role-creator

am-will/codex-skills · updated Apr 8, 2026

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$npx skills add https://github.com/am-will/codex-skills --skill role-creator
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summary

Create and install custom Codex agent roles with guided configuration and validation.

  • Collects required inputs (model, reasoning effort, developer instructions, role name, install scope) before writing any files, with strict validation against the config schema
  • Writes minimal role config files by default, adding optional parameters like sandboxing, web search controls, and MCP servers only when explicitly requested
  • Installs roles into global ( ~/.codex/config.toml ) or project-scoped
skill.md

Role Creator

Overview

Use this skill to author, update, or troubleshoot custom Codex agents as standalone TOML files.

Current behavior:

  • Each custom agent is defined by one file:
    • Global: ~/.codex/agents/<agent-name>.toml
    • Project: <project>/.codex/agents/<agent-name>.toml
  • The agent file is the role source of truth.
  • ~/.codex/config.toml is only for global/runtime settings (for example [agents] thread limits), not per-role registration.

Non-Negotiable Inputs

Step 1 is required before writing files:

  • name (role identifier used by agent_type)
  • description (short, human-readable purpose)
  • developer_instructions
  • model (recommend gpt-5.3-codex unless requested)
  • model_reasoning_effort (none|minimal|low|medium|high|xhigh)
  • role scope (global or project)
  • output TOML path (canonical absolute path preferred)
  • whether to include nickname_candidates and exact values

Execution rule:

  • Do not infer required values.
  • Do not write until required inputs are explicitly confirmed.

Role file contract (current)

From Codex custom agent docs (/codex/subagents):

  • Required keys: name, description, developer_instructions.
  • Optional keys: nickname_candidates, model, model_reasoning_effort, sandbox_mode, web_search, mcp_servers, skills.config, etc.
  • name is the spawn identifier and source of truth.
  • description + developer_instructions define behavior and usage boundaries.
  • nickname_candidates is optional and used only for display.

nickname_candidates requirements:

  • Must be a non-empty list of unique values when present.
  • Allowed characters: ASCII letters, digits, spaces, hyphens, underscores.

Default policy for optional settings

  • Do not add sandboxing/MCP/web-search/model extras unless requested.
  • Keep generated files minimal by default.
  • If the user says "inherit defaults", omit optional keys instead of setting explicit values.

Workflow

  1. Collect and confirm required inputs.
  2. Resolve output path:
    • global~/.codex/agents/<name>.toml
    • project<project>/.codex/agents/<name>.toml
  3. Create or update the file directly with required keys.
  4. Validate TOML parse and required keys.
  5. Return a ready-to-run example call:
{"agent_type":"<name>","message":"<task>"}

Commands

# 1) Write a standalone custom-agent file
/home/willr/Applications/skills/skills/role-creator/scripts/write_role_config.sh \
  --output ~/.codex/agents/reviewer.toml \
  --role-name reviewer \
  --description "PR reviewer focused on correctness, security, and risk." \
  --model gpt-5.4 \
  --reasoning high \
  --developer-instructions "Review code like an owner. Lead with concrete findings and residual risks."

# Optional: include nickname candidates for display
/home/willr/Applications/skills/skills/role-creator/scripts/write_role_config.sh \
  --output ~/.codex/agents/reviewer.toml \
  --role-name reviewer \
  --description "PR reviewer focused on correctness, security, and risk." \
  --model gpt-5.4 \
  --reasoning high \
  --developer-instructions "Review code like an owner. Lead with concrete findings and residual risks." \
  --nickname-candidates "Atlas,Delta,Echo" \
  --sandbox-mode read-only \
  --web-search disabled

Guardrails

  • If runtime returns unknown agent_type, verify the active scope and confirm the file exists at the expected path.
  • Check syntax first with tomlq or tomlq -C.
  • Keep role instructions operational and narrowly scoped to avoid drift.

References

  • Codex subagents docs: https://developers.openai.com/codex/subagents
  • Display nicknames: https://developers.openai.com/codex/subagents#display-nicknames
  • Reusable templates: templates/
how to use role-creator

How to use role-creator 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 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 role-creator
2

Execute installation command

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

$npx skills add https://github.com/am-will/codex-skills --skill role-creator

The skills CLI fetches role-creator from GitHub repository am-will/codex-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/role-creator

Reload or restart Cursor to activate role-creator. Access the skill through slash commands (e.g., /role-creator) 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

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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

Installation Steps

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

  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

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.469 reviews
  • Aditi Taylor· Dec 24, 2024

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

  • Alexander Abebe· Dec 24, 2024

    Registry listing for role-creator matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aditi Khanna· Dec 20, 2024

    role-creator reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aditi Malhotra· Dec 16, 2024

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

  • Carlos Gupta· Dec 8, 2024

    Useful defaults in role-creator — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Mei Okafor· Nov 27, 2024

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

  • Rahul Santra· Nov 19, 2024

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

  • Carlos Ramirez· Nov 15, 2024

    role-creator reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aditi Patel· Nov 11, 2024

    Registry listing for role-creator matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Charlotte Gonzalez· Nov 11, 2024

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

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