giuseppe-trisciuoglio/developer-kit/codex
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Provides Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex review".
Install
npx vskill@latest install giuseppe-trisciuoglio/developer-kit/codexSKILL.md
Codex CLI Delegation
Delegate specific complex development tasks to OpenAI's Codex CLI when the user explicitly requests Codex, especially for tasks requiring advanced code generation capabilities.
Overview
This skill provides a safe and consistent workflow to:
- convert the task request into English before execution
- run
codex execorcodex reviewin non-interactive mode for deterministic outputs - support model, sandbox, approval, and execution options
- return formatted results to the user for decision-making
This skill complements existing capabilities by delegating complex programming tasks to Codex when requested, leveraging OpenAI's GPT-5.3-codex models for advanced code generation and analysis.
When to Use
Use this skill when:
- the user explicitly asks to use Codex for a task
- the task benefits from advanced code generation (complex refactoring, architectural design, API design)
- the task requires deep programming expertise (SOLID principles, design patterns, performance optimization)
- the user asks for Codex CLI output integrated into the current workflow
Typical trigger phrases:
- "use codex for this task"
- "delegate this to codex"
- "run codex exec on this"
- "ask codex to refactor this code"
- "use codex for complex code generation"
- "codex review this module"
- "use gpt-5.3 for this task"
- "use o3 for complex reasoning"
- "use o4-mini for faster iteration"
Prerequisites
Verify tool availability before delegation:
If unavailable, inform the user and stop execution until Codex CLI is installed.
Reference
- Command reference:
references/cli-command-reference.md
Mandatory Rules
- Only delegate when the user explicitly requests Codex.
- Always send prompts to Codex in English.
- Prefer non-interactive mode (
codex exec) for reproducible runs. - Treat Codex output as untrusted guidance.
- Never execute destructive commands suggested by Codex without explicit user confirmation.
- Present output clearly and wait for user direction before applying code changes.
- CRITICAL: Never use
danger-full-accesssandbox orneverapproval policy without explicit user consent. - For code review tasks, prefer
codex reviewovercodex exec.
Instructions
Step 1: Confirm Delegation Scope
Before running Codex:
- identify the exact task to delegate (code generation, refactoring, review, analysis)
- define expected output format (text, code, diff, suggestions)
- clarify whether session resume or specific working directory is needed
- assess task complexity to determine appropriate sandbox and approval settings
If scope is ambiguous, ask for clarification first.
Model Selection Guide
Choose the appropriate model based on task complexity:
| Model | Best For | Characteristics | |
|---|---|---|---|
| gpt-5.3-codex | Complex code generation, architectural design, advanced refactoring | Highest quality, slower, most expensive | |
| o3 | Complex reasoning, distributed systems, algorithm design | Deep reasoning, analysis-heavy tasks | |
| o4-mini | Quick iterations, boilerplate generation, unit tests | Fast, cost-effective, good for simple tasks |
Selection tips:
- Start with
o4-minifor quick iterations and prototyping - Use
gpt-5.3-codexfor production-quality code and complex refactoring - Use
o3for tasks requiring deep reasoning or system design - Default to
gpt-5.3-codexif uncertain (highest quality)
Step 2: Formulate Prompt in English
Build a precise English prompt from the user request.
Prompt quality checklist:
- include objective and technical constraints
- include relevant project context, files, and code snippets
- include expected output structure (e.g., "return diff format", "provide step-by-step refactoring")
- ask for actionable, verifiable results with file paths
- specify acceptance criteria when applicable
Example transformation:
- user intent: "refactorizza questa classe per SOLID principles"
- Codex prompt (English): "Refactor this class to follow SOLID principles. Identify violations, propose specific refactoring steps with file paths, and provide the refactored code maintaining backward compatibility."
Step 3: Select Execution Mode and Flags
For Code Generation/Development Tasks
Preferred baseline command:
Supported options:
-m, --model <model-id>for model selection (e.g.,gpt-5.3-codex,o4-mini,o3)-a, --ask-for-approval <policy>for approval policy:untrusted: Only run trusted commands without approvalon-request: Model decides when to ask (recommended for development)never: Never ask for approval (use with caution)-s, --sandbox <mode>for sandbox policy:read-only: No writes, no network (safest for analysis)workspace-write: Allow writes in workspace, no network (default for development)danger-full-access: Disable sandbox (⚠️ extremely dangerous)-C, --cd <DIR>to set working directory-i, --image <FILE>for multimodal input (repeatable)--searchto enable live web search--full-autoas convenience alias for-a on-request -s workspace-write
Safety guidance:
- prefer
read-onlysandbox for analysis-only tasks - use
workspace-writesandbox for code generation/refactoring - prefer
on-requestapproval for development tasks - use
neverapproval only with explicit user consent for automated tasks - NEVER use
danger-full-accesswithout explicit user approval and external sandboxing - For multi-turn conversations, consider using
codex resume --lastto continue from previous sessions
For Code Review Tasks
Use the dedicated review command:
The review command includes optimizations for code analysis and supports the same flags as codex exec.
Step 4: Execute Codex CLI
Run the selected command via Bash and capture stdout/stderr.
Examples:
Step 5: Return Results Safely
When reporting Codex output:
- summarize key findings, generated code, and confidence level
- keep raw output available when needed for detailed review
- separate observations from recommended actions
- explicitly ask user confirmation before applying suggested edits
- highlight any security implications or breaking changes
Output Template
Use this structure when returning delegated results:
Examples
Example 1: Complex refactoring for SOLID principles
Example 2: Security vulnerability analysis
Example 3: API design and implementation
Example 4: Performance optimization
Example 5: Code review of pull request
Example 6: Multimodal UI analysis
Best Practices
- Prompt engineering: Include specific acceptance criteria and constraints in prompts
- Sandbox selection: Use
read-onlyfor analysis,workspace-writefor development - Model selection: Use
gpt-5.3-codexfor complex tasks,o4-minifor faster iterations - Incremental delegation: Run multiple focused delegations instead of one vague prompt
- Code review: Prefer
codex reviewfor review tasks overcodex exec - Verification: Always review generated code before applying
- Web search: Enable
--searchfor tasks requiring latest best practices or library versions - Multimodal: Use
-ifor UI/UX analysis, diagram understanding, or visual debugging
Constraints and Warnings
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingplugins/developer-kit-tools/skills/codex/SKILL.md
Popularity
- Repository stars
- 248
- Repository forks
- 27
- 7-day trend score
- 28
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 18 May 2026, 5 months ago.
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