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Found 37 Skills
Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
AI 에이전트 협업 개발의 핵심 원칙. 분해정복, 컨텍스트 관리, 추상화 수준 선택, 자동화 철학, 검증 회고를 정의. 모든 AI 에이전트 사용 시 최적의 협업 패턴 적용.
AI-native terminal emulator & IDE built with Tauri, React, and Rust
Generate and use a pre-edit structure brief so coding agents learn likely owners, consumer surfaces, and unsafe edit locations before implementing.
AI voice assistants with custom instructions, knowledge bases, and tool integrations.
Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
Use when the user wants to turn a PRD requirement (FR-N or similar ID) into an executable development plan. Locates the requirement in docs/prd/, investigates the surrounding code, asks clarifying questions, and produces docs/plans/<FR-N>.md as a markdown checkbox list of independent, vertical-slice tasks. Triggers on "make a plan for FR-001", "generate a plan for this requirement", "break this PRD into tasks", "plan the implementation of FR-N", "create a dev plan", "/plan FR-N".
Use when executing multi-task plans where each task can be implemented independently by a subagent. Triggers when a plan has 3+ independent tasks, when speed of execution is important, when tasks have clear acceptance criteria suitable for delegation, or when two-stage review gates (spec compliance and code quality) are needed for iterative fix cycles.
Use when starting a session, deciding which framework skill applies to the current task, or sequencing them across a feature. Maps the user's intent to one of the five framework skills (ai-driven-prd, init-claude-project, generate-dev-plan, declarative-design, execute-plan) and enforces the cross-skill operating behaviors. Triggers on "which skill should I use", "where do I start", "how do these skills fit together", "I have a PRD now what", "/using-agent-skills".
Use when the user asks to "plan this feature", "plan refactor", "research & plan", "plan auth/API/work", or needs multi-step work with evidence-based planning before coding. Understands → Researches (via Local Search/Research) → Plans → Implement. No guessing; validates with code.
Use when starting work on a new or unfamiliar project, when encountering unexpected patterns, when user corrects your assumptions, or when explicitly invoked via /learn - auto-discovers and remembers project context through structured codebase analysis
Capture AI agent sessions in your git workflow. Use for setup, rewinding to checkpoints, exploring session history, and troubleshooting.