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feat: Complete Phase 1 - AI Coding Assistant Integrations (v2.10.0)

Browse Source 
Add comprehensive integration guides for 4 AI coding assistants:

## New Integration Guides (98KB total)
- docs/integrations/WINDSURF.md (20KB) - Windsurf IDE with .windsurfrules
- docs/integrations/CLINE.md (25KB) - Cline VS Code extension with MCP
- docs/integrations/CONTINUE_DEV.md (28KB) - Continue.dev for any IDE
- docs/integrations/INTEGRATIONS.md (25KB) - Comprehensive hub with decision tree

## Working Examples (3 directories, 11 files)
- examples/windsurf-fastapi-context/ - FastAPI + Windsurf automation
- examples/cline-django-assistant/ - Django + Cline with MCP server
- examples/continue-dev-universal/ - HTTP context server for all IDEs

## README.md Updates
- Updated tagline: Universal preprocessor for 10+ AI systems
- Expanded Supported Integrations table (7 → 10 platforms)
- Added 'AI Coding Assistant Integrations' section (60+ lines)
- Cross-links to all new guides and examples

## Impact
- Week 2 of ACTION_PLAN.md: 4/4 tasks complete (100%) ✅
- Total new documentation: ~3,000 lines
- Total new code: ~1,000 lines (automation scripts, servers)
- Integration coverage: LangChain, LlamaIndex, Pinecone, Cursor, Windsurf,
  Cline, Continue.dev, Claude, Gemini, ChatGPT

## Key Features
- All guides follow proven 11-section pattern from CURSOR.md
- Real-world examples with automation scripts
- Multi-IDE consistency (Continue.dev works in VS Code, JetBrains, Vim)
- MCP integration for dynamic documentation access
- Complete troubleshooting sections with solutions

Positions Skill Seekers as universal preprocessor for ANY AI system.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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# Cline + Django Assistant Example
Complete example showing how to use Skill Seekers to generate Cline rules for Django development with MCP integration.
## What This Example Does
- ✅ Generates Django documentation skill
- ✅ Creates .clinerules for Cline agent
- ✅ Sets up MCP server for dynamic documentation access
- ✅ Shows autonomous Django code generation
## Quick Start
### 1. Generate Django Skill
```bash
# Install Skill Seekers with MCP support
pip install skill-seekers[mcp]
# Generate Django documentation skill
skill-seekers scrape --config configs/django.json
# Package for Cline (markdown format)
skill-seekers package output/django --target markdown
```
### 2. Copy to Django Project
```bash
# Copy rules to project root
cp output/django-markdown/SKILL.md my-django-project/.clinerules
# Or use the automation script
python generate_clinerules.py --project my-django-project
```
### 3. Configure MCP Server
```bash
# In VS Code Cline panel:
# Settings → MCP Servers → Add Server
# Add this configuration:
{
"skill-seekers": {
"command": "python",
"args": ["-m", "skill_seekers.mcp.server_fastmcp", "--transport", "stdio"],
"env": {}
}
}
# Reload VS Code
```
### 4. Test in Cline
```bash
# Open project in VS Code
code my-django-project/
# Open Cline panel (sidebar icon)
# Start autonomous task:
"Create a Django blog app with:
- Post model with author, title, content, created_at
- Comment model with post foreign key
- Admin registration
- REST API with DRF
- Full test suite with pytest"
# Cline will autonomously generate code following Django best practices
```
## Expected Results
### Before (Without .clinerules)
**Cline Task:** "Create a Django user model"
**Output:**
```python
from django.db import models
class User(models.Model):
username = models.CharField(max_length=100)
email = models.EmailField()
```
❌ Missing timestamps
❌ No __str__ method
❌ No Meta class
❌ Not using AbstractUser
### After (With .clinerules)
**Cline Task:** "Create a Django user model"
**Output:**
```python
from django.contrib.auth.models import AbstractUser
from django.db import models
class User(AbstractUser):
email = models.EmailField(unique=True)
bio = models.TextField(blank=True)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
class Meta:
ordering = ['-created_at']
verbose_name = 'User'
verbose_name_plural = 'Users'
def __str__(self):
return self.username
```
✅ Uses AbstractUser
✅ Includes timestamps
✅ Has __str__ method
✅ Proper Meta class
✅ Email uniqueness
## Files in This Example
- `generate_clinerules.py` - Automation script
- `mcp_config.json` - MCP server configuration
- `requirements.txt` - Python dependencies
- `example-project/` - Minimal Django project
- `manage.py`
- `app/models.py`
- `app/views.py`
- `tests/`
## MCP Integration Benefits
With MCP server configured, Cline can:
1. **Search documentation dynamically**
```
Cline task: "Use skill-seekers MCP to search Django async views"
```
2. **Generate fresh rules**
```
Cline task: "Use skill-seekers MCP to scrape latest Django 5.0 docs"
```
3. **Package skills on-demand**
```
Cline task: "Use skill-seekers MCP to package React docs for this project"
```
## Rule Files Structure
After setup, your project has:
```
my-django-project/
├── .clinerules # Core Django patterns (auto-loaded)
├── .clinerules.models # Model-specific patterns (optional)
├── .clinerules.views # View-specific patterns (optional)
├── .clinerules.testing # Testing patterns (optional)
├── .clinerules.project # Project conventions (highest priority)
└── .cline/
└── memory-bank/ # Persistent project knowledge
└── README.md
```
Cline automatically loads all `.clinerules*` files.
## Customization
### Add Project-Specific Patterns
Create `.clinerules.project`:
```markdown
# Project-Specific Conventions
## Database Queries
ALWAYS use select_related/prefetch_related:
\```python
# BAD
posts = Post.objects.all() # N+1 queries!
# GOOD
posts = Post.objects.select_related('author').prefetch_related('comments').all()
\```
## API Responses
NEVER expose sensitive fields:
\```python
class UserSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ['id', 'username', 'email', 'bio']
# NEVER include: password, is_staff, is_superuser
\```
```
### Memory Bank Setup
```bash
# Initialize memory bank
mkdir -p .cline/memory-bank
# Add project context
cat > .cline/memory-bank/README.md << 'EOF'
# Project Memory Bank
## Tech Stack
- Django 5.0
- PostgreSQL 16
- Redis for caching
- Celery for background tasks
## Architecture
- Modular apps (users, posts, comments)
- API-first with Django REST Framework
- Async views for I/O-bound operations
## Conventions
- All models inherit from BaseModel (timestamps)
- Use pytest for testing
- API versioning: /api/v1/
EOF
# Ask Cline to initialize
# In Cline: "Initialize memory bank from README"
```
## Troubleshooting
### Issue: .clinerules not loading
**Solution:** Check file location
```bash
# Must be at project root
ls -la .clinerules
# Reload VS Code
# Cmd+Shift+P → "Developer: Reload Window"
```
### Issue: MCP server not connecting
**Solution 1:** Verify installation
```bash
pip show skill-seekers
# Should show: [mcp] extra installed
```
**Solution 2:** Test MCP server directly
```bash
python -m skill_seekers.mcp.server_fastmcp --transport stdio
# Should start without errors
```
**Solution 3:** Use absolute Python path
```json
{
"skill-seekers": {
"command": "/usr/local/bin/python3",
"args": ["-m", "skill_seekers.mcp.server_fastmcp", "--transport", "stdio"]
}
}
```
### Issue: Cline not using rules
**Solution:** Add explicit instructions
```markdown
# Django Expert
You MUST follow these patterns in ALL Django code:
- Include timestamps in models
- Use select_related for queries
- Write tests with pytest
NEVER deviate from these patterns.
```
## Advanced Usage
### Multi-Framework Project (Django + React)
```bash
# Backend rules
skill-seekers package output/django --target markdown
cp output/django-markdown/SKILL.md .clinerules.backend
# Frontend rules
skill-seekers package output/react --target markdown
cp output/react-markdown/SKILL.md .clinerules.frontend
# Now Cline knows BOTH Django AND React patterns
```
### Cline + RAG Pipeline
```python
# Create both .clinerules and RAG pipeline
from skill_seekers.cli.doc_scraper import main as scrape
from skill_seekers.cli.package_skill import main as package
# Scrape
scrape(["--config", "configs/django.json"])
# For Cline
package(["output/django", "--target", "markdown"])
# For RAG search
package(["output/django", "--target", "langchain", "--chunk-for-rag"])
# Now you have:
# 1. .clinerules (for Cline context)
# 2. LangChain docs (for deep search)
```
## Real-World Workflow
### Complete Blog API with Cline
**Task:** "Create production-ready blog API"
**Cline Autonomous Steps:**
1. ✅ Creates models (Post, Comment) with timestamps, __str__, Meta
2. ✅ Adds select_related to querysets (from .clinerules)
3. ✅ Creates serializers with nested data (from .clinerules)
4. ✅ Implements ViewSets with filtering (from .clinerules)
5. ✅ Sets up URL routing (from .clinerules)
6. ✅ Writes pytest tests (from .clinerules.testing)
7. ✅ Adds admin registration (from .clinerules)
**Result:** Production-ready API in minutes, following all best practices!
## Related Examples
- [Cursor Example](../cursor-react-skill/) - Similar IDE approach
- [Windsurf Example](../windsurf-fastapi-context/) - Windsurf IDE
- [Continue.dev Example](../continue-dev-universal/) - IDE-agnostic
- [LangChain RAG Example](../langchain-rag-pipeline/) - RAG integration
## Next Steps
1. Add more frameworks (React, Vue) for full-stack
2. Create memory bank for project knowledge
3. Build RAG pipeline with `--target langchain`
4. Share your .clinerules patterns with community
5. Integrate custom MCP tools for project-specific needs
## Support
- **Skill Seekers Issues:** [GitHub](https://github.com/yusufkaraaslan/Skill_Seekers/issues)
- **Cline Docs:** [docs.cline.bot](https://docs.cline.bot/)
- **Integration Guide:** [CLINE.md](../../docs/integrations/CLINE.md)

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#!/usr/bin/env python3
"""
Automation script to generate Cline rules from Django documentation.
Usage:
python generate_clinerules.py --project /path/to/project
python generate_clinerules.py --project . --with-mcp
"""
import argparse
import json
import shutil
import subprocess
import sys
from pathlib import Path
def run_command(cmd: list[str], description: str) -> bool:
"""Run a shell command and return success status."""
print(f"\n{'='*60}")
print(f"STEP: {description}")
print(f"{'='*60}")
print(f"Running: {' '.join(cmd)}\n")
result = subprocess.run(cmd, capture_output=True, text=True)
if result.stdout:
print(result.stdout)
if result.stderr:
print(result.stderr, file=sys.stderr)
if result.returncode != 0:
print(f"❌ ERROR: {description} failed with code {result.returncode}")
return False
print(f"✅ SUCCESS: {description}")
return True
def setup_mcp_server(project_path: Path) -> bool:
"""Set up MCP server configuration for Cline."""
print(f"\n{'='*60}")
print(f"STEP: Configuring MCP Server")
print(f"{'='*60}")
# Create MCP config
mcp_config = {
"mcpServers": {
"skill-seekers": {
"command": "python",
"args": [
"-m",
"skill_seekers.mcp.server_fastmcp",
"--transport",
"stdio"
],
"env": {}
}
}
}
# Save to project
vscode_dir = project_path / ".vscode"
vscode_dir.mkdir(exist_ok=True)
mcp_config_file = vscode_dir / "mcp_config.json"
with open(mcp_config_file, 'w') as f:
json.dump(mcp_config, f, indent=2)
print(f"✅ Created: {mcp_config_file}")
print(f"\nTo activate in Cline:")
print(f"1. Open Cline panel in VS Code")
print(f"2. Settings → MCP Servers → Load Configuration")
print(f"3. Select: {mcp_config_file}")
print(f"4. Reload VS Code window")
return True
def main():
parser = argparse.ArgumentParser(
description="Generate Cline rules from Django documentation"
)
parser.add_argument(
"--project",
type=str,
default=".",
help="Path to your project directory (default: current directory)",
)
parser.add_argument(
"--skip-scrape",
action="store_true",
help="Skip scraping step (use existing output/django)",
)
parser.add_argument(
"--with-mcp",
action="store_true",
help="Set up MCP server configuration",
)
parser.add_argument(
"--modular",
action="store_true",
help="Create modular rules files (.clinerules.models, .clinerules.views, etc.)",
)
args = parser.parse_args()
project_path = Path(args.project).resolve()
output_dir = Path("output/django")
print("=" * 60)
print("Cline Rules Generator for Django")
print("=" * 60)
print(f"Project: {project_path}")
print(f"Modular rules: {args.modular}")
print(f"MCP integration: {args.with_mcp}")
print("=" * 60)
# Step 1: Scrape Django documentation (unless skipped)
if not args.skip_scrape:
if not run_command(
[
"skill-seekers",
"scrape",
"--config",
"configs/django.json",
],
"Scraping Django documentation",
):
return 1
else:
print(f"\n⏭️ SKIPPED: Using existing {output_dir}")
if not output_dir.exists():
print(f"❌ ERROR: {output_dir} does not exist!")
print(f"Run without --skip-scrape to generate documentation first.")
return 1
# Step 2: Package for Cline
if not run_command(
[
"skill-seekers",
"package",
str(output_dir),
"--target",
"markdown",
],
"Packaging for Cline",
):
return 1
# Step 3: Copy rules to project
print(f"\n{'='*60}")
print(f"STEP: Copying rules to project")
print(f"{'='*60}")
markdown_output = output_dir.parent / "django-markdown"
source_skill = markdown_output / "SKILL.md"
if not source_skill.exists():
print(f"❌ ERROR: {source_skill} does not exist!")
return 1
if args.modular:
# Split into modular files
print("Creating modular rules files...")
with open(source_skill, 'r') as f:
content = f.read()
# Split by major sections
sections = content.split('\n## ')
# Core rules (first part)
core_rules = project_path / ".clinerules"
with open(core_rules, 'w') as f:
f.write(sections[0])
print(f"✅ Created: {core_rules}")
# Try to extract specific sections (simplified)
# In a real implementation, this would be more sophisticated
models_content = next((s for s in sections if 'Model' in s), None)
if models_content:
models_rules = project_path / ".clinerules.models"
with open(models_rules, 'w') as f:
f.write('## ' + models_content)
print(f"✅ Created: {models_rules}")
views_content = next((s for s in sections if 'View' in s), None)
if views_content:
views_rules = project_path / ".clinerules.views"
with open(views_rules, 'w') as f:
f.write('## ' + views_content)
print(f"✅ Created: {views_rules}")
else:
# Single file
dest_file = project_path / ".clinerules"
shutil.copy(source_skill, dest_file)
print(f"✅ Copied: {dest_file}")
# Step 4: Set up MCP server (optional)
if args.with_mcp:
if not setup_mcp_server(project_path):
print("⚠️ WARNING: MCP setup failed, but rules were created successfully")
print(f"\n{'='*60}")
print(f"✅ SUCCESS: Cline rules generated!")
print(f"{'='*60}")
print(f"\nNext steps:")
print(f"1. Open project in VS Code: code {project_path}")
print(f"2. Install Cline extension (if not already)")
print(f"3. Reload VS Code window: Cmd+Shift+P → 'Reload Window'")
print(f"4. Open Cline panel (sidebar icon)")
print(f"5. Start autonomous task:")
print(f" 'Create a Django blog app with posts and comments'")
if args.with_mcp:
print(f"\n📡 MCP Server configured at:")
print(f" {project_path / '.vscode' / 'mcp_config.json'}")
print(f" Load in Cline: Settings → MCP Servers → Load Configuration")
return 0
if __name__ == "__main__":
sys.exit(main())

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skill-seekers[mcp]>=2.9.0
django>=5.0.0
djangorestframework>=3.15.0
pytest>=8.0.0
pytest-django>=4.8.0

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# Continue.dev + Universal Context Example
Complete example showing how to use Skill Seekers to create IDE-agnostic context providers for Continue.dev across VS Code, JetBrains, and other IDEs.
## What This Example Does
- ✅ Generates framework documentation (Vue.js example)
- ✅ Creates HTTP context provider server
- ✅ Works across all IDEs (VS Code, IntelliJ, PyCharm, WebStorm, etc.)
- ✅ Single configuration, consistent results
## Quick Start
### 1. Generate Documentation
```bash
# Install Skill Seekers
pip install skill-seekers[mcp]
# Generate Vue.js documentation
skill-seekers scrape --config configs/vue.json
skill-seekers package output/vue --target markdown
```
### 2. Start Context Server
```bash
# Use the provided HTTP context server
python context_server.py
# Server runs on http://localhost:8765
# Serves documentation at /docs/{framework}
```
### 3. Configure Continue.dev
Edit `~/.continue/config.json`:
```json
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/vue",
"title": "vue-docs",
"displayTitle": "Vue.js Documentation",
"description": "Vue.js framework expert knowledge"
}
}
]
}
```
### 4. Test in Any IDE
**VS Code:**
```bash
code my-vue-project/
# Open Continue panel (Cmd+L)
# Type: @vue-docs Create a Vue 3 component with Composition API
```
**IntelliJ IDEA:**
```bash
idea my-vue-project/
# Open Continue panel (Cmd+L)
# Type: @vue-docs Create a Vue 3 component with Composition API
```
**Result:** IDENTICAL suggestions in both IDEs!
## Expected Results
### Before (Without Context Provider)
**Prompt:** "Create a Vue component"
**Continue Output:**
```javascript
export default {
name: 'MyComponent',
data() {
return {
message: 'Hello'
}
}
}
```
❌ Uses Options API (outdated)
❌ No TypeScript
❌ No Composition API
❌ Generic patterns
### After (With Context Provider)
**Prompt:** "@vue-docs Create a Vue component"
**Continue Output:**
```typescript
<script setup lang="ts">
import { ref, computed } from 'vue'
interface Props {
title: string
count?: number
}
const props = withDefaults(defineProps<Props>(), {
count: 0
})
const message = ref('Hello')
const displayCount = computed(() => props.count * 2)
</script>
<template>
<div>
<h2>{{ props.title }}</h2>
<p>{{ message }} - Count: {{ displayCount }}</p>
</div>
</template>
<style scoped>
/* Component styles */
</style>
```
✅ Composition API with `<script setup>`
✅ TypeScript interfaces
✅ Proper props definition
✅ Vue 3 best practices
## Files in This Example
- `context_server.py` - HTTP context provider server (FastAPI)
- `quickstart.py` - Automation script for setup
- `requirements.txt` - Python dependencies
- `config.example.json` - Sample Continue.dev configuration
## Multi-IDE Testing
This example demonstrates IDE consistency:
### Test 1: VS Code
```bash
cd examples/continue-dev-universal
python context_server.py &
code test-project/
# In Continue: @vue-docs Create a component
# Note the exact code generated
```
### Test 2: IntelliJ IDEA
```bash
# Same server still running
idea test-project/
# In Continue: @vue-docs Create a component
# Code should be IDENTICAL to VS Code
```
### Test 3: PyCharm
```bash
# Same server still running
pycharm test-project/
# In Continue: @vue-docs Create a component
# Code should be IDENTICAL to both above
```
**Why it works:** Continue.dev uses the SAME `~/.continue/config.json` across all IDEs!
## Context Server Architecture
The `context_server.py` implements a simple HTTP server:
```python
from fastapi import FastAPI
from skill_seekers.cli.doc_scraper import load_skill
app = FastAPI()
@app.get("/docs/{framework}")
async def get_framework_docs(framework: str):
"""
Serve framework documentation as Continue context.
Args:
framework: Framework name (vue, react, django, etc.)
Returns:
JSON with contextItems array
"""
# Load documentation
docs = load_skill(f"output/{framework}-markdown/SKILL.md")
return {
"contextItems": [
{
"name": f"{framework.title()} Documentation",
"description": f"Complete {framework} framework knowledge",
"content": docs
}
]
}
```
## Multi-Framework Support
Add more frameworks easily:
```bash
# Generate React docs
skill-seekers scrape --config configs/react.json
skill-seekers package output/react --target markdown
# Generate Django docs
skill-seekers scrape --config configs/django.json
skill-seekers package output/django --target markdown
# Server automatically serves both at:
# http://localhost:8765/docs/react
# http://localhost:8765/docs/django
```
Update `~/.continue/config.json`:
```json
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/vue",
"title": "vue-docs",
"displayTitle": "Vue.js"
}
},
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/react",
"title": "react-docs",
"displayTitle": "React"
}
},
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/django",
"title": "django-docs",
"displayTitle": "Django"
}
}
]
}
```
Now you can use:
```
@vue-docs @react-docs @django-docs Create a full-stack app
```
## Team Deployment
### Option 1: Shared Server
```bash
# Run on team server
ssh team-server
python context_server.py --host 0.0.0.0 --port 8765
# Team members update config:
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://team-server.company.com:8765/docs/vue",
"title": "vue-docs"
}
}
]
}
```
### Option 2: Docker Deployment
```dockerfile
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY context_server.py .
COPY output/ output/
EXPOSE 8765
CMD ["python", "context_server.py", "--host", "0.0.0.0"]
```
```bash
# Build and run
docker build -t skill-seekers-context .
docker run -d -p 8765:8765 skill-seekers-context
# Team uses: http://your-server:8765/docs/vue
```
### Option 3: Kubernetes Deployment
```yaml
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: skill-seekers-context
spec:
replicas: 3
selector:
matchLabels:
app: skill-seekers-context
template:
metadata:
labels:
app: skill-seekers-context
spec:
containers:
- name: context-server
image: skill-seekers-context:latest
ports:
- containerPort: 8765
---
apiVersion: v1
kind: Service
metadata:
name: skill-seekers-context
spec:
selector:
app: skill-seekers-context
ports:
- port: 80
targetPort: 8765
type: LoadBalancer
```
## Customization
### Add Project-Specific Context
```python
# In context_server.py
@app.get("/project/conventions")
async def get_project_conventions():
"""Serve company-specific patterns."""
return {
"contextItems": [{
"name": "Project Conventions",
"description": "Company coding standards",
"content": """
# Company Coding Standards
## Vue Components
- Always use Composition API
- TypeScript is required
- Props must have interfaces
- Use Pinia for state management
## API Calls
- Use axios with interceptors
- All endpoints must be typed
- Error handling with try/catch
- Loading states required
"""
}]
}
```
Add to Continue config:
```json
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/vue",
"title": "vue-docs"
}
},
{
"name": "http",
"params": {
"url": "http://localhost:8765/project/conventions",
"title": "conventions",
"displayTitle": "Company Standards"
}
}
]
}
```
Now use both:
```
@vue-docs @conventions Create a component following our standards
```
## Troubleshooting
### Issue: Context provider not showing
**Solution:** Check server is running
```bash
curl http://localhost:8765/docs/vue
# Should return JSON
# If not running:
python context_server.py
```
### Issue: Different results in different IDEs
**Solution:** Verify same config file
```bash
# All IDEs use same config
cat ~/.continue/config.json
# NOT project-specific configs
# (those would cause inconsistency)
```
### Issue: Documentation outdated
**Solution:** Re-generate and restart
```bash
skill-seekers scrape --config configs/vue.json
skill-seekers package output/vue --target markdown
# Restart server (will load new docs)
pkill -f context_server.py
python context_server.py
```
## Advanced Usage
### RAG Integration
```python
# rag_context_server.py
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
# Load vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(
persist_directory="./chroma_db",
embedding_function=embeddings
)
@app.get("/docs/search")
async def search_docs(query: str, k: int = 5):
"""RAG-powered search."""
results = vectorstore.similarity_search(query, k=k)
return {
"contextItems": [
{
"name": f"Result {i+1}",
"description": doc.metadata.get("source", "Docs"),
"content": doc.page_content
}
for i, doc in enumerate(results)
]
}
```
Continue config:
```json
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/search?query={query}",
"title": "rag-search",
"displayTitle": "RAG Search"
}
}
]
}
```
### MCP Integration
```bash
# Install MCP support
pip install skill-seekers[mcp]
# Continue config with MCP
{
"mcpServers": {
"skill-seekers": {
"command": "python",
"args": ["-m", "skill_seekers.mcp.server_fastmcp", "--transport", "stdio"]
}
},
"contextProviders": [
{
"name": "mcp",
"params": {
"serverName": "skill-seekers"
}
}
]
}
```
## Performance Tips
### 1. Cache Documentation
```python
from functools import lru_cache
@lru_cache(maxsize=100)
def load_cached_docs(framework: str) -> str:
"""Cache docs in memory."""
return load_skill(f"output/{framework}-markdown/SKILL.md")
```
### 2. Compress Responses
```python
from fastapi.responses import JSONResponse
import gzip
@app.get("/docs/{framework}")
async def get_docs(framework: str):
docs = load_cached_docs(framework)
# Compress if large
if len(docs) > 10000:
docs = gzip.compress(docs.encode()).decode('latin1')
return JSONResponse(...)
```
### 3. Load Balancing
```bash
# Run multiple instances
python context_server.py --port 8765 &
python context_server.py --port 8766 &
python context_server.py --port 8767 &
# Configure Continue with failover
{
"contextProviders": [
{
"name": "http",
"params": {
"url": "http://localhost:8765/docs/vue",
"fallbackUrls": [
"http://localhost:8766/docs/vue",
"http://localhost:8767/docs/vue"
]
}
}
]
}
```
## Related Examples
- [Cursor Example](../cursor-react-skill/) - IDE-specific approach
- [Windsurf Example](../windsurf-fastapi-context/) - Windsurf IDE
- [Cline Example](../cline-django-assistant/) - VS Code extension
- [LangChain RAG Example](../langchain-rag-pipeline/) - RAG integration
## Next Steps
1. Add more frameworks for full-stack development
2. Deploy to team server for shared access
3. Integrate with RAG for deep search
4. Create project-specific context providers
5. Set up CI/CD for automatic documentation updates
## Support
- **Skill Seekers Issues:** [GitHub](https://github.com/yusufkaraaslan/Skill_Seekers/issues)
- **Continue.dev Docs:** [docs.continue.dev](https://docs.continue.dev/)
- **Integration Guide:** [CONTINUE_DEV.md](../../docs/integrations/CONTINUE_DEV.md)

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#!/usr/bin/env python3
"""
HTTP Context Provider Server for Continue.dev
Serves framework documentation as Continue.dev context items.
Supports multiple frameworks from Skill Seekers output.
Usage:
python context_server.py
python context_server.py --host 0.0.0.0 --port 8765
"""
import argparse
from pathlib import Path
from functools import lru_cache
from typing import Dict, List
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
app = FastAPI(
title="Skill Seekers Context Server",
description="HTTP context provider for Continue.dev",
version="1.0.0"
)
# Add CORS middleware for browser access
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@lru_cache(maxsize=100)
def load_framework_docs(framework: str) -> str:
"""
Load framework documentation from Skill Seekers output.
Args:
framework: Framework name (vue, react, django, etc.)
Returns:
Documentation content as string
Raises:
FileNotFoundError: If documentation not found
"""
# Try multiple possible locations
possible_paths = [
Path(f"output/{framework}-markdown/SKILL.md"),
Path(f"../../output/{framework}-markdown/SKILL.md"),
Path(f"../../../output/{framework}-markdown/SKILL.md"),
]
for doc_path in possible_paths:
if doc_path.exists():
with open(doc_path, 'r', encoding='utf-8') as f:
return f.read()
raise FileNotFoundError(
f"Documentation not found for framework: {framework}\n"
f"Tried paths: {[str(p) for p in possible_paths]}\n"
f"Run: skill-seekers scrape --config configs/{framework}.json"
)
@app.get("/")
async def root():
"""Root endpoint with server information."""
return {
"name": "Skill Seekers Context Server",
"description": "HTTP context provider for Continue.dev",
"version": "1.0.0",
"endpoints": {
"/docs/{framework}": "Get framework documentation",
"/frameworks": "List available frameworks",
"/health": "Health check"
}
}
@app.get("/health")
async def health():
"""Health check endpoint."""
return {"status": "healthy"}
@app.get("/frameworks")
async def list_frameworks() -> Dict[str, List[str]]:
"""
List available frameworks.
Returns:
Dictionary with available and missing frameworks
"""
# Check common framework locations
output_dir = Path("output")
if not output_dir.exists():
output_dir = Path("../../output")
if not output_dir.exists():
output_dir = Path("../../../output")
if not output_dir.exists():
return {
"available": [],
"message": "No output directory found. Run skill-seekers to generate documentation."
}
# Find all *-markdown directories
available = []
for item in output_dir.glob("*-markdown"):
framework = item.name.replace("-markdown", "")
skill_file = item / "SKILL.md"
if skill_file.exists():
available.append(framework)
return {
"available": available,
"count": len(available),
"usage": "GET /docs/{framework} to access documentation"
}
@app.get("/docs/{framework}")
async def get_framework_docs(framework: str, query: str = None) -> JSONResponse:
"""
Get framework documentation as Continue.dev context items.
Args:
framework: Framework name (vue, react, django, etc.)
query: Optional search query for filtering (future feature)
Returns:
JSON response with contextItems array for Continue.dev
"""
try:
# Load documentation (cached)
docs = load_framework_docs(framework)
# TODO: Implement query filtering if provided
if query:
# Filter docs based on query (simplified)
# In production, use better search (regex, fuzzy matching, etc.)
pass
# Return in Continue.dev format
return JSONResponse({
"contextItems": [
{
"name": f"{framework.title()} Documentation",
"description": f"Complete {framework} framework expert knowledge",
"content": docs
}
]
})
except FileNotFoundError as e:
raise HTTPException(
status_code=404,
detail=str(e)
)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Error loading documentation: {str(e)}"
)
@app.get("/project/conventions")
async def get_project_conventions() -> JSONResponse:
"""
Get project-specific conventions.
Returns:
JSON response with project conventions
"""
# Load project conventions if they exist
conventions_path = Path(".project-conventions.md")
if conventions_path.exists():
with open(conventions_path, 'r') as f:
content = f.read()
else:
# Default conventions
content = """
# Project Conventions
## General
- Use TypeScript for all new code
- Follow framework-specific best practices
- Write tests for all features
## Git Workflow
- Feature branch workflow
- Squash commits before merge
- Descriptive commit messages
## Code Style
- Use prettier for formatting
- ESLint for linting
- Follow team conventions
"""
return JSONResponse({
"contextItems": [
{
"name": "Project Conventions",
"description": "Team coding standards and conventions",
"content": content
}
]
})
def main():
parser = argparse.ArgumentParser(
description="HTTP Context Provider Server for Continue.dev"
)
parser.add_argument(
"--host",
type=str,
default="127.0.0.1",
help="Host to bind to (default: 127.0.0.1, use 0.0.0.0 for all interfaces)"
)
parser.add_argument(
"--port",
type=int,
default=8765,
help="Port to bind to (default: 8765)"
)
parser.add_argument(
"--reload",
action="store_true",
help="Enable auto-reload on code changes (development)"
)
args = parser.parse_args()
print("=" * 60)
print("Skill Seekers Context Server for Continue.dev")
print("=" * 60)
print(f"Server: http://{args.host}:{args.port}")
print(f"Endpoints:")
print(f" - GET / # Server info")
print(f" - GET /health # Health check")
print(f" - GET /frameworks # List available frameworks")
print(f" - GET /docs/{{framework}} # Get framework docs")
print(f" - GET /project/conventions # Get project conventions")
print("=" * 60)
print(f"\nConfigure Continue.dev:")
print(f"""
{{
"contextProviders": [
{{
"name": "http",
"params": {{
"url": "http://{args.host}:{args.port}/docs/vue",
"title": "vue-docs",
"displayTitle": "Vue.js Documentation"
}}
}}
]
}}
""")
print("=" * 60)
print("\nPress Ctrl+C to stop\n")
# Run server
uvicorn.run(
app,
host=args.host,
port=args.port,
reload=args.reload,
log_level="info"
)
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
Quickstart script for Continue.dev + Skill Seekers integration.
Usage:
python quickstart.py --framework vue
python quickstart.py --framework django --skip-scrape
"""
import argparse
import json
import subprocess
import sys
from pathlib import Path
def run_command(cmd: list[str], description: str) -> bool:
"""Run a shell command and return success status."""
print(f"\n{'='*60}")
print(f"STEP: {description}")
print(f"{'='*60}")
print(f"Running: {' '.join(cmd)}\n")
result = subprocess.run(cmd, capture_output=True, text=True)
if result.stdout:
print(result.stdout)
if result.stderr:
print(result.stderr, file=sys.stderr)
if result.returncode != 0:
print(f"❌ ERROR: {description} failed with code {result.returncode}")
return False
print(f"✅ SUCCESS: {description}")
return True
def create_continue_config(framework: str, port: int = 8765) -> Path:
"""
Create Continue.dev configuration.
Args:
framework: Framework name
port: Context server port
Returns:
Path to created config file
"""
config_dir = Path.home() / ".continue"
config_dir.mkdir(exist_ok=True)
config_path = config_dir / "config.json"
# Load existing config or create new
if config_path.exists():
with open(config_path, 'r') as f:
config = json.load(f)
else:
config = {
"models": [],
"contextProviders": []
}
# Add context provider for this framework
provider = {
"name": "http",
"params": {
"url": f"http://localhost:{port}/docs/{framework}",
"title": f"{framework}-docs",
"displayTitle": f"{framework.title()} Documentation",
"description": f"{framework} framework expert knowledge"
}
}
# Check if already exists
existing = [
p for p in config.get("contextProviders", [])
if p.get("params", {}).get("title") == provider["params"]["title"]
]
if not existing:
config.setdefault("contextProviders", []).append(provider)
print(f"✅ Added {framework} context provider to Continue config")
else:
print(f"⏭️ {framework} context provider already exists in Continue config")
# Save config
with open(config_path, 'w') as f:
json.dump(config, f, indent=2)
return config_path
def main():
parser = argparse.ArgumentParser(
description="Quickstart script for Continue.dev + Skill Seekers"
)
parser.add_argument(
"--framework",
type=str,
required=True,
help="Framework to generate documentation for (vue, react, django, etc.)"
)
parser.add_argument(
"--skip-scrape",
action="store_true",
help="Skip scraping step (use existing output)"
)
parser.add_argument(
"--port",
type=int,
default=8765,
help="Context server port (default: 8765)"
)
args = parser.parse_args()
framework = args.framework.lower()
output_dir = Path(f"output/{framework}")
print("=" * 60)
print("Continue.dev + Skill Seekers Quickstart")
print("=" * 60)
print(f"Framework: {framework}")
print(f"Context server port: {args.port}")
print("=" * 60)
# Step 1: Scrape documentation (unless skipped)
if not args.skip_scrape:
if not run_command(
[
"skill-seekers",
"scrape",
"--config",
f"configs/{framework}.json"
],
f"Scraping {framework} documentation"
):
return 1
else:
print(f"\n⏭️ SKIPPED: Using existing {output_dir}")
if not output_dir.exists():
print(f"❌ ERROR: {output_dir} does not exist!")
print(f"Run without --skip-scrape to generate documentation first.")
return 1
# Step 2: Package documentation
if not run_command(
[
"skill-seekers",
"package",
str(output_dir),
"--target",
"markdown"
],
f"Packaging {framework} documentation"
):
return 1
# Step 3: Create Continue config
print(f"\n{'='*60}")
print(f"STEP: Configuring Continue.dev")
print(f"{'='*60}")
config_path = create_continue_config(framework, args.port)
print(f"✅ Continue config updated: {config_path}")
# Step 4: Instructions for starting server
print(f"\n{'='*60}")
print(f"✅ SUCCESS: Setup complete!")
print(f"{'='*60}")
print(f"\nNext steps:")
print(f"\n1. Start context server:")
print(f" python context_server.py --port {args.port}")
print(f"\n2. Open any IDE with Continue.dev:")
print(f" - VS Code: code my-project/")
print(f" - IntelliJ: idea my-project/")
print(f" - PyCharm: pycharm my-project/")
print(f"\n3. Test in Continue panel (Cmd+L or Ctrl+L):")
print(f" @{framework}-docs Create a {framework} component")
print(f"\n4. Verify Continue references documentation")
print(f"\nContinue config location: {config_path}")
print(f"Context provider: @{framework}-docs")
return 0
if __name__ == "__main__":
sys.exit(main())

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skill-seekers[mcp]>=2.9.0
fastapi>=0.115.0
uvicorn>=0.32.0

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# Windsurf + FastAPI Context Example
Complete example showing how to use Skill Seekers to generate Windsurf rules for FastAPI development.
## What This Example Does
- ✅ Generates FastAPI documentation skill
- ✅ Creates modular .windsurfrules for Windsurf IDE
- ✅ Shows Cascade AI-powered FastAPI code generation
- ✅ Handles character limits with split rules
## Quick Start
### 1. Generate FastAPI Skill
```bash
# Install Skill Seekers
pip install skill-seekers
# Generate FastAPI documentation skill
skill-seekers scrape --config configs/fastapi.json
# Package for Windsurf with split rules (respects 6K char limit)
skill-seekers package output/fastapi --target markdown --split-rules
```
### 2. Copy to Windsurf Project
```bash
# Create rules directory
mkdir -p my-fastapi-project/.windsurf/rules
# Copy all rule files
cp -r output/fastapi-markdown/rules/* my-fastapi-project/.windsurf/rules/
# Or use the automation script
python generate_windsurfrules.py --project my-fastapi-project
```
### 3. Test in Windsurf
```bash
# Open project in Windsurf
windsurf my-fastapi-project/
# Start Cascade (Cmd+L or Ctrl+L)
# Try these prompts:
# - "Create a FastAPI endpoint with async database queries"
# - "Add Pydantic models with validation for user registration"
# - "Implement JWT authentication with dependencies"
```
## Expected Results
### Before (Without Rules)
**Prompt:** "Create a FastAPI user endpoint with database"
**Cascade Output:**
```python
from fastapi import FastAPI
app = FastAPI()
@app.get("/users")
def get_users():
# Generic sync code
users = db.query(User).all()
return users
```
❌ Uses sync code (not async)
❌ No dependency injection
❌ Returns ORM instances (not Pydantic)
### After (With Rules)
**Prompt:** "Create a FastAPI user endpoint with database"
**Cascade Output:**
```python
from fastapi import APIRouter, Depends
from sqlalchemy.ext.asyncio import AsyncSession
from app.database import get_db
from app.schemas import UserResponse
router = APIRouter(prefix="/api/v1")
@router.get("/users", response_model=list[UserResponse])
async def get_users(
skip: int = 0,
limit: int = 100,
db: AsyncSession = Depends(get_db)
):
"""Get all users with pagination."""
result = await db.execute(
select(User).offset(skip).limit(limit)
)
users = result.scalars().all()
return [UserResponse.model_validate(user) for user in users]
```
✅ Async/await pattern
✅ Dependency injection
✅ Pydantic response models
✅ Proper pagination
✅ OpenAPI documentation
## Files in This Example
- `generate_windsurfrules.py` - Automation script for generating rules
- `requirements.txt` - Python dependencies
- `example-project/` - Minimal FastAPI project structure
- `app/main.py` - FastAPI application
- `app/models.py` - SQLAlchemy models
- `app/schemas.py` - Pydantic schemas
- `app/database.py` - Database connection
## Rule Files Generated
After running the script, you'll have:
```
my-fastapi-project/.windsurf/rules/
├── fastapi-core.md (5,200 chars, Always On)
├── fastapi-database.md (5,800 chars, Always On)
├── fastapi-authentication.md (4,900 chars, Model Decision)
├── fastapi-testing.md (4,100 chars, Manual)
└── fastapi-best-practices.md (3,500 chars, Always On)
```
## Rule Activation Modes
| File | Activation | When Used |
|------|-----------|-----------|
| `fastapi-core.md` | Always On | Every request - core patterns |
| `fastapi-database.md` | Always On | Database-related code |
| `fastapi-authentication.md` | Model Decision | When Cascade detects auth needs |
| `fastapi-testing.md` | Manual | Only when @mentioned for testing |
| `fastapi-best-practices.md` | Always On | Code quality, error handling |
## Customization
### Add Project-Specific Patterns
Create `project-conventions.md`:
```markdown
---
name: "Project Conventions"
activation: "always-on"
priority: "highest"
---
# Project-Specific Patterns
## Database Sessions
ALWAYS use this pattern:
\```python
async with get_session() as db:
result = await db.execute(query)
\```
## API Versioning
All endpoints MUST use `/api/v1` prefix:
\```python
router = APIRouter(prefix="/api/v1")
\```
```
### Adjust Character Limits
```bash
# Generate smaller rule files (5K chars each)
skill-seekers package output/fastapi --target markdown --split-rules --max-chars 5000
# Generate larger rule files (5.5K chars each)
skill-seekers package output/fastapi --target markdown --split-rules --max-chars 5500
```
## Troubleshooting
### Issue: Rules not loading
**Solution 1:** Verify directory structure
```bash
# Must be exactly:
my-project/.windsurf/rules/*.md
# Check:
ls -la my-project/.windsurf/rules/
```
**Solution 2:** Reload Windsurf
```
Cmd+Shift+P → "Reload Window"
```
### Issue: Character limit exceeded
**Solution:** Re-generate with smaller max-chars
```bash
skill-seekers package output/fastapi --target markdown --split-rules --max-chars 4500
```
### Issue: Cascade not using rules
**Solution:** Check activation mode in frontmatter
```markdown
---
activation: "always-on" # Not "model-decision"
priority: "high"
---
```
## Advanced Usage
### Combine with MCP Server
```bash
# Install Skill Seekers MCP server
pip install skill-seekers[mcp]
# Configure in Windsurf's mcp_config.json
{
"mcpServers": {
"skill-seekers": {
"command": "python",
"args": ["-m", "skill_seekers.mcp.server_fastmcp", "--transport", "stdio"]
}
}
}
```
Now Cascade can query documentation dynamically via MCP tools.
### Multi-Framework Project
```bash
# Generate backend rules (FastAPI)
skill-seekers package output/fastapi --target markdown --split-rules
# Generate frontend rules (React)
skill-seekers package output/react --target markdown --split-rules
# Organize rules:
.windsurf/rules/
├── backend/
│ ├── fastapi-core.md
│ └── fastapi-database.md
└── frontend/
├── react-hooks.md
└── react-components.md
```
## Related Examples
- [Cursor Example](../cursor-react-skill/) - Similar IDE, different format
- [Cline Example](../cline-django-assistant/) - VS Code extension with MCP
- [Continue.dev Example](../continue-dev-universal/) - IDE-agnostic
- [LangChain RAG Example](../langchain-rag-pipeline/) - Build RAG systems
## Next Steps
1. Customize rules for your project patterns
2. Add team-specific conventions
3. Integrate with MCP for live documentation
4. Build RAG pipeline with `--target langchain`
5. Share your rules at [Windsurf Rules Directory](https://windsurf.com/editor/directory)
## Support
- **Skill Seekers Issues:** [GitHub](https://github.com/yusufkaraaslan/Skill_Seekers/issues)
- **Windsurf Docs:** [docs.windsurf.com](https://docs.windsurf.com/)
- **Integration Guide:** [WINDSURF.md](../../docs/integrations/WINDSURF.md)

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#!/usr/bin/env python3
"""
Automation script to generate Windsurf rules from FastAPI documentation.
Usage:
python generate_windsurfrules.py --project /path/to/project
python generate_windsurfrules.py --project . --max-chars 5000
"""
import argparse
import shutil
import subprocess
import sys
from pathlib import Path
def run_command(cmd: list[str], description: str) -> bool:
"""Run a shell command and return success status."""
print(f"\n{'='*60}")
print(f"STEP: {description}")
print(f"{'='*60}")
print(f"Running: {' '.join(cmd)}\n")
result = subprocess.run(cmd, capture_output=True, text=True)
if result.stdout:
print(result.stdout)
if result.stderr:
print(result.stderr, file=sys.stderr)
if result.returncode != 0:
print(f"❌ ERROR: {description} failed with code {result.returncode}")
return False
print(f"✅ SUCCESS: {description}")
return True
def main():
parser = argparse.ArgumentParser(
description="Generate Windsurf rules from FastAPI documentation"
)
parser.add_argument(
"--project",
type=str,
default=".",
help="Path to your project directory (default: current directory)",
)
parser.add_argument(
"--max-chars",
type=int,
default=5500,
help="Maximum characters per rule file (default: 5500, max: 6000)",
)
parser.add_argument(
"--skip-scrape",
action="store_true",
help="Skip scraping step (use existing output/fastapi)",
)
args = parser.parse_args()
project_path = Path(args.project).resolve()
output_dir = Path("output/fastapi")
rules_dir = project_path / ".windsurf" / "rules"
print("=" * 60)
print("Windsurf Rules Generator for FastAPI")
print("=" * 60)
print(f"Project: {project_path}")
print(f"Rules directory: {rules_dir}")
print(f"Max characters per file: {args.max_chars}")
print("=" * 60)
# Step 1: Scrape FastAPI documentation (unless skipped)
if not args.skip_scrape:
if not run_command(
[
"skill-seekers",
"scrape",
"--config",
"configs/fastapi.json",
],
"Scraping FastAPI documentation",
):
return 1
else:
print(f"\n⏭️ SKIPPED: Using existing {output_dir}")
if not output_dir.exists():
print(f"❌ ERROR: {output_dir} does not exist!")
print(f"Run without --skip-scrape to generate documentation first.")
return 1
# Step 2: Package with split rules
if not run_command(
[
"skill-seekers",
"package",
str(output_dir),
"--target",
"markdown",
"--split-rules",
"--max-chars",
str(args.max_chars),
],
"Packaging for Windsurf with split rules",
):
return 1
# Step 3: Copy rules to project
print(f"\n{'='*60}")
print(f"STEP: Copying rules to project")
print(f"{'='*60}")
markdown_output = output_dir.parent / "fastapi-markdown"
source_rules = markdown_output / "rules"
if not source_rules.exists():
# Single file (no splitting needed)
source_skill = markdown_output / "SKILL.md"
if not source_skill.exists():
print(f"❌ ERROR: {source_skill} does not exist!")
return 1
# Create rules directory
rules_dir.mkdir(parents=True, exist_ok=True)
# Copy as single rule file
dest_file = rules_dir / "fastapi.md"
shutil.copy(source_skill, dest_file)
print(f"✅ Copied: {dest_file}")
else:
# Multiple rule files
rules_dir.mkdir(parents=True, exist_ok=True)
for rule_file in source_rules.glob("*.md"):
dest_file = rules_dir / rule_file.name
shutil.copy(rule_file, dest_file)
print(f"✅ Copied: {dest_file}")
print(f"\n{'='*60}")
print(f"✅ SUCCESS: Rules generated and copied!")
print(f"{'='*60}")
print(f"\nRules location: {rules_dir}")
print(f"\nNext steps:")
print(f"1. Open project in Windsurf: windsurf {project_path}")
print(f"2. Reload window: Cmd+Shift+P → 'Reload Window'")
print(f"3. Start Cascade: Cmd+L (or Ctrl+L)")
print(f"4. Test: 'Create a FastAPI endpoint with async database'")
print(f"\nRule files:")
for rule_file in sorted(rules_dir.glob("*.md")):
size = rule_file.stat().st_size
print(f" - {rule_file.name} ({size:,} bytes)")
return 0
if __name__ == "__main__":
sys.exit(main())

4
examples/windsurf-fastapi-context/requirements.txt Normal file
View File

@@ -0,0 +1,4 @@
skill-seekers>=2.9.0
fastapi>=0.115.0
uvicorn>=0.32.0
sqlalchemy>=2.0.0