt-5.3-codex 사용.
• 이미지 미사용 작업은 가벼운 요청에서는 gpt-5.3-codex-spark 사용.
• 복잡/대규모/고난도 작업은 gpt-5.3-codex 사용.
• 모델 자체는 전환하지 않고, 조절은 model_reasoning_effort만 수행.

실무 예시:

codex exec --yolo --image screenshot.png -c model_reasoning_effort=\"xhigh\" "Analyze image context"
codex exec --yolo -c model_reasoning_effort=\"low\" "Refactor a small utility function"
codex exec --yolo -c model_reasoning_effort=\"xhigh\" "Redesign a complex scheduling algorithm"

Workflow

1. Identify task type: logic optimization or image-based
2. Prepare inputs: file content or image path
3. Construct Codex command with --yolo flag
4. Execute via bash tool
5. Capture output and delegate to subagent for implementation
6. Verify results with lsp_diagnostics

Image-Based Tasks

Generate Code from Screenshot

# UI mockup to component
codex exec --yolo --image mockup.png "Generate React component matching this design"

# Diagram to code
codex exec --yolo --image architecture.png "Implement the data flow shown in this diagram"

# Error screenshot to fix
codex exec --yolo --image error-screenshot.png "Analyze this error and provide fix"

Multiple Images

codex exec --yolo --image before.png --image after.png "Generate code to transform UI from before to after state"

Logic Optimization Tasks

Algorithm Optimization

# Optimize function
cat src/utils/sort.ts | codex exec --yolo - "Optimize this sorting algorithm for large datasets"

# Reduce complexity
cat src/services/parser.ts | codex exec --yolo - "Refactor to reduce cyclomatic complexity"

Refactoring

# Extract functions
cat src/handlers/user.ts | codex exec --yolo - "Extract reusable validation logic into separate functions"

# Simplify conditionals
cat src/logic/pricing.ts | codex exec --yolo - "Simplify nested conditionals using early returns"

Performance Tuning

# Memory optimization
cat src/data/processor.ts | codex exec --yolo - "Optimize memory usage, avoid unnecessary allocations"

# Async optimization
codex exec --yolo -C src/services "Identify and fix N+1 query patterns"

Output Handling

JSON Output for Parsing

codex exec --yolo --json "Generate utility function" | jq -r '.content'

Save to File

codex exec --yolo -o result.ts "Generate TypeScript interface from this JSON schema"

Resume Session

# Continue previous work
codex exec resume --last "Apply the suggested changes"

Integration with Subagents

After Codex CLI provides analysis/suggestions, delegate implementation:

1. TASK: Implement logic changes based on Codex analysis
2. EXPECTED OUTCOME: Optimized code matching Codex recommendations
3. REQUIRED SKILLS: logic-master
4. REQUIRED TOOLS: Bash (for codex CLI), Read, Edit, lsp_diagnostics
5. MUST DO:
   - Execute codex exec with --yolo flag first
   - Parse Codex output for implementation steps
   - Apply changes using Edit tool
   - Verify with lsp_diagnostics
   - Run tests if available
6. MUST NOT DO:
   - Modify styling/visual elements
   - Skip verification step
   - Ignore Codex warnings or caveats
7. CONTEXT: [file paths, performance requirements, constraints]

Image-Based Workflow Example

1. TASK: Generate component from design mockup
2. EXPECTED OUTCOME: Working React component matching mockup
3. REQUIRED SKILLS: logic-master
4. REQUIRED TOOLS: Bash (for codex CLI), Write, lsp_diagnostics
5. MUST DO:
   - Use codex exec --yolo --image <path> to analyze mockup
   - Create component based on Codex output
   - Match existing component patterns in codebase
   - Verify types with lsp_diagnostics
6. MUST NOT DO:
   - Generate inline styles (use existing CSS framework)
   - Create new dependencies without approval
7. CONTEXT: [mockup path, component location, existing patterns]

Example Complete Workflow

# Step 1: Analyze image and get code suggestion
codex exec --yolo --image ui-mockup.png "Analyze this mockup and describe the React component structure needed" -o analysis.md

# Step 2: Generate implementation
codex exec --yolo --image ui-mockup.png "Generate React component code for this mockup using Tailwind CSS" -o Component.tsx

# Step 3: Verify and integrate
# (delegate to subagent to place file, add imports, run diagnostics)

Troubleshooting

Issue	Solution
Image not recognized	Use absolute path or ensure file exists
Command hangs	Ensure --yolo flag is present
Sandbox errors	Add --sandbox workspace-write
Large output truncated	Use -o <file> to save full output
모델 품질/속도 조절	과도한 결과 변동이 있으면 model_reasoning_effort (low, medium, high, xhigh) 값만 조정

Security Note

--yolo mode bypasses approval and sandbox restrictions. Use only when:

• Working in version-controlled directories
• Changes can be easily reverted
• No sensitive operations or credentials involved
• Running in isolated development environment