codex▌
softaworks/agent-toolkit · updated Apr 8, 2026
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AI-powered code analysis, refactoring, and automated editing via Codex CLI with GPT-5.2.
- ›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
Codex Skill Guide
Running a Task
- Default to
gpt-5.2model. Ask the user (viaAskUserQuestion) which reasoning effort to use (xhigh,high,medium, orlow). User can override model if needed (see Model Options below). - Select the sandbox mode required for the task; default to
--sandbox read-onlyunless edits or network access are necessary. - 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
- Always use --skip-git-repo-check.
- When continuing a previous session, use
codex exec --skip-git-repo-check resume --lastvia 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. - IMPORTANT: By default, append
2>/dev/nullto allcodex execcommands to suppress thinking tokens (stderr). Only show stderr if the user explicitly requests to see thinking tokens or if debugging is needed. - Run the command, capture stdout/stderr (filtered as appropriate), and summarize the outcome for the user.
- 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
codexcommand, immediately useAskUserQuestionto confirm next steps, collect clarifications, or decide whether to resume withcodex 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 --versionor acodex execcommand 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.
How to use codex on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add codex
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches codex from GitHub repository softaworks/agent-toolkit and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate codex. Access the skill through slash commands (e.g., /codex) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.4★★★★★34 reviews- ★★★★★Dhruvi Jain· Dec 8, 2024
I recommend codex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Hiroshi Haddad· Dec 8, 2024
Solid pick for teams standardizing on skills: codex is focused, and the summary matches what you get after install.
- ★★★★★Isabella Patel· Dec 4, 2024
codex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Oshnikdeep· Nov 27, 2024
Solid pick for teams standardizing on skills: codex is focused, and the summary matches what you get after install.
- ★★★★★Isabella Sethi· Nov 27, 2024
I recommend codex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aarav Gupta· Nov 23, 2024
We added codex from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Oct 18, 2024
codex is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★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.
- ★★★★★Mia Torres· Oct 14, 2024
codex reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakshi Patil· Sep 25, 2024
codex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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