scs-mcp

# SCS — Skill Compiler System **[中文版](README_CN.md)** > **Turn scattered skills into reliable workflows** You've collected many skill files. But when it's time to actually use them: - *"Which ones do I need for this task?"* - *"Can they chain together? Or will it break halfway?"* - *"Why do I have to manually piece them together, with no way to know if it will work?"* **The core problem**: Skills are defined separately, but they need to work together. Nobody builds the bridges. --- ## Core Concepts Before diving in, understand these 3 key concepts: ### What is a Skill? A **Skill** is a reusable capability definition. Think of it as a "function" in programming: ``` Skill: "Write PRD" - Input: User requirements (raw text) - Output: PRD document (structured) - Description: How to transform user input into a formal PRD ``` Each skill is a `.md` file in your agent's skills directory, defining what it does, what it needs, and what it produces. ### What is an Artifact? An **Artifact** is the "data" that flows between skills — the input and output of each step. ``` [User Requirements] → Skill: Write PRD → [PRD Document] → Skill: Review PRD → [Validated PRD] ``` Artifacts have **types** (like PRDDocument, SourceCode, TestReport). SCS uses these types to match skills together — if skill A outputs `PRDDocument` and skill B takes `PRDDocument` as input, they can connect. **Think of artifacts as typed variables that skills pass around.** ### What is an Execution Plan (DAG)? An **Execution Plan** is a verified, step-by-step workflow. SCS generates these by: 1. Finding skills that can chain together (matching artifact types) 2. Ordering them correctly (inputs ready before outputs needed) 3. Checking feasibility (no circular dependencies, no missing inputs) 4. Showing you the full path before execution **It's like Terraform Plan — preview with validation, before you commit.** --- ## Supported AI Agents SCS works with these AI coding assistants: | Age…