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This skill should be used when the user asks to "clarify requirements", "refine requirements", "specify requirements", "what do I mean", "make this clearer", or when the user's request is ambiguous and needs iterative questioning to become actionable. Also trigger when user says "clarify", "/clarify", or mentions unclear/vague requirements.

Install

mkdir -p .claude/skills/clarify && curl -L -o skill.zip "https://mcp.directory/api/skills/download/2849" && unzip -o skill.zip -d .claude/skills/clarify && rm skill.zip

Installs to .claude/skills/clarify

About clarify

clarify is a specialized agent skill created by team-attention that extends AI coding assistants with enhanced capabilities. Agent skills provide context, workflows, and specialized knowledge that help AI assistants perform specific tasks more effectively than general-purpose AI alone.

This skill should be used when the user asks to "clarify requirements", "refine requirements", "specify requirements", "what do I mean", "make this clearer", or when the user's request is ambiguous and needs iterative questioning to become actionable. Also trigger when user says "clarify", "/clarify", or mentions unclear/vague requirements.

How to use clarify

Once installed, clarify becomes available in your AI coding environment automatically. Your AI assistant will use the skill's instructions and knowledge whenever relevant tasks arise in your workflow. You can install it at the project level (available only in a specific project) or globally (available across all your projects).

Use the install panel on this page to copy a one-line command for your preferred AI client. The skill files are downloaded and placed in the appropriate directory — no additional configuration is required.

Compatible AI clients

clarify works with multiple AI coding assistants including Cursor, Claude Code, Codex, Copilot, and other agents that support the skills format. Each client stores skills in a slightly different directory, but the installation command handles this automatically.

What are agent skills?

Agent skills are reusable instruction sets that give AI coding assistants new capabilities. Unlike MCP servers that provide tools and API connections, skills provide context, workflows, and domain-specific knowledge. Think of skills as specialized training for your AI assistant — they help it understand particular frameworks, coding patterns, or development workflows.

Browse more skills in the skills directory or check out the leaderboard to see the most popular skills.

About the author

team-attention has published 7 skills on MCP.Directory. Browse their full catalog to discover complementary skills for your workflow.

tech-decision

team-attention

This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

00

kakaotalk

team-attention

This skill should be used when the user asks to "카톡 보내줘", "카카오톡 메시지", "KakaoTalk message", "채팅 읽어줘", "~에게 메시지 보내줘", or needs to send/read messages via KakaoTalk on macOS.

00

gmail

team-attention

This skill should be used when the user asks to "check email", "read emails", "send email", "reply to email", "search inbox", or manages Gmail. Supports multi-account Gmail integration for reading, searching, sending, and label management.

40

session-analyzer

team-attention

This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

00

google-calendar

team-attention

Google 캘린더 일정 조회/생성/수정/삭제. "오늘 일정", "이번 주 일정", "미팅 추가해줘" 요청에 사용. 여러 계정(work, personal) 통합 조회 지원.

00

session-wrap

team-attention

This skill should be used when the user asks to "wrap up session", "end session", "session wrap", "/wrap", "document learnings", "what should I commit", or wants to analyze completed work before ending a coding session.

00

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