oracle-codex
Use OpenAI Codex CLI as a read-only oracle — planning, review, and analysis only. Codex provides its perspective; you synthesize and present results to the user.
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Installation Guide
How to use oracle-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 machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
oracle-codex
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches oracle-codex from paulrberg/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate oracle-codex. Access via /oracle-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.
Documentation
Codex Oracle
Use OpenAI Codex CLI as a read-only oracle — planning, review, and analysis only. Codex provides its perspective; you synthesize and present results to the user.
Sandbox is always read-only. Codex must never implement changes.
Arguments
Parse $ARGUMENTS for:
- query — the main question or task (everything not a flag). Required — if empty, tell the user to provide a query and stop.
--reasoning <level>— override reasoning effort (low,medium,high,xhigh). Optional; default is auto-selected based on complexity.
Prerequisites
Run the check script before any Codex invocation:
scripts/check-codex.sh
If it exits non-zero, display the error and stop. Use the wrapper for all codex exec calls:
scripts/run-codex-exec.sh
Configuration
| Setting | Default | Override |
|---|---|---|
| Model | gpt-5.3-codex |
Allowlist only (see references/codex-flags.md) |
| Reasoning | Auto | --reasoning <level> or user prose |
| Sandbox | read-only |
Not overridable |
Reasoning Effort
| Complexity | Effort | Timeout | Criteria |
|---|---|---|---|
| Simple | low |
300000ms | <3 files, quick question |
| Moderate | medium |
300000ms | 3–10 files, focused analysis |
| Complex | high |
600000ms | Multi-module, architectural thinking |
| Maximum | xhigh |
600000ms | Full codebase, critical decisions |
For xhigh tasks that may exceed 10 minutes, use run_in_background: true on the Bash tool and set CODEX_OUTPUT so you can read the output later.
See references/codex-flags.md for full flag documentation.
Workflow
1. Parse and Validate
- Parse
$ARGUMENTSfor query and--reasoning - Run
scripts/check-codex.sh— abort on failure - Assess complexity to select reasoning effort (unless overridden)
2. Construct Prompt
Build a focused prompt from the user's query and any relevant context (diffs, file contents, prior conversation). Keep it direct — state what you want Codex to analyze and what kind of output you need. Do not implement; request analysis and recommendations only.
3. Execute
Invoke via the wrapper with HEREDOC. Set the Bash tool timeout per the reasoning effort table above.
EFFORT="<effort>" \
CODEX_OUTPUT="/tmp/codex-${RANDOM}${RANDOM}.txt" \
scripts/run-codex-exec.sh <<'EOF'
[constructed prompt]
EOF
For xhigh, consider run_in_background: true on the Bash tool call, then read CODEX_OUTPUT when done.
4. Present Results
Read the output file and present with attribution:
## Codex Analysis
[Codex output — summarize if >200 lines]
---
Model: gpt-5.3-codex | Reasoning: [effort level]
Synthesize key insights and actionable items for the user.
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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 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
- 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
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Reviews
- MMaya Brown★★★★★Dec 28, 2024
Useful defaults in oracle-codex — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- NNaina Menon★★★★★Dec 28, 2024
Registry listing for oracle-codex matched our evaluation — installs cleanly and behaves as described in the markdown.
- MMichael Tandon★★★★★Dec 24, 2024
oracle-codex has been reliable in day-to-day use. Documentation quality is above average for community skills.
- TTariq Malhotra★★★★★Dec 16, 2024
oracle-codex has been reliable in day-to-day use. Documentation quality is above average for community skills.
- NNaina Patel★★★★★Dec 16, 2024
I recommend oracle-codex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- MMichael Smith★★★★★Dec 12, 2024
Registry listing for oracle-codex matched our evaluation — installs cleanly and behaves as described in the markdown.
- MMia Anderson★★★★★Dec 12, 2024
Keeps context tight: oracle-codex is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDev Sethi★★★★★Dec 12, 2024
oracle-codex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- HHiroshi White★★★★★Nov 19, 2024
We added oracle-codex from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMeera Malhotra★★★★★Nov 19, 2024
oracle-codex reduced setup friction for our internal harness; good balance of opinion and flexibility.
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