Document Hub: Initialize

Bootstrap a new project with a complete documentation hub structure.

Helper Scripts Available:

•scripts/validate_hub.py - Validates documentation structure
•scripts/extract_glossary.py - Extracts domain-specific terms from code
•scripts/detect_drift.py - Detects existing modules and technologies

Always run scripts with --help or check scripts/README.md first to understand their usage and output format.

What This Skill Does

Creates the Documentation Hub file structure in a project's cline-docs/ directory:

code

project-root/
└── cline-docs/
    ├── systemArchitecture.md      # High-level architecture diagrams
    ├── keyPairResponsibility.md   # Module responsibilities
    ├── glossary.md                # Domain-specific terms
    └── techStack.md               # Technologies used

Decision Tree: When to Use This Skill

code

User wants to set up documentation → Does cline-docs/ exist?
    ├─ Yes → Run validation first
    │         ├─ Valid → Use /document-hub read instead
    │         └─ Invalid → Ask user: overwrite or skip?
    │
    └─ No → Initialize new hub:
        1. Create directory structure
        2. Detect technologies (detect_drift.py)
        3. Extract glossary terms (extract_glossary.py)
        4. Generate initial content
        5. Validate result (validate_hub.py)

Initialization Workflow

Step 1: Check Existing State

First, check if a documentation hub already exists:

bash

# Check if cline-docs exists
ls project-root/cline-docs/

# If exists, validate it first
python scripts/validate_hub.py /path/to/project

If validation returns "valid": false with missing files, proceed with initialization.

Step 2: Gather Project Information

Use helper scripts to analyze the project:

Detect existing technologies and modules:

bash

python scripts/detect_drift.py /path/to/project

This returns JSON with:

•Actual modules found in src/
•Technologies detected from package.json/requirements.txt
•Configuration files present

Extract domain-specific terms:

bash

python scripts/extract_glossary.py /path/to/project

This returns ranked terms with context from code comments.

Step 3: Create Documentation Files

Create the four core files with initial content based on the gathered information.

systemArchitecture.md template:

markdown

# System Architecture

## High-Level Overview

[Brief description of what this project does]

## Architecture Diagram

\`\`\`mermaid
flowchart TD
    Client[Client/User] --> Server[Application Server]
    Server --> DB[Database]
\`\`\`

## Database Schema

[If applicable, add ER diagram or schema description]

## Key Processes

[Describe major workflows]

keyPairResponsibility.md template:

markdown

# Key Modules & Responsibilities

## Project Overview

[What this project does and why]

## Module Breakdown

### [Module Name]
**Location:** `src/module-name/`
**Responsibility:** [What this module handles]
**Key Files:**
- `file1.ts` - [Purpose]
- `file2.ts` - [Purpose]

glossary.md template:

markdown

# Glossary

Domain-specific terms used throughout the codebase.

## A

**[Term]** - [Definition based on code context]

techStack.md template:

markdown

# Technology Stack

## Core Technologies

### [Framework Name]
**Purpose:** [What it's used for]
**Version:** [If detected from package.json]

## Infrastructure

[Databases, caching, etc.]

## Development Tools

[Build tools, testing frameworks, etc.]

Step 4: Populate with Detected Information

Use the JSON output from helper scripts to populate initial content:

•
From detect_drift.py output:
- •Add detected technologies to techStack.md
- •Add detected modules to keyPairResponsibility.md
•
From extract_glossary.py output:
- •Add top 20-30 terms to glossary.md alphabetically
- •Include contexts as definitions

Step 5: Prompt User for Additional Context

After creating initial files, ask the user:

code

Documentation hub initialized with detected information.

Please provide additional context:
1. What is the primary purpose of this project?
2. Are there any key architectural decisions to document?
3. Any specific modules or workflows that need detailed explanation?

I can update the documentation with your input.

Step 6: Validate Result

After initialization, always validate:

bash

python scripts/validate_hub.py /path/to/project

Check that:

•"valid": true
•All required files exist
•No Mermaid syntax errors

Example: Complete Initialization

python

import json
import subprocess
from pathlib import Path

project_path = Path("/path/to/project")
docs_path = project_path / "cline-docs"

# Step 1: Check if exists
if docs_path.exists():
    print("Documentation hub already exists. Validating...")
    result = subprocess.run(
        ["python", "scripts/validate_hub.py", str(project_path)],
        capture_output=True, text=True
    )
    validation = json.loads(result.stdout)
    if validation["valid"]:
        print("Hub is valid. Use /document-hub read to view it.")
        exit(0)

# Step 2: Create directory
docs_path.mkdir(exist_ok=True)

# Step 3: Detect technologies and modules
drift_result = subprocess.run(
    ["python", "scripts/detect_drift.py", str(project_path)],
    capture_output=True, text=True
)
drift_data = json.loads(drift_result.stdout)

# Step 4: Extract glossary terms
glossary_result = subprocess.run(
    ["python", "scripts/extract_glossary.py", str(project_path)],
    capture_output=True, text=True
)
glossary_data = json.loads(glossary_result.stdout)

# Step 5: Generate files
# [Create files using templates + detected data]

# Step 6: Validate
validate_result = subprocess.run(
    ["python", "scripts/validate_hub.py", str(project_path)],
    capture_output=True, text=True
)
validation = json.loads(validate_result.stdout)

if validation["valid"]:
    print("✓ Documentation hub initialized successfully!")
else:
    print("⚠ Issues detected:", validation["errors"])

Best Practices

•Run detection scripts first - Don't guess what's in the project
•Use templates - Ensure consistent structure across projects
•Validate after creation - Always run validate_hub.py at the end
•Prompt for context - Auto-detected info needs human input for completeness
•Keep diagrams simple - Initial architecture diagrams should be high-level

Common Pitfalls

❌ Don't create documentation without analyzing the codebase first ❌ Don't overwrite existing valid documentation without user confirmation ❌ Don't create empty files - populate with at least template structure

✅ Do use helper scripts to gather information ✅ Do validate before and after ✅ Do ask user for additional context

What Comes Next

After initialization:

•User reviews generated documentation
•User provides additional context via prompts
•Documentation is refined
•Future updates use /document-hub update skill

Helper Script Reference

validate_hub.py - Check documentation structure

bash

python scripts/validate_hub.py /path/to/project
# Returns: {"valid": bool, "errors": [], "warnings": []}

detect_drift.py - Find undocumented code

bash

python scripts/detect_drift.py /path/to/project
# Returns: {"module_drift": {...}, "technology_drift": {...}}

extract_glossary.py - Extract domain terms

bash

python scripts/extract_glossary.py /path/to/project
# Returns: {"terms": [{term, contexts, score}...]}

See scripts/README.md for complete documentation.