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codex

AI-powered code analysis, refactoring, and automated editing via Codex CLI with GPT-5.2.

softaworks/agent-toolkitUpdated Apr 8, 2026

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Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/softaworks/agent-toolkit --skill codex

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What it does

  • Runs codex exec and codex resume commands with configurable reasoning effort ( xhigh , high , medium , low ) and sandbox modes (read-only, workspace-write, danger-full-access)

  • Defaults to GPT-5.2 model (76.3% SWE-bench performance); supports gpt-5.2-max, gpt-5.2-mini, and gpt-5.1-thinking for different complexity and cost trade-offs

  • Session continuity: resume prior Codex work at any time using co

Category

Productivity

Last updated

Apr 8, 2026

Installation Guide

How to use codex on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your machine
  • ›Node.js 16+ with npm — verify with node --version
  • ›Active project directory where you want to add codex
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/softaworks/agent-toolkit --skill codex

Fetches codex from softaworks/agent-toolkit and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/codex

Restart Cursor to activate codex. Access via /codex in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub›Skills CLI docs›About Cursor›What are agent skills?

Documentation

Codex Skill Guide

Running a Task

  1. Default to gpt-5.2 model. Ask the user (via AskUserQuestion) which reasoning effort to use (xhigh,high, medium, or low). User can override model if needed (see Model Options below).
  2. Select the sandbox mode required for the task; default to --sandbox read-only unless edits or network access are necessary.
  3. Assemble the command with the appropriate options:
    • -m, --model <MODEL>
    • --config model_reasoning_effort="<high|medium|low>"
    • --sandbox <read-only|workspace-write|danger-full-access>
    • --full-auto
    • -C, --cd <DIR>
    • --skip-git-repo-check
  4. Always use --skip-git-repo-check.
  5. When continuing a previous session, use codex exec --skip-git-repo-check resume --last via stdin. When resuming don't use any configuration flags unless explicitly requested by the user e.g. if he species the model or the reasoning effort when requesting to resume a session. Resume syntax: echo "your prompt here" | codex exec --skip-git-repo-check resume --last 2>/dev/null. All flags have to be inserted between exec and resume.
  6. IMPORTANT: By default, append 2>/dev/null to all codex exec commands to suppress thinking tokens (stderr). Only show stderr if the user explicitly requests to see thinking tokens or if debugging is needed.
  7. Run the command, capture stdout/stderr (filtered as appropriate), and summarize the outcome for the user.
  8. After Codex completes, inform the user: "You can resume this Codex session at any time by saying 'codex resume' or asking me to continue with additional analysis or changes."

Quick Reference

Use case Sandbox mode Key flags
Read-only review or analysis read-only --sandbox read-only 2>/dev/null
Apply local edits workspace-write --sandbox workspace-write --full-auto 2>/dev/null
Permit network or broad access danger-full-access --sandbox danger-full-access --full-auto 2>/dev/null
Resume recent session Inherited from original echo "prompt" | codex exec --skip-git-repo-check resume --last 2>/dev/null (no flags allowed)
Run from another directory Match task needs -C <DIR> plus other flags 2>/dev/null

Model Options

Model Best for Context window Key features
gpt-5.2-max Max model: Ultra-complex reasoning, deep problem analysis 400K input / 128K output 76.3% SWE-bench, adaptive reasoning, $1.25/$10.00
gpt-5.2 ⭐ Flagship model: Software engineering, agentic coding workflows 400K input / 128K output 76.3% SWE-bench, adaptive reasoning, $1.25/$10.00
gpt-5.2-mini Cost-efficient coding (4x more usage allowance) 400K input / 128K output Near SOTA performance, $0.25/$2.00
gpt-5.1-thinking Ultra-complex reasoning, deep problem analysis 400K input / 128K output Adaptive thinking depth, runs 2x slower on hardest tasks

GPT-5.2 Advantages: 76.3% SWE-bench (vs 72.8% GPT-5), 30% faster on average tasks, better tool handling, reduced hallucinations, improved code quality. Knowledge cutoff: September 30, 2024.

Reasoning Effort Levels:

  • xhigh - Ultra-complex tasks (deep problem analysis, complex reasoning, deep understanding of the problem)
  • high - Complex tasks (refactoring, architecture, security analysis, performance optimization)
  • medium - Standard tasks (refactoring, code organization, feature additions, bug fixes)
  • low - Simple tasks (quick fixes, simple changes, code formatting, documentation)

Cached Input Discount: 90% off ($0.125/M tokens) for repeated context, cache lasts up to 24 hours.

Following Up

  • After every codex command, immediately use AskUserQuestion to confirm next steps, collect clarifications, or decide whether to resume with codex exec resume --last.
  • When resuming, pipe the new prompt via stdin: echo "new prompt" | codex exec resume --last 2>/dev/null. The resumed session automatically uses the same model, reasoning effort, and sandbox mode from the original session.
  • Restate the chosen model, reasoning effort, and sandbox mode when proposing follow-up actions.

Error Handling

  • Stop and report failures whenever codex --version or a codex exec command exits non-zero; request direction before retrying.
  • Before you use high-impact flags (--full-auto, --sandbox danger-full-access, --skip-git-repo-check) ask the user for permission using AskUserQuestion unless it was already given.
  • When output includes warnings or partial results, summarize them and ask how to adjust using AskUserQuestion.

CLI Version

Requires Codex CLI v0.57.0 or later for GPT-5.2 model support. The CLI defaults to gpt-5.2 on macOS/Linux and gpt-5.2 on Windows. Check version: codex --version

Use /model slash command within a Codex session to switch models, or configure default in ~/.codex/config.toml.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

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Reviews

4.4★★★★★34 reviews
  • D
    Dhruvi Jain★★★★★Dec 8, 2024

    I recommend codex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • H
    Hiroshi Haddad★★★★★Dec 8, 2024

    Solid pick for teams standardizing on skills: codex is focused, and the summary matches what you get after install.

  • I
    Isabella Patel★★★★★Dec 4, 2024

    codex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • O
    Oshnikdeep★★★★★Nov 27, 2024

    Solid pick for teams standardizing on skills: codex is focused, and the summary matches what you get after install.

  • I
    Isabella Sethi★★★★★Nov 27, 2024

    I recommend codex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • A
    Aarav Gupta★★★★★Nov 23, 2024

    We added codex from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • G
    Ganesh Mohane★★★★★Oct 18, 2024

    codex is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • S
    Soo Wang★★★★★Oct 18, 2024

    Keeps context tight: codex is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • M
    Mia Torres★★★★★Oct 14, 2024

    codex reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • S
    Sakshi Patil★★★★★Sep 25, 2024

    codex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

showing 1-10 of 34

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