# Scw

> Skill: Semantic Code Weaving (SCW)

- Skill: `duclm1x1/scw` (Agent Skill)
- Install (CLI): `npx skillmds@latest add duclm1x1/scw`
- Raw SKILL.md: https://api.skillmd.com/api/skills/duclm1x1/scw/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: duclm1x1 (https://skillmd.com/u/duclm1x1)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/duclm1x1/scw

---

# Skill: Semantic Code Weaving (SCW)

**Version:** 1.0
**Author:** Manus AI

---

## 1. Description

This skill implements **Semantic Code Weaving (SCW)**, a powerful technique for ensuring that AI-generated code is directly and explicitly tied to user requirements. Instead of treating code generation as a monolithic task, SCW first builds an "intent graph" from the user's prompt. Each node in this graph represents a specific, atomic requirement (a feature, a data model, a UI component, etc.). The AI then "weaves" code around this graph, ensuring that every piece of generated code serves a distinct, traceable purpose.

This skill is a direct implementation of the third of the 10 breakthrough LLM innovations.

### Key Features:

- **Intent-Driven Development:** Shifts the focus from generating code to fulfilling semantic intents.
- **Traceability:** Creates a clear and auditable link between every user requirement and the code that implements it.
- **Topological Sorting:** Can determine the correct order of implementation to respect dependencies between features.
- **Modularity:** Encourages the creation of modular, loosely-coupled code by breaking down requirements into discrete nodes.

## 2. How to Use

### 2.1. Installation

This skill is a self-contained Python module. To use it, import the `SemanticCodeWeaver` class.

```python
from skills.scw.src.scw_engine import SemanticCodeWeaver
```

### 2.2. Building the Intent Graph

Start by instantiating the `SemanticCodeWeaver`. Then, parse the user's prompt and add a node for each distinct requirement.

```python
scw = SemanticCodeWeaver()

# From a prompt like "Create a blog with users and posts"
user_model_id = scw.add_node(
    name="User Data Model",
    description="Represents a user with username and password.",
    node_type="data_model"
)

post_model_id = scw.add_node(
    name="Post Data Model",
    description="Represents a blog post with a title, content, and author.",
    node_type="data_model",
    dependencies=[user_model_id]  # A post must have an author (a user)
)

api_id = scw.add_node(
    name="Blog Post API",
    description="API endpoints for creating, reading, and deleting posts.",
    node_type="feature",
    dependencies=[post_model_id]
)
```

### 2.3. Determining Implementation Order

Before generating code, you can get a valid, dependency-respecting order of implementation.

```python
implementation_plan = scw.get_implementation_order()
print(f"Recommended implementation order: {implementation_plan}")
# Output might be: [NODE_ABC, NODE_DEF, NODE_GHI]
```

### 2.4. Weaving the Code

Iterate through the implementation plan. For each node, generate the corresponding code and then mark the node as implemented.

```python
for node_id in implementation_plan:
    node = scw.get_node(node_id)
    print(f"Implementing {node.name}...")
    # ... (code generation logic for this node) ...
    scw.mark_as_implemented(node_id)
```

### 2.5. Tracking Progress

You can check which parts of the original request are still pending.

```python
unimplemented = scw.get_unimplemented_nodes()
if not unimplemented:
    print("All requirements have been successfully implemented.")
```

## 3. Development Roadmap

SCW is the backbone of intent-driven code generation. Future development will focus on making the graph more intelligent and automated.

- **v1.1: Automated Node Extraction:**
    - **Goal:** Use a dedicated LLM agent to automatically parse a user prompt and generate the initial intent graph, including dependencies.
    - **Timeline:** 3 weeks

- **v1.2: Graph Visualization:**
    - **Goal:** Create a tool to render the intent graph visually. This will provide a clear, high-level overview of the project architecture before any code is written.
    - **Timeline:** 3 weeks

- **v1.3: Code-to-Node Mapping:**
    - **Goal:** Implement a mechanism to store a direct reference to the generated code block(s) within each `IntentNode`. This will create a powerful, bidirectional link between requirements and implementation.
    - **Timeline:** 4 weeks

- **v2.0: Live Graph Refactoring:**
    - **Goal:** Allow the AI to dynamically refactor the intent graph as it discovers new requirements or constraints during the code generation process. This will enable more complex and adaptive project planning.
    - **Timeline:** 8 weeks