A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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Create and update Codex custom agents using standalone custom-agent TOML files.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill to author, update, or troubleshoot custom Codex agents as standalone TOML files.
Current behavior:
~/.codex/agents/<agent-name>.toml<project>/.codex/agents/<agent-name>.toml~/.codex/config.toml is only for global/runtime settings (for example [agents] thread limits), not per-role registration.Step 1 is required before writing files:
name (role identifier used by agent_type)description (short, human-readable purpose)developer_instructionsmodel (recommend gpt-5.3-codex unless requested)model_reasoning_effort (none|minimal|low|medium|high|xhigh)global or project)nickname_candidates and exact valuesExecution rule:
From Codex custom agent docs (/codex/subagents):
name, description, developer_instructions.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:
global → ~/.codex/agents/<name>.tomlproject → <project>/.codex/agents/<name>.toml{"agent_type":"<name>","message":"<task>"}
# 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
unknown agent_type, verify the active scope and confirm the file exists at the expected path.tomlq or tomlq -C.https://developers.openai.com/codex/subagentshttps://developers.openai.com/codex/subagents#display-nicknamestemplates/아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.
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Create or update Codex hooks for global or project scope by asking for scope and hook type when ambiguous, then writing the hook config, scripts, and any required feature flag config.
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Build responsive, mobile-first layouts using fluid containers, flexible units, media queries, and touch-friendly design that works across all screen sizes. Use this skill when creating or modifying UI layouts, responsive grids, breakpoint styles, mobile navigation, or any interface that needs to adapt to different screen sizes. Apply when working with responsive CSS, media queries, viewport settings, flexbox/grid layouts, mobile-first styling, breakpoint definitions (mobile, tablet, desktop), touch target sizing, relative units (rem, em, %), image optimization for different screens, or testing layouts across multiple devices. Use for any task involving multi-device support, responsive design patterns, or adaptive layouts.
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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.
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Build and run Gemini 2.5 Computer Use browser-control agents with Playwright. Use when a user wants to automate web browser tasks via the Gemini Computer Use model, needs an agent loop (screenshot → function_call → action → function_response), or asks to integrate safety confirmation for risky UI actions.
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Build, transform, audit, and migrate prompts for GPT-5.6 Sol using outcome-first contracts, explicit success criteria, evidence and tool rules, approval boundaries, output requirements, and stop conditions. Use when someone asks for a GPT-5.6 prompt, wants an existing prompt optimized for GPT-5.6, or needs a prompt stack adapted from an older model.
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