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docs: audit and clean up docs/ directory

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Removals (duplicate/stale):
- docs/DOCKER_GUIDE.md: 80% overlap with DOCKER_DEPLOYMENT.md
- docs/KUBERNETES_GUIDE.md: 70% overlap with KUBERNETES_DEPLOYMENT.md
- docs/strategy/TASK19_COMPLETE.md: stale task tracking
- docs/strategy/TASK20_COMPLETE.md: stale task tracking
- docs/strategy/TASK21_COMPLETE.md: stale task tracking
- docs/strategy/WEEK2_COMPLETE.md: stale progress report

Updates (version/counts):
- docs/FAQ.md: v2.7.0 → v3.1.0-dev, 18 MCP tools → 26, 4 platforms → 16+
- docs/QUICK_REFERENCE.md: 18 MCP tools → 26, 1200+ tests → 1,880+, footer updated
- docs/features/BOOTSTRAP_SKILL.md: v2.7.0 → v3.1.0-dev header and footer

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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# Docker Deployment Guide
Complete guide for deploying Skill Seekers using Docker and Docker Compose.
## Quick Start
### 1. Prerequisites
- Docker 20.10+ installed
- Docker Compose 2.0+ installed
- 2GB+ available RAM
- 5GB+ available disk space
```bash
# Check Docker installation
docker --version
docker-compose --version
```
### 2. Clone Repository
```bash
git clone https://github.com/your-org/skill-seekers.git
cd skill-seekers
```
### 3. Configure Environment
```bash
# Copy environment template
cp .env.example .env
# Edit .env with your API keys
nano .env # or your preferred editor
```
**Minimum Required:**
- `ANTHROPIC_API_KEY` - For AI enhancement features
### 4. Start Services
```bash
# Start all services (CLI + MCP server + vector DBs)
docker-compose up -d
# Or start specific services
docker-compose up -d mcp-server weaviate
```
### 5. Verify Deployment
```bash
# Check service status
docker-compose ps
# Test CLI
docker-compose run skill-seekers skill-seekers --version
# Test MCP server
curl http://localhost:8765/health
```
---
## Available Images
### 1. skill-seekers (CLI)
**Purpose:** Main CLI application for documentation scraping and skill generation
**Usage:**
```bash
# Run CLI command
docker run --rm \
-v $(pwd)/output:/output \
-e ANTHROPIC_API_KEY=your-key \
skill-seekers skill-seekers scrape --config /configs/react.json
# Interactive shell
docker run -it --rm skill-seekers bash
```
**Image Size:** ~400MB
**Platforms:** linux/amd64, linux/arm64
### 2. skill-seekers-mcp (MCP Server)
**Purpose:** MCP server with 25 tools for AI assistants
**Usage:**
```bash
# HTTP mode (default)
docker run -d -p 8765:8765 \
-e ANTHROPIC_API_KEY=your-key \
skill-seekers-mcp
# Stdio mode
docker run -it \
-e ANTHROPIC_API_KEY=your-key \
skill-seekers-mcp \
python -m skill_seekers.mcp.server_fastmcp --transport stdio
```
**Image Size:** ~450MB
**Platforms:** linux/amd64, linux/arm64
**Health Check:** http://localhost:8765/health
---
## Docker Compose Services
### Service Architecture
```
┌─────────────────────┐
│ skill-seekers │ CLI Application
└─────────────────────┘
┌─────────────────────┐
│ mcp-server │ MCP Server (25 tools)
│ Port: 8765 │
└─────────────────────┘
┌─────────────────────┐
│ weaviate │ Vector DB (hybrid search)
│ Port: 8080 │
└─────────────────────┘
┌─────────────────────┐
│ qdrant │ Vector DB (native filtering)
│ Ports: 6333/6334 │
└─────────────────────┘
┌─────────────────────┐
│ chroma │ Vector DB (local-first)
│ Port: 8000 │
└─────────────────────┘
```
### Service Commands
```bash
# Start all services
docker-compose up -d
# Start specific services
docker-compose up -d mcp-server weaviate
# Stop all services
docker-compose down
# View logs
docker-compose logs -f mcp-server
# Restart service
docker-compose restart mcp-server
# Scale service (if supported)
docker-compose up -d --scale mcp-server=3
```
---
## Common Use Cases
### Use Case 1: Scrape Documentation
```bash
# Create skill from React documentation
docker-compose run skill-seekers \
skill-seekers scrape --config /configs/react.json
# Output will be in ./output/react/
```
### Use Case 2: Export to Vector Databases
```bash
# Export React skill to all vector databases
docker-compose run skill-seekers bash -c "
skill-seekers scrape --config /configs/react.json &&
python -c '
import sys
from pathlib import Path
sys.path.insert(0, \"/app/src\")
from skill_seekers.cli.adaptors import get_adaptor
for target in [\"weaviate\", \"chroma\", \"faiss\", \"qdrant\"]:
adaptor = get_adaptor(target)
adaptor.package(Path(\"/output/react\"), Path(\"/output\"))
print(f\"✅ Exported to {target}\")
'
"
```
### Use Case 3: Run Quality Analysis
```bash
# Generate quality report for a skill
docker-compose run skill-seekers bash -c "
python3 <<'EOF'
import sys
from pathlib import Path
sys.path.insert(0, '/app/src')
from skill_seekers.cli.quality_metrics import QualityAnalyzer
analyzer = QualityAnalyzer(Path('/output/react'))
report = analyzer.generate_report()
print(analyzer.format_report(report))
EOF
"
```
### Use Case 4: MCP Server Integration
```bash
# Start MCP server
docker-compose up -d mcp-server
# Configure Claude Desktop
# Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"skill-seekers": {
"url": "http://localhost:8765/sse"
}
}
}
```
---
## Volume Management
### Default Volumes
| Volume | Path | Purpose |
|--------|------|---------|
| `./data` | `/data` | Persistent data (cache, logs) |
| `./configs` | `/configs` | Configuration files (read-only) |
| `./output` | `/output` | Generated skills and exports |
| `weaviate-data` | N/A | Weaviate database storage |
| `qdrant-data` | N/A | Qdrant database storage |
| `chroma-data` | N/A | Chroma database storage |
### Backup Volumes
```bash
# Backup vector database data
docker run --rm -v skill-seekers_weaviate-data:/data -v $(pwd):/backup \
alpine tar czf /backup/weaviate-backup.tar.gz -C /data .
# Restore from backup
docker run --rm -v skill-seekers_weaviate-data:/data -v $(pwd):/backup \
alpine tar xzf /backup/weaviate-backup.tar.gz -C /data
```
### Clean Up Volumes
```bash
# Remove all volumes (WARNING: deletes all data)
docker-compose down -v
# Remove specific volume
docker volume rm skill-seekers_weaviate-data
```
---
## Environment Variables
### Required Variables
| Variable | Description | Example |
|----------|-------------|---------|
| `ANTHROPIC_API_KEY` | Claude AI API key | `sk-ant-...` |
### Optional Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `GOOGLE_API_KEY` | Gemini API key | - |
| `OPENAI_API_KEY` | OpenAI API key | - |
| `GITHUB_TOKEN` | GitHub API token | - |
| `MCP_TRANSPORT` | MCP transport mode | `http` |
| `MCP_PORT` | MCP server port | `8765` |
### Setting Variables
**Option 1: .env file (recommended)**
```bash
cp .env.example .env
# Edit .env with your keys
```
**Option 2: Export in shell**
```bash
export ANTHROPIC_API_KEY=sk-ant-your-key
docker-compose up -d
```
**Option 3: Inline**
```bash
ANTHROPIC_API_KEY=sk-ant-your-key docker-compose up -d
```
---
## Building Images Locally
### Build CLI Image
```bash
docker build -t skill-seekers:local -f Dockerfile .
```
### Build MCP Server Image
```bash
docker build -t skill-seekers-mcp:local -f Dockerfile.mcp .
```
### Build with Custom Base Image
```bash
# Use slim base (smaller)
docker build -t skill-seekers:slim \
--build-arg BASE_IMAGE=python:3.12-slim \
-f Dockerfile .
# Use alpine base (smallest)
docker build -t skill-seekers:alpine \
--build-arg BASE_IMAGE=python:3.12-alpine \
-f Dockerfile .
```
---
## Troubleshooting
### Issue: MCP Server Won't Start
**Symptoms:**
- Container exits immediately
- Health check fails
**Solutions:**
```bash
# Check logs
docker-compose logs mcp-server
# Verify port is available
lsof -i :8765
# Test MCP package installation
docker-compose run mcp-server python -c "import mcp; print('OK')"
```
### Issue: Permission Denied
**Symptoms:**
- Cannot write to /output
- Cannot access /configs
**Solutions:**
```bash
# Fix permissions
chmod -R 777 data/ output/
# Or use specific user ID
docker-compose run -u $(id -u):$(id -g) skill-seekers ...
```
### Issue: Out of Memory
**Symptoms:**
- Container killed
- OOMKilled in `docker-compose ps`
**Solutions:**
```bash
# Increase Docker memory limit
# Edit docker-compose.yml, add:
services:
skill-seekers:
mem_limit: 4g
memswap_limit: 4g
# Or use streaming for large docs
docker-compose run skill-seekers \
skill-seekers scrape --config /configs/react.json --streaming
```
### Issue: Vector Database Connection Failed
**Symptoms:**
- Cannot connect to Weaviate/Qdrant/Chroma
- Connection refused errors
**Solutions:**
```bash
# Check if services are running
docker-compose ps
# Test connectivity
docker-compose exec skill-seekers curl http://weaviate:8080
docker-compose exec skill-seekers curl http://qdrant:6333
docker-compose exec skill-seekers curl http://chroma:8000
# Restart services
docker-compose restart weaviate qdrant chroma
```
### Issue: Slow Performance
**Symptoms:**
- Long scraping times
- Slow container startup
**Solutions:**
```bash
# Use smaller image
docker pull skill-seekers:slim
# Enable BuildKit cache
export DOCKER_BUILDKIT=1
docker build -t skill-seekers:local .
# Increase CPU allocation
docker-compose up -d --scale skill-seekers=1 --cpu-shares=2048
```
---
## Production Deployment
### Security Hardening
1. **Use secrets management**
```bash
# Docker secrets (Swarm mode)
echo "sk-ant-your-key" | docker secret create anthropic_key -
# Kubernetes secrets
kubectl create secret generic skill-seekers-secrets \
--from-literal=anthropic-api-key=sk-ant-your-key
```
2. **Run as non-root**
```dockerfile
# Already configured in Dockerfile
USER skillseeker # UID 1000
```
3. **Read-only filesystems**
```yaml
# docker-compose.yml
services:
mcp-server:
read_only: true
tmpfs:
- /tmp
```
4. **Resource limits**
```yaml
services:
mcp-server:
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
reservations:
cpus: '0.5'
memory: 512M
```
### Monitoring
1. **Health checks**
```bash
# Check all services
docker-compose ps
# Detailed health status
docker inspect --format='{{.State.Health.Status}}' skill-seekers-mcp
```
2. **Logs**
```bash
# Stream logs
docker-compose logs -f --tail=100
# Export logs
docker-compose logs > skill-seekers-logs.txt
```
3. **Metrics**
```bash
# Resource usage
docker stats
# Container inspect
docker-compose exec mcp-server ps aux
docker-compose exec mcp-server df -h
```
### Scaling
1. **Horizontal scaling**
```bash
# Scale MCP servers
docker-compose up -d --scale mcp-server=3
# Use load balancer
# Add nginx/haproxy in docker-compose.yml
```
2. **Vertical scaling**
```yaml
# Increase resources
services:
mcp-server:
deploy:
resources:
limits:
cpus: '4.0'
memory: 8G
```
---
## Best Practices
### 1. Use Multi-Stage Builds
✅ Already implemented in Dockerfile
- Builder stage for dependencies
- Runtime stage for production
### 2. Minimize Image Size
- Use slim base images
- Clean up apt cache
- Remove unnecessary files via .dockerignore
### 3. Security
- Run as non-root user (UID 1000)
- Use secrets for sensitive data
- Keep images updated
### 4. Persistence
- Use named volumes for databases
- Mount ./output for generated skills
- Regular backups of vector DB data
### 5. Monitoring
- Enable health checks
- Stream logs to external service
- Monitor resource usage
---
## Additional Resources
- [Docker Documentation](https://docs.docker.com/)
- [Docker Compose Reference](https://docs.docker.com/compose/compose-file/)
- [Skill Seekers Documentation](https://skillseekersweb.com/)
- [MCP Server Setup](docs/MCP_SETUP.md)
- [Vector Database Integration](docs/strategy/WEEK2_COMPLETE.md)
---
**Last Updated:** February 7, 2026
**Docker Version:** 20.10+
**Compose Version:** 2.0+

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@@ -1,7 +1,7 @@
# Frequently Asked Questions (FAQ) # Frequently Asked Questions (FAQ)
**Version:** 2.7.0 **Version:** 3.1.0-dev
**Last Updated:** 2026-01-18 **Last Updated:** 2026-02-18
--- ---
@@ -9,7 +9,7 @@
### What is Skill Seekers? ### What is Skill Seekers?
Skill Seekers is a Python tool that converts documentation websites, GitHub repositories, and PDF files into AI skills for Claude AI, Google Gemini, OpenAI ChatGPT, and generic Markdown format. Skill Seekers is a Python tool that converts documentation websites, GitHub repositories, and PDF files into AI-ready formats for 16+ platforms: LLM platforms (Claude, Gemini, OpenAI), RAG frameworks (LangChain, LlamaIndex, Haystack), vector databases (ChromaDB, FAISS, Weaviate, Qdrant, Pinecone), and AI coding assistants (Cursor, Windsurf, Cline, Continue.dev).
**Use Cases:** **Use Cases:**
- Create custom documentation skills for your favorite frameworks - Create custom documentation skills for your favorite frameworks
@@ -19,12 +19,32 @@ Skill Seekers is a Python tool that converts documentation websites, GitHub repo
### Which platforms are supported? ### Which platforms are supported?
**Supported Platforms (4):** **Supported Platforms (16+):**
*LLM Platforms:*
1. **Claude AI** - ZIP format with YAML frontmatter 1. **Claude AI** - ZIP format with YAML frontmatter
2. **Google Gemini** - tar.gz format for Grounded Generation 2. **Google Gemini** - tar.gz format for Grounded Generation
3. **OpenAI ChatGPT** - ZIP format for Vector Stores 3. **OpenAI ChatGPT** - ZIP format for Vector Stores
4. **Generic Markdown** - ZIP format with markdown files 4. **Generic Markdown** - ZIP format with markdown files
*RAG Frameworks:*
5. **LangChain** - Document objects for QA chains and agents
6. **LlamaIndex** - TextNodes for query engines
7. **Haystack** - Document objects for enterprise RAG
*Vector Databases:*
8. **ChromaDB** - Direct collection upload
9. **FAISS** - Index files for local similarity search
10. **Weaviate** - Vector objects with schema creation
11. **Qdrant** - Points with payload indexing
12. **Pinecone** - Ready-to-upsert format
*AI Coding Assistants:*
13. **Cursor** - .cursorrules persistent context
14. **Windsurf** - .windsurfrules AI coding rules
15. **Cline** - .clinerules + MCP integration
16. **Continue.dev** - HTTP context server (all IDEs)
Each platform has a dedicated adaptor for optimal formatting and upload. Each platform has a dedicated adaptor for optimal formatting and upload.
### Is it free to use? ### Is it free to use?
@@ -472,16 +492,20 @@ skill-seekers-mcp --transport http --port 8765
### What MCP tools are available? ### What MCP tools are available?
**18 MCP tools:** **26 MCP tools:**
*Core Tools (9):*
1. `list_configs` - List preset configurations 1. `list_configs` - List preset configurations
2. `generate_config` - Generate config from docs URL 2. `generate_config` - Generate config from docs URL
3. `validate_config` - Validate config structure 3. `validate_config` - Validate config structure
4. `estimate_pages` - Estimate page count 4. `estimate_pages` - Estimate page count
5. `scrape_docs` - Scrape documentation 5. `scrape_docs` - Scrape documentation
6. `package_skill` - Package to .zip 6. `package_skill` - Package to .zip (supports `--format` and `--target`)
7. `upload_skill` - Upload to platform 7. `upload_skill` - Upload to platform (supports `--target`)
8. `enhance_skill` - AI enhancement 8. `enhance_skill` - AI enhancement
9. `install_skill` - Complete workflow 9. `install_skill` - Complete workflow
*Extended Tools (10):*
10. `scrape_github` - GitHub analysis 10. `scrape_github` - GitHub analysis
11. `scrape_pdf` - PDF extraction 11. `scrape_pdf` - PDF extraction
12. `unified_scrape` - Multi-source scraping 12. `unified_scrape` - Multi-source scraping
@@ -491,6 +515,18 @@ skill-seekers-mcp --transport http --port 8765
16. `generate_router` - Generate router skills 16. `generate_router` - Generate router skills
17. `add_config_source` - Register git repos 17. `add_config_source` - Register git repos
18. `fetch_config` - Fetch configs from git 18. `fetch_config` - Fetch configs from git
19. `list_config_sources` - List registered sources
20. `remove_config_source` - Remove config source
*Vector DB Tools (4):*
21. `export_to_chroma` - Export to ChromaDB
22. `export_to_weaviate` - Export to Weaviate
23. `export_to_faiss` - Export to FAISS
24. `export_to_qdrant` - Export to Qdrant
*Cloud Tools (3):*
25. `cloud_upload` - Upload to S3/GCS/Azure
26. `cloud_download` - Download from cloud storage
### How do I configure MCP for Claude Code? ### How do I configure MCP for Claude Code?
@@ -650,6 +686,6 @@ Yes!
--- ---
**Version:** 2.7.0 **Version:** 3.1.0-dev
**Last Updated:** 2026-01-18 **Last Updated:** 2026-02-18
**Questions? Ask on [GitHub Discussions](https://github.com/yusufkaraaslan/Skill_Seekers/discussions)** **Questions? Ask on [GitHub Discussions](https://github.com/yusufkaraaslan/Skill_Seekers/discussions)**

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# Kubernetes Deployment Guide
Complete guide for deploying Skill Seekers to Kubernetes using Helm charts.
## Table of Contents
- [Prerequisites](#prerequisites)
- [Quick Start](#quick-start)
- [Installation Methods](#installation-methods)
- [Configuration](#configuration)
- [Accessing Services](#accessing-services)
- [Scaling](#scaling)
- [Persistence](#persistence)
- [Vector Databases](#vector-databases)
- [Security](#security)
- [Monitoring](#monitoring)
- [Troubleshooting](#troubleshooting)
- [Production Best Practices](#production-best-practices)
## Prerequisites
### Required
- Kubernetes cluster (1.23+)
- Helm 3.8+
- kubectl configured for your cluster
- 20GB+ available storage (for persistence)
### Recommended
- Ingress controller (nginx, traefik)
- cert-manager (for TLS certificates)
- Prometheus operator (for monitoring)
- Persistent storage provisioner
### Cluster Resource Requirements
**Minimum (Development):**
- 2 CPU cores
- 8GB RAM
- 20GB storage
**Recommended (Production):**
- 8+ CPU cores
- 32GB+ RAM
- 200GB+ storage (persistent volumes)
## Quick Start
### 1. Add Helm Repository (if published)
```bash
# Add Helm repo
helm repo add skill-seekers https://yourusername.github.io/skill-seekers
helm repo update
# Install with default values
helm install my-skill-seekers skill-seekers/skill-seekers \
--create-namespace \
--namespace skill-seekers
```
### 2. Install from Local Chart
```bash
# Clone repository
git clone https://github.com/yourusername/skill-seekers.git
cd skill-seekers
# Install chart
helm install my-skill-seekers ./helm/skill-seekers \
--create-namespace \
--namespace skill-seekers
```
### 3. Quick Test
```bash
# Port-forward MCP server
kubectl port-forward -n skill-seekers svc/my-skill-seekers-mcp 8765:8765
# Test health endpoint
curl http://localhost:8765/health
# Expected response: {"status": "ok"}
```
## Installation Methods
### Method 1: Minimal Installation (Testing)
Smallest deployment for testing - no persistence, no vector databases.
```bash
helm install my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--create-namespace \
--set persistence.enabled=false \
--set vectorDatabases.weaviate.enabled=false \
--set vectorDatabases.qdrant.enabled=false \
--set vectorDatabases.chroma.enabled=false \
--set mcpServer.replicaCount=1 \
--set mcpServer.autoscaling.enabled=false
```
### Method 2: Development Installation
Moderate resources with persistence for local development.
```bash
helm install my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--create-namespace \
--set persistence.data.size=5Gi \
--set persistence.output.size=10Gi \
--set vectorDatabases.weaviate.persistence.size=20Gi \
--set mcpServer.replicaCount=1 \
--set secrets.anthropicApiKey="sk-ant-..."
```
### Method 3: Production Installation
Full production deployment with autoscaling, persistence, and all vector databases.
```bash
helm install my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--create-namespace \
--values production-values.yaml
```
**production-values.yaml:**
```yaml
global:
environment: production
mcpServer:
enabled: true
replicaCount: 3
autoscaling:
enabled: true
minReplicas: 3
maxReplicas: 20
targetCPUUtilizationPercentage: 70
resources:
limits:
cpu: 2000m
memory: 4Gi
requests:
cpu: 500m
memory: 1Gi
persistence:
data:
size: 20Gi
storageClass: "fast-ssd"
output:
size: 50Gi
storageClass: "fast-ssd"
vectorDatabases:
weaviate:
enabled: true
persistence:
size: 100Gi
storageClass: "fast-ssd"
qdrant:
enabled: true
persistence:
size: 100Gi
storageClass: "fast-ssd"
chroma:
enabled: true
persistence:
size: 50Gi
storageClass: "fast-ssd"
ingress:
enabled: true
className: nginx
annotations:
cert-manager.io/cluster-issuer: "letsencrypt-prod"
nginx.ingress.kubernetes.io/ssl-redirect: "true"
hosts:
- host: skill-seekers.example.com
paths:
- path: /mcp
pathType: Prefix
backend:
service:
name: mcp
port: 8765
tls:
- secretName: skill-seekers-tls
hosts:
- skill-seekers.example.com
secrets:
anthropicApiKey: "sk-ant-..."
googleApiKey: ""
openaiApiKey: ""
githubToken: ""
```
### Method 4: Custom Values Installation
```bash
# Create custom values
cat > my-values.yaml <<EOF
mcpServer:
replicaCount: 2
resources:
requests:
cpu: 1000m
memory: 2Gi
secrets:
anthropicApiKey: "sk-ant-..."
EOF
# Install with custom values
helm install my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--create-namespace \
--values my-values.yaml
```
## Configuration
### API Keys and Secrets
**Option 1: Via Helm values (NOT recommended for production)**
```bash
helm install my-skill-seekers ./helm/skill-seekers \
--set secrets.anthropicApiKey="sk-ant-..." \
--set secrets.githubToken="ghp_..."
```
**Option 2: Create Secret first (Recommended)**
```bash
# Create secret
kubectl create secret generic skill-seekers-secrets \
--from-literal=ANTHROPIC_API_KEY="sk-ant-..." \
--from-literal=GITHUB_TOKEN="ghp_..." \
--namespace skill-seekers
# Reference in values
# (Chart already uses the secret name pattern)
helm install my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers
```
**Option 3: External Secrets Operator**
```yaml
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
name: skill-seekers-secrets
namespace: skill-seekers
spec:
secretStoreRef:
name: aws-secrets-manager
kind: SecretStore
target:
name: skill-seekers-secrets
data:
- secretKey: ANTHROPIC_API_KEY
remoteRef:
key: skill-seekers/anthropic-api-key
```
### Environment Variables
Customize via ConfigMap values:
```yaml
env:
MCP_TRANSPORT: "http"
MCP_PORT: "8765"
PYTHONUNBUFFERED: "1"
CUSTOM_VAR: "value"
```
### Resource Limits
**Development:**
```yaml
mcpServer:
resources:
limits:
cpu: 1000m
memory: 2Gi
requests:
cpu: 250m
memory: 512Mi
```
**Production:**
```yaml
mcpServer:
resources:
limits:
cpu: 4000m
memory: 8Gi
requests:
cpu: 1000m
memory: 2Gi
```
## Accessing Services
### Port Forwarding (Development)
```bash
# MCP Server
kubectl port-forward -n skill-seekers svc/my-skill-seekers-mcp 8765:8765
# Weaviate
kubectl port-forward -n skill-seekers svc/my-skill-seekers-weaviate 8080:8080
# Qdrant
kubectl port-forward -n skill-seekers svc/my-skill-seekers-qdrant 6333:6333
# Chroma
kubectl port-forward -n skill-seekers svc/my-skill-seekers-chroma 8000:8000
```
### Via LoadBalancer
```yaml
mcpServer:
service:
type: LoadBalancer
```
Get external IP:
```bash
kubectl get svc -n skill-seekers my-skill-seekers-mcp
```
### Via Ingress (Production)
```yaml
ingress:
enabled: true
className: nginx
hosts:
- host: skill-seekers.example.com
paths:
- path: /mcp
pathType: Prefix
backend:
service:
name: mcp
port: 8765
```
Access at: `https://skill-seekers.example.com/mcp`
## Scaling
### Manual Scaling
```bash
# Scale MCP server
kubectl scale deployment -n skill-seekers my-skill-seekers-mcp --replicas=5
# Scale Weaviate
kubectl scale deployment -n skill-seekers my-skill-seekers-weaviate --replicas=3
```
### Horizontal Pod Autoscaler
Enabled by default for MCP server:
```yaml
mcpServer:
autoscaling:
enabled: true
minReplicas: 2
maxReplicas: 10
targetCPUUtilizationPercentage: 70
targetMemoryUtilizationPercentage: 80
```
Monitor HPA:
```bash
kubectl get hpa -n skill-seekers
kubectl describe hpa -n skill-seekers my-skill-seekers-mcp
```
### Vertical Scaling
Update resource requests/limits:
```bash
helm upgrade my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--set mcpServer.resources.requests.cpu=2000m \
--set mcpServer.resources.requests.memory=4Gi \
--reuse-values
```
## Persistence
### Storage Classes
Specify storage class for different workloads:
```yaml
persistence:
data:
storageClass: "fast-ssd" # Frequently accessed
output:
storageClass: "standard" # Archive storage
configs:
storageClass: "fast-ssd" # Configuration files
```
### PVC Management
```bash
# List PVCs
kubectl get pvc -n skill-seekers
# Expand PVC (if storage class supports it)
kubectl patch pvc my-skill-seekers-data \
-n skill-seekers \
-p '{"spec":{"resources":{"requests":{"storage":"50Gi"}}}}'
# View PVC details
kubectl describe pvc -n skill-seekers my-skill-seekers-data
```
### Backup and Restore
**Backup:**
```bash
# Using Velero
velero backup create skill-seekers-backup \
--include-namespaces skill-seekers
# Manual backup (example with data PVC)
kubectl exec -n skill-seekers deployment/my-skill-seekers-mcp -- \
tar czf - /data | \
cat > skill-seekers-data-backup.tar.gz
```
**Restore:**
```bash
# Using Velero
velero restore create --from-backup skill-seekers-backup
# Manual restore
kubectl exec -i -n skill-seekers deployment/my-skill-seekers-mcp -- \
tar xzf - -C /data < skill-seekers-data-backup.tar.gz
```
## Vector Databases
### Weaviate
**Access:**
```bash
kubectl port-forward -n skill-seekers svc/my-skill-seekers-weaviate 8080:8080
```
**Query:**
```bash
curl http://localhost:8080/v1/schema
```
### Qdrant
**Access:**
```bash
# HTTP API
kubectl port-forward -n skill-seekers svc/my-skill-seekers-qdrant 6333:6333
# gRPC
kubectl port-forward -n skill-seekers svc/my-skill-seekers-qdrant 6334:6334
```
**Query:**
```bash
curl http://localhost:6333/collections
```
### Chroma
**Access:**
```bash
kubectl port-forward -n skill-seekers svc/my-skill-seekers-chroma 8000:8000
```
**Query:**
```bash
curl http://localhost:8000/api/v1/collections
```
### Disable Vector Databases
To disable individual vector databases:
```yaml
vectorDatabases:
weaviate:
enabled: false
qdrant:
enabled: false
chroma:
enabled: false
```
## Security
### Pod Security Context
Runs as non-root user (UID 1000):
```yaml
podSecurityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 1000
securityContext:
capabilities:
drop:
- ALL
readOnlyRootFilesystem: false
allowPrivilegeEscalation: false
```
### Network Policies
Create network policies for isolation:
```yaml
networkPolicy:
enabled: true
policyTypes:
- Ingress
- Egress
ingress:
- from:
- namespaceSelector:
matchLabels:
name: ingress-nginx
egress:
- to:
- namespaceSelector: {}
```
### RBAC
Enable RBAC with minimal permissions:
```yaml
rbac:
create: true
rules:
- apiGroups: [""]
resources: ["configmaps", "secrets"]
verbs: ["get", "list"]
```
### Secrets Management
**Best Practices:**
1. Never commit secrets to git
2. Use external secret managers (AWS Secrets Manager, HashiCorp Vault)
3. Enable encryption at rest in Kubernetes
4. Rotate secrets regularly
**Example with Sealed Secrets:**
```bash
# Create sealed secret
kubectl create secret generic skill-seekers-secrets \
--from-literal=ANTHROPIC_API_KEY="sk-ant-..." \
--dry-run=client -o yaml | \
kubeseal -o yaml > sealed-secret.yaml
# Apply sealed secret
kubectl apply -f sealed-secret.yaml -n skill-seekers
```
## Monitoring
### Pod Metrics
```bash
# View pod status
kubectl get pods -n skill-seekers
# View pod metrics (requires metrics-server)
kubectl top pods -n skill-seekers
# View pod logs
kubectl logs -n skill-seekers -l app.kubernetes.io/component=mcp-server --tail=100 -f
```
### Prometheus Integration
Enable ServiceMonitor (requires Prometheus Operator):
```yaml
serviceMonitor:
enabled: true
interval: 30s
scrapeTimeout: 10s
labels:
prometheus: kube-prometheus
```
### Grafana Dashboards
Import dashboard JSON from `helm/skill-seekers/dashboards/`.
### Health Checks
MCP server has built-in health checks:
```yaml
livenessProbe:
httpGet:
path: /health
port: 8765
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /health
port: 8765
initialDelaySeconds: 10
periodSeconds: 5
```
Test manually:
```bash
kubectl exec -n skill-seekers deployment/my-skill-seekers-mcp -- \
curl http://localhost:8765/health
```
## Troubleshooting
### Pods Not Starting
```bash
# Check pod status
kubectl get pods -n skill-seekers
# View events
kubectl get events -n skill-seekers --sort-by='.lastTimestamp'
# Describe pod
kubectl describe pod -n skill-seekers <pod-name>
# Check logs
kubectl logs -n skill-seekers <pod-name>
```
### Common Issues
**Issue: ImagePullBackOff**
```bash
# Check image pull secrets
kubectl get secrets -n skill-seekers
# Verify image exists
docker pull <image-name>
```
**Issue: CrashLoopBackOff**
```bash
# View recent logs
kubectl logs -n skill-seekers <pod-name> --previous
# Check environment variables
kubectl exec -n skill-seekers <pod-name> -- env
```
**Issue: PVC Pending**
```bash
# Check storage class
kubectl get storageclass
# View PVC events
kubectl describe pvc -n skill-seekers <pvc-name>
# Check if provisioner is running
kubectl get pods -n kube-system | grep provisioner
```
**Issue: API Key Not Working**
```bash
# Verify secret exists
kubectl get secret -n skill-seekers my-skill-seekers
# Check secret contents (base64 encoded)
kubectl get secret -n skill-seekers my-skill-seekers -o yaml
# Test API key manually
kubectl exec -n skill-seekers deployment/my-skill-seekers-mcp -- \
env | grep ANTHROPIC
```
### Debug Container
Run debug container in same namespace:
```bash
kubectl run debug -n skill-seekers --rm -it \
--image=nicolaka/netshoot \
--restart=Never -- bash
# Inside debug container:
# Test MCP server connectivity
curl http://my-skill-seekers-mcp:8765/health
# Test vector database connectivity
curl http://my-skill-seekers-weaviate:8080/v1/.well-known/ready
```
## Production Best Practices
### 1. Resource Planning
**Capacity Planning:**
- MCP Server: 500m CPU + 1Gi RAM per 10 concurrent requests
- Vector DBs: 2GB RAM + 10GB storage per 100K documents
- Reserve 30% overhead for spikes
**Example Production Setup:**
```yaml
mcpServer:
replicaCount: 5 # Handle 50 concurrent requests
resources:
requests:
cpu: 2500m
memory: 5Gi
autoscaling:
minReplicas: 5
maxReplicas: 20
```
### 2. High Availability
**Anti-Affinity Rules:**
```yaml
mcpServer:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app.kubernetes.io/component
operator: In
values:
- mcp-server
topologyKey: kubernetes.io/hostname
```
**Multiple Replicas:**
- MCP Server: 3+ replicas across different nodes
- Vector DBs: 2+ replicas with replication
### 3. Monitoring and Alerting
**Key Metrics to Monitor:**
- Pod restart count (> 5 per hour = critical)
- Memory usage (> 90% = warning)
- CPU throttling (> 50% = investigate)
- Request latency (p95 > 1s = warning)
- Error rate (> 1% = critical)
**Prometheus Alerts:**
```yaml
- alert: HighPodRestarts
expr: rate(kube_pod_container_status_restarts_total{namespace="skill-seekers"}[15m]) > 0.1
for: 5m
labels:
severity: warning
```
### 4. Backup Strategy
**Automated Backups:**
```yaml
# CronJob for daily backups
apiVersion: batch/v1
kind: CronJob
metadata:
name: skill-seekers-backup
spec:
schedule: "0 2 * * *" # 2 AM daily
jobTemplate:
spec:
template:
spec:
containers:
- name: backup
image: skill-seekers:latest
command:
- /bin/sh
- -c
- tar czf /backup/data-$(date +%Y%m%d).tar.gz /data
```
### 5. Security Hardening
**Security Checklist:**
- [ ] Enable Pod Security Standards
- [ ] Use Network Policies
- [ ] Enable RBAC with least privilege
- [ ] Rotate secrets every 90 days
- [ ] Scan images for vulnerabilities
- [ ] Enable audit logging
- [ ] Use private container registry
- [ ] Enable encryption at rest
### 6. Cost Optimization
**Strategies:**
- Use spot/preemptible instances for non-critical workloads
- Enable cluster autoscaler
- Right-size resource requests
- Use storage tiering (hot/warm/cold)
- Schedule downscaling during off-hours
**Example Cost Optimization:**
```yaml
# Development environment: downscale at night
# Create CronJob to scale down replicas
apiVersion: batch/v1
kind: CronJob
metadata:
name: downscale-dev
spec:
schedule: "0 20 * * *" # 8 PM
jobTemplate:
spec:
template:
spec:
serviceAccountName: scaler
containers:
- name: kubectl
image: bitnami/kubectl
command:
- kubectl
- scale
- deployment
- my-skill-seekers-mcp
- --replicas=1
```
### 7. Update Strategy
**Rolling Updates:**
```yaml
mcpServer:
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
```
**Update Process:**
```bash
# 1. Test in staging
helm upgrade my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers-staging \
--values staging-values.yaml
# 2. Run smoke tests
./scripts/smoke-test.sh
# 3. Deploy to production
helm upgrade my-skill-seekers ./helm/skill-seekers \
--namespace skill-seekers \
--values production-values.yaml
# 4. Monitor for 15 minutes
kubectl rollout status deployment -n skill-seekers my-skill-seekers-mcp
# 5. Rollback if issues
helm rollback my-skill-seekers -n skill-seekers
```
## Upgrade Guide
### Minor Version Upgrade
```bash
# Fetch latest chart
helm repo update
# Upgrade with existing values
helm upgrade my-skill-seekers skill-seekers/skill-seekers \
--namespace skill-seekers \
--reuse-values
```
### Major Version Upgrade
```bash
# Backup current values
helm get values my-skill-seekers -n skill-seekers > backup-values.yaml
# Review CHANGELOG for breaking changes
curl https://raw.githubusercontent.com/yourusername/skill-seekers/main/CHANGELOG.md
# Upgrade with migration steps
helm upgrade my-skill-seekers skill-seekers/skill-seekers \
--namespace skill-seekers \
--values backup-values.yaml \
--force # Only if schema changed
```
## Uninstallation
### Full Cleanup
```bash
# Delete Helm release
helm uninstall my-skill-seekers -n skill-seekers
# Delete PVCs (if you want to remove data)
kubectl delete pvc -n skill-seekers --all
# Delete namespace
kubectl delete namespace skill-seekers
```
### Keep Data
```bash
# Delete release but keep PVCs
helm uninstall my-skill-seekers -n skill-seekers
# PVCs remain for later use
kubectl get pvc -n skill-seekers
```
## Additional Resources
- [Helm Documentation](https://helm.sh/docs/)
- [Kubernetes Documentation](https://kubernetes.io/docs/)
- [Skill Seekers GitHub](https://github.com/yourusername/skill-seekers)
- [Issue Tracker](https://github.com/yourusername/skill-seekers/issues)
---
**Need Help?**
- GitHub Issues: https://github.com/yourusername/skill-seekers/issues
- Documentation: https://skillseekersweb.com
- Community: [Link to Discord/Slack]

6
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View File

@@ -239,7 +239,7 @@ skill-seekers-mcp
skill-seekers-mcp --transport http --port 8765 skill-seekers-mcp --transport http --port 8765
``` ```
### MCP Tools (18 total) ### MCP Tools (26 total)
**Core Tools:** **Core Tools:**
1. `list_configs` - List preset configurations 1. `list_configs` - List preset configurations
@@ -286,7 +286,7 @@ export GITHUB_TOKEN=ghp_...
## Testing ## Testing
```bash ```bash
# Run all tests (1200+) # Run all tests (1,880+)
pytest tests/ -v pytest tests/ -v
# Run with coverage # Run with coverage
@@ -463,4 +463,4 @@ skill-seekers validate-config configs/my-config.json
--- ---
**Version:** 3.1.0-dev | **Test Count:** 1880+ | **Platforms:** Claude, Gemini, OpenAI, Markdown **Version:** 3.1.0-dev | **Test Count:** 1,880+ | **MCP Tools:** 26 | **Platforms:** 16+ (Claude, Gemini, OpenAI, LangChain, LlamaIndex, ChromaDB, FAISS, Cursor, Windsurf, and more)

10
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View File

@@ -1,9 +1,9 @@
# Bootstrap Skill - Self-Hosting (v2.7.0) # Bootstrap Skill - Self-Hosting (v3.1.0-dev)
**Version:** 2.7.0 **Version:** 3.1.0-dev
**Feature:** Bootstrap Skill (Dogfooding) **Feature:** Bootstrap Skill (Dogfooding)
**Status:** ✅ Production Ready **Status:** ✅ Production Ready
**Last Updated:** 2026-01-18 **Last Updated:** 2026-02-18
--- ---
@@ -691,6 +691,6 @@ echo "✅ Validation passed"
--- ---
**Version:** 2.7.0 **Version:** 3.1.0-dev
**Last Updated:** 2026-01-18 **Last Updated:** 2026-02-18
**Status:** ✅ Production Ready **Status:** ✅ Production Ready

422
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View File

@@ -1,422 +0,0 @@
# Task #19 Complete: MCP Server Integration for Vector Databases
**Completion Date:** February 7, 2026
**Status:** ✅ Complete
**Tests:** 8/8 passing
---
## Objective
Extend the MCP server to expose the 4 new vector database adaptors (Weaviate, Chroma, FAISS, Qdrant) as MCP tools, enabling Claude AI assistants to export skills directly to vector databases.
---
## Implementation Summary
### Files Created
1. **src/skill_seekers/mcp/tools/vector_db_tools.py** (500+ lines)
- 4 async implementation functions
- Comprehensive docstrings with examples
- Error handling for missing directories/adaptors
- Usage instructions with code examples
- Links to official documentation
2. **tests/test_mcp_vector_dbs.py** (274 lines)
- 8 comprehensive test cases
- Test fixtures for skill directories
- Validation of exports, error handling, and output format
- All tests passing (8/8)
### Files Modified
1. **src/skill_seekers/mcp/tools/__init__.py**
- Added vector_db_tools module to docstring
- Imported 4 new tool implementations
- Added to __all__ exports
2. **src/skill_seekers/mcp/server_fastmcp.py**
- Updated docstring from "21 tools" to "25 tools"
- Added 6th category: "Vector Database tools"
- Imported 4 new implementations (both try/except blocks)
- Registered 4 new tools with @safe_tool_decorator
- Added VECTOR DATABASE TOOLS section (125 lines)
---
## New MCP Tools
### 1. export_to_weaviate
**Description:** Export skill to Weaviate vector database format (hybrid search, 450K+ users)
**Parameters:**
- `skill_dir` (str): Path to skill directory
- `output_dir` (str, optional): Output directory
**Output:** JSON file with Weaviate schema, objects, and configuration
**Usage Instructions Include:**
- Python code for uploading to Weaviate
- Hybrid search query examples
- Links to Weaviate documentation
---
### 2. export_to_chroma
**Description:** Export skill to Chroma vector database format (local-first, 800K+ developers)
**Parameters:**
- `skill_dir` (str): Path to skill directory
- `output_dir` (str, optional): Output directory
**Output:** JSON file with Chroma collection data
**Usage Instructions Include:**
- Python code for loading into Chroma
- Query collection examples
- Links to Chroma documentation
---
### 3. export_to_faiss
**Description:** Export skill to FAISS vector index format (billion-scale, GPU-accelerated)
**Parameters:**
- `skill_dir` (str): Path to skill directory
- `output_dir` (str, optional): Output directory
**Output:** JSON file with FAISS embeddings, metadata, and index config
**Usage Instructions Include:**
- Python code for building FAISS index (Flat, IVF, HNSW options)
- Search examples
- Index saving/loading
- Links to FAISS documentation
---
### 4. export_to_qdrant
**Description:** Export skill to Qdrant vector database format (native filtering, 100K+ users)
**Parameters:**
- `skill_dir` (str): Path to skill directory
- `output_dir` (str, optional): Output directory
**Output:** JSON file with Qdrant collection data and points
**Usage Instructions Include:**
- Python code for uploading to Qdrant
- Search with filters examples
- Links to Qdrant documentation
---
## Test Coverage
### Test Cases (8/8 passing)
1. **test_export_to_weaviate** - Validates Weaviate export with output verification
2. **test_export_to_chroma** - Validates Chroma export with output verification
3. **test_export_to_faiss** - Validates FAISS export with output verification
4. **test_export_to_qdrant** - Validates Qdrant export with output verification
5. **test_export_with_default_output_dir** - Tests default output directory behavior
6. **test_export_missing_skill_dir** - Validates error handling for missing directories
7. **test_all_exports_create_files** - Validates file creation for all 4 exports
8. **test_export_output_includes_instructions** - Validates usage instructions in output
### Test Results
```
tests/test_mcp_vector_dbs.py::test_export_to_weaviate PASSED
tests/test_mcp_vector_dbs.py::test_export_to_chroma PASSED
tests/test_mcp_vector_dbs.py::test_export_to_faiss PASSED
tests/test_mcp_vector_dbs.py::test_export_to_qdrant PASSED
tests/test_mcp_vector_dbs.py::test_export_with_default_output_dir PASSED
tests/test_mcp_vector_dbs.py::test_export_missing_skill_dir PASSED
tests/test_mcp_vector_dbs.py::test_all_exports_create_files PASSED
tests/test_mcp_vector_dbs.py::test_export_output_includes_instructions PASSED
8 passed in 0.35s
```
---
## Integration Architecture
### MCP Server Structure
```
MCP Server (25 tools, 6 categories)
├── Config tools (3)
├── Scraping tools (8)
├── Packaging tools (4)
├── Splitting tools (2)
├── Source tools (4)
└── Vector Database tools (4) ← NEW
├── export_to_weaviate
├── export_to_chroma
├── export_to_faiss
└── export_to_qdrant
```
### Tool Implementation Pattern
Each tool follows the FastMCP pattern:
```python
@safe_tool_decorator(description="...")
async def export_to_<target>(
skill_dir: str,
output_dir: str | None = None,
) -> str:
"""Tool docstring with args and returns."""
args = {"skill_dir": skill_dir}
if output_dir:
args["output_dir"] = output_dir
result = await export_to_<target>_impl(args)
if isinstance(result, list) and result:
return result[0].text if hasattr(result[0], "text") else str(result[0])
return str(result)
```
---
## Usage Examples
### Claude Desktop MCP Config
```json
{
"mcpServers": {
"skill-seeker": {
"command": "python",
"args": ["-m", "skill_seekers.mcp.server_fastmcp"]
}
}
}
```
### Using Vector Database Tools
**Example 1: Export to Weaviate**
```
export_to_weaviate(
skill_dir="output/react",
output_dir="output"
)
```
**Example 2: Export to Chroma with default output**
```
export_to_chroma(skill_dir="output/django")
```
**Example 3: Export to FAISS**
```
export_to_faiss(
skill_dir="output/fastapi",
output_dir="/tmp/exports"
)
```
**Example 4: Export to Qdrant**
```
export_to_qdrant(skill_dir="output/vue")
```
---
## Output Format Example
Each tool returns comprehensive instructions:
```
✅ Weaviate Export Complete!
📦 Package: react-weaviate.json
📁 Location: output/
📊 Size: 45,678 bytes
🔧 Next Steps:
1. Upload to Weaviate:
```python
import weaviate
import json
client = weaviate.Client("http://localhost:8080")
data = json.load(open("output/react-weaviate.json"))
# Create schema
client.schema.create_class(data["schema"])
# Batch upload objects
with client.batch as batch:
for obj in data["objects"]:
batch.add_data_object(obj["properties"], data["class_name"])
```
2. Query with hybrid search:
```python
result = client.query.get(data["class_name"], ["content", "source"]) \
.with_hybrid("React hooks usage") \
.with_limit(5) \
.do()
```
📚 Resources:
- Weaviate Docs: https://weaviate.io/developers/weaviate
- Hybrid Search: https://weaviate.io/developers/weaviate/search/hybrid
```
---
## Technical Achievements
### 1. Consistent Interface
All 4 tools share the same interface:
- Same parameter structure
- Same error handling pattern
- Same output format (TextContent with detailed instructions)
- Same integration with existing adaptors
### 2. Comprehensive Documentation
Each tool includes:
- Clear docstrings with parameter descriptions
- Usage examples in output
- Python code snippets for uploading
- Query examples for searching
- Links to official documentation
### 3. Robust Error Handling
- Missing skill directory detection
- Adaptor import failure handling
- Graceful fallback for missing dependencies
- Clear error messages with suggestions
### 4. Complete Test Coverage
- 8 test cases covering all scenarios
- Fixture-based test setup for reusability
- Validation of structure, content, and files
- Error case testing
---
## Impact
### MCP Server Expansion
- **Before:** 21 tools across 5 categories
- **After:** 25 tools across 6 categories (+19% growth)
- **New Capability:** Direct vector database export from MCP
### Vector Database Support
- **Weaviate:** Hybrid search (vector + BM25), 450K+ users
- **Chroma:** Local-first development, 800K+ developers
- **FAISS:** Billion-scale search, GPU-accelerated
- **Qdrant:** Native filtering, 100K+ users
### Developer Experience
- Claude AI assistants can now export skills to vector databases directly
- No manual CLI commands needed
- Comprehensive usage instructions included
- Complete end-to-end workflow from scraping to vector database
---
## Integration with Week 2 Adaptors
Task #19 completes the MCP integration of Week 2's vector database adaptors:
| Task | Feature | MCP Integration |
|------|---------|-----------------|
| #10 | Weaviate Adaptor | ✅ export_to_weaviate |
| #11 | Chroma Adaptor | ✅ export_to_chroma |
| #12 | FAISS Adaptor | ✅ export_to_faiss |
| #13 | Qdrant Adaptor | ✅ export_to_qdrant |
---
## Next Steps (Week 3)
With Task #19 complete, Week 3 can begin:
- **Task #20:** GitHub Actions automation
- **Task #21:** Docker deployment
- **Task #22:** Kubernetes Helm charts
- **Task #23:** Multi-cloud storage (S3, GCS, Azure Blob)
- **Task #24:** API server for embedding generation
- **Task #25:** Real-time documentation sync
- **Task #26:** Performance benchmarking suite
- **Task #27:** Production deployment guides
---
## Files Summary
### Created (2 files, ~800 lines)
- `src/skill_seekers/mcp/tools/vector_db_tools.py` (500+ lines)
- `tests/test_mcp_vector_dbs.py` (274 lines)
### Modified (3 files)
- `src/skill_seekers/mcp/tools/__init__.py` (+16 lines)
- `src/skill_seekers/mcp/server_fastmcp.py` (+140 lines)
- (Updated: tool count, imports, new section)
### Total Impact
- **New Lines:** ~800
- **Modified Lines:** ~150
- **Test Coverage:** 8/8 passing
- **New MCP Tools:** 4
- **MCP Tool Count:** 21 → 25
---
## Lessons Learned
### What Worked Well ✅
1. **Consistent patterns** - Following existing MCP tool structure made integration seamless
2. **Comprehensive testing** - 8 test cases caught all edge cases
3. **Clear documentation** - Usage instructions in output reduce support burden
4. **Error handling** - Graceful degradation for missing dependencies
### Challenges Overcome ⚡
1. **Async testing** - Converted to synchronous tests with asyncio.run() wrapper
2. **pytest-asyncio unavailable** - Used run_async() helper for compatibility
3. **Import paths** - Careful CLI_DIR path handling for adaptor access
---
## Quality Metrics
- **Test Pass Rate:** 100% (8/8)
- **Code Coverage:** All new functions tested
- **Documentation:** Complete docstrings and usage examples
- **Integration:** Seamless with existing MCP server
- **Performance:** Tests run in <0.5 seconds
---
**Task #19: MCP Server Integration for Vector Databases - COMPLETE ✅**
**Ready for Week 3 Task #20: GitHub Actions Automation**

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# Task #20 Complete: GitHub Actions Automation Workflows
**Completion Date:** February 7, 2026
**Status:** ✅ Complete
**New Workflows:** 4
---
## Objective
Extend GitHub Actions with automated workflows for Week 2 features, including vector database exports, quality metrics automation, scheduled skill updates, and comprehensive testing infrastructure.
---
## Implementation Summary
Created 4 new GitHub Actions workflows that automate Week 2 features and provide comprehensive CI/CD capabilities for skill generation, quality analysis, and vector database integration.
---
## New Workflows
### 1. Vector Database Export (`vector-db-export.yml`)
**Triggers:**
- Manual (`workflow_dispatch`) with parameters
- Scheduled (weekly on Sundays at 2 AM UTC)
**Features:**
- Matrix strategy for popular frameworks (react, django, godot, fastapi)
- Export to all 4 vector databases (Weaviate, Chroma, FAISS, Qdrant)
- Configurable targets (single, multiple, or all)
- Automatic quality report generation
- Artifact uploads with 30-day retention
- GitHub Step Summary with export results
**Parameters:**
- `skill_name`: Framework to export
- `targets`: Vector databases (comma-separated or "all")
- `config_path`: Optional config file path
**Output:**
- Vector database JSON exports
- Quality metrics report
- Export summary in GitHub UI
**Security:** All inputs accessed via environment variables (safe pattern)
---
### 2. Quality Metrics Dashboard (`quality-metrics.yml`)
**Triggers:**
- Manual (`workflow_dispatch`) with parameters
- Pull requests affecting `output/` or `configs/`
**Features:**
- Automated quality analysis with 4-dimensional scoring
- GitHub annotations (errors, warnings, notices)
- Configurable fail threshold (default: 70/100)
- Automatic PR comments with quality dashboard
- Multi-skill analysis support
- Artifact uploads of detailed reports
**Quality Dimensions:**
1. **Completeness** (30% weight) - SKILL.md, references, metadata
2. **Accuracy** (25% weight) - No TODOs, valid JSON, no placeholders
3. **Coverage** (25% weight) - Getting started, API docs, examples
4. **Health** (20% weight) - No empty files, proper structure
**Output:**
- Quality score with letter grade (A+ to F)
- Component breakdowns
- GitHub annotations on files
- PR comments with dashboard
- Detailed reports as artifacts
**Security:** Workflow_dispatch inputs and PR events only, no untrusted content
---
### 3. Test Vector Database Adaptors (`test-vector-dbs.yml`)
**Triggers:**
- Push to `main` or `development`
- Pull requests
- Manual (`workflow_dispatch`)
- Path filters for adaptor/MCP code
**Features:**
- Matrix testing across 4 adaptors × 2 Python versions (3.10, 3.12)
- Individual adaptor tests
- Integration testing with real packaging
- MCP tool testing
- Week 2 validation script
- Test artifact uploads
- Comprehensive test summary
**Test Jobs:**
1. **test-adaptors** - Tests each adaptor (Weaviate, Chroma, FAISS, Qdrant)
2. **test-mcp-tools** - Tests MCP vector database tools
3. **test-week2-integration** - Full Week 2 feature validation
**Coverage:**
- 4 vector database adaptors
- 8 MCP tools
- 6 Week 2 feature categories
- Python 3.10 and 3.12 compatibility
**Security:** Push/PR/workflow_dispatch only, matrix values are hardcoded constants
---
### 4. Scheduled Skill Updates (`scheduled-updates.yml`)
**Triggers:**
- Scheduled (weekly on Sundays at 3 AM UTC)
- Manual (`workflow_dispatch`) with optional framework filter
**Features:**
- Matrix strategy for 6 popular frameworks
- Incremental updates using change detection (95% faster)
- Full scrape for new skills
- Streaming ingestion for large docs
- Automatic quality report generation
- Claude AI packaging
- Artifact uploads with 90-day retention
- Update summary dashboard
**Supported Frameworks:**
- React
- Django
- FastAPI
- Godot
- Vue
- Flask
**Workflow:**
1. Check if skill exists
2. Incremental update if exists (change detection)
3. Full scrape if new
4. Generate quality metrics
5. Package for Claude AI
6. Upload artifacts
**Parameters:**
- `frameworks`: Comma-separated list or "all" (default: all)
**Security:** Schedule + workflow_dispatch, input accessed via FRAMEWORKS_INPUT env variable
---
## Workflow Integration
### Existing Workflows Enhanced
The new workflows complement existing CI/CD:
| Workflow | Purpose | Integration |
|----------|---------|-------------|
| `tests.yml` | Core testing | Enhanced with Week 2 test runs |
| `release.yml` | PyPI publishing | Now includes quality metrics |
| `vector-db-export.yml` | ✨ NEW - Export automation | |
| `quality-metrics.yml` | ✨ NEW - Quality dashboard | |
| `test-vector-dbs.yml` | ✨ NEW - Week 2 testing | |
| `scheduled-updates.yml` | ✨ NEW - Auto-refresh | |
### Workflow Relationships
```
tests.yml (Core CI)
└─> test-vector-dbs.yml (Week 2 specific)
└─> quality-metrics.yml (Quality gates)
scheduled-updates.yml (Weekly refresh)
└─> vector-db-export.yml (Export to vector DBs)
└─> quality-metrics.yml (Quality check)
Pull Request
└─> tests.yml + quality-metrics.yml (PR validation)
```
---
## Features & Benefits
### 1. Automation
**Before Task #20:**
- Manual vector database exports
- Manual quality checks
- No automated skill updates
- Limited CI/CD for Week 2 features
**After Task #20:**
- ✅ Automated weekly exports to 4 vector databases
- ✅ Automated quality analysis with PR comments
- ✅ Automated skill refresh for 6 frameworks
- ✅ Comprehensive Week 2 feature testing
### 2. Quality Gates
**PR Quality Checks:**
1. Code quality (ruff, mypy) - `tests.yml`
2. Unit tests (pytest) - `tests.yml`
3. Vector DB tests - `test-vector-dbs.yml`
4. Quality metrics - `quality-metrics.yml`
**Release Quality:**
1. All tests pass
2. Quality score ≥ 70/100
3. Vector DB exports successful
4. MCP tools validated
### 3. Continuous Delivery
**Weekly Automation:**
- Sunday 2 AM: Vector DB exports (`vector-db-export.yml`)
- Sunday 3 AM: Skill updates (`scheduled-updates.yml`)
**On-Demand:**
- Manual triggers for all workflows
- Custom framework selection
- Configurable quality thresholds
- Selective vector database exports
---
## Security Measures
All workflows follow GitHub Actions security best practices:
### ✅ Safe Input Handling
1. **Environment Variables:** All inputs accessed via `env:` section
2. **No Direct Interpolation:** Never use `${{ github.event.* }}` in `run:` commands
3. **Quoted Variables:** All shell variables properly quoted
4. **Controlled Triggers:** Only `workflow_dispatch`, `schedule`, `push`, `pull_request`
### ❌ Avoided Patterns
- No `github.event.issue.title/body` usage
- No `github.event.comment.body` in run commands
- No `github.event.pull_request.head.ref` direct usage
- No untrusted commit messages in commands
### Security Documentation
Each workflow includes security comment header:
```yaml
# Security Note: This workflow uses [trigger types].
# All inputs accessed via environment variables (safe pattern).
```
---
## Usage Examples
### Manual Vector Database Export
```bash
# Export React skill to all vector databases
gh workflow run vector-db-export.yml \
-f skill_name=react \
-f targets=all
# Export Django to specific databases
gh workflow run vector-db-export.yml \
-f skill_name=django \
-f targets=weaviate,chroma
```
### Quality Analysis
```bash
# Analyze specific skill
gh workflow run quality-metrics.yml \
-f skill_dir=output/react \
-f fail_threshold=80
# On PR: Automatically triggered
# (no manual invocation needed)
```
### Scheduled Updates
```bash
# Update specific frameworks
gh workflow run scheduled-updates.yml \
-f frameworks=react,django
# Weekly automatic updates
# (runs every Sunday at 3 AM UTC)
```
### Vector DB Testing
```bash
# Manual test run
gh workflow run test-vector-dbs.yml
# Automatic on push/PR
# (triggered by adaptor code changes)
```
---
## Artifacts & Outputs
### Artifact Types
1. **Vector Database Exports** (30-day retention)
- `{skill}-vector-exports` - All 4 JSON files
- Format: `{skill}-{target}.json`
2. **Quality Reports** (30-day retention)
- `{skill}-quality-report` - Detailed analysis
- `quality-metrics-reports` - All reports
3. **Updated Skills** (90-day retention)
- `{framework}-skill-updated` - Refreshed skill ZIPs
- Claude AI ready packages
4. **Test Packages** (7-day retention)
- `test-package-{adaptor}-py{version}` - Test exports
### GitHub UI Integration
**Step Summaries:**
- Export results with file sizes
- Quality dashboard with grades
- Test results matrix
- Update status for frameworks
**PR Comments:**
- Quality metrics dashboard
- Threshold pass/fail status
- Recommendations for improvement
**Annotations:**
- Errors: Quality < threshold
- Warnings: Quality < 80
- Notices: Quality ≥ 80
---
## Performance Metrics
### Workflow Execution Times
| Workflow | Duration | Frequency |
|----------|----------|-----------|
| vector-db-export.yml | 5-10 min/skill | Weekly + manual |
| quality-metrics.yml | 1-2 min/skill | PR + manual |
| test-vector-dbs.yml | 8-12 min | Push/PR |
| scheduled-updates.yml | 10-15 min/framework | Weekly |
### Resource Usage
- **Concurrency:** Matrix strategies for parallelization
- **Caching:** pip cache for dependencies
- **Artifacts:** Compressed with retention policies
- **Storage:** ~500MB/week for all workflows
---
## Integration with Week 2 Features
Task #20 workflows integrate all Week 2 capabilities:
| Week 2 Feature | Workflow Integration |
|----------------|---------------------|
| **Weaviate Adaptor** | `vector-db-export.yml`, `test-vector-dbs.yml` |
| **Chroma Adaptor** | `vector-db-export.yml`, `test-vector-dbs.yml` |
| **FAISS Adaptor** | `vector-db-export.yml`, `test-vector-dbs.yml` |
| **Qdrant Adaptor** | `vector-db-export.yml`, `test-vector-dbs.yml` |
| **Streaming Ingestion** | `scheduled-updates.yml` |
| **Incremental Updates** | `scheduled-updates.yml` |
| **Multi-Language** | All workflows (language detection) |
| **Embedding Pipeline** | `vector-db-export.yml` |
| **Quality Metrics** | `quality-metrics.yml` |
| **MCP Integration** | `test-vector-dbs.yml` |
---
## Next Steps (Week 3 Remaining)
With Task #20 complete, continue Week 3 automation:
- **Task #21:** Docker deployment
- **Task #22:** Kubernetes Helm charts
- **Task #23:** Multi-cloud storage (S3, GCS, Azure)
- **Task #24:** API server for embedding generation
- **Task #25:** Real-time documentation sync
- **Task #26:** Performance benchmarking suite
- **Task #27:** Production deployment guides
---
## Files Created
### GitHub Actions Workflows (4 files)
1. `.github/workflows/vector-db-export.yml` (220 lines)
2. `.github/workflows/quality-metrics.yml` (180 lines)
3. `.github/workflows/test-vector-dbs.yml` (140 lines)
4. `.github/workflows/scheduled-updates.yml` (200 lines)
### Total Impact
- **New Files:** 4 workflows (~740 lines)
- **Enhanced Workflows:** 2 (tests.yml, release.yml)
- **Automation Coverage:** 10 Week 2 features
- **CI/CD Maturity:** Basic → Advanced
---
## Quality Improvements
### CI/CD Coverage
- **Before:** 2 workflows (tests, release)
- **After:** 6 workflows (+4 new)
- **Automation:** Manual → Automated
- **Frequency:** On-demand → Scheduled
### Developer Experience
- **Quality Feedback:** Manual → Automated PR comments
- **Vector DB Export:** CLI → GitHub Actions
- **Skill Updates:** Manual → Weekly automatic
- **Testing:** Basic → Comprehensive matrix
---
**Task #20: GitHub Actions Automation Workflows - COMPLETE ✅**
**Week 3 Progress:** 1/8 tasks complete
**Ready for Task #21:** Docker Deployment

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# Task #21 Complete: Docker Deployment Infrastructure
**Completion Date:** February 7, 2026
**Status:** ✅ Complete
**Deliverables:** 6 files
---
## Objective
Create comprehensive Docker deployment infrastructure including multi-stage builds, Docker Compose orchestration, vector database integration, CI/CD automation, and production-ready documentation.
---
## Deliverables
### 1. Dockerfile (Main CLI)
**File:** `Dockerfile` (70 lines)
**Features:**
- Multi-stage build (builder + runtime)
- Python 3.12 slim base
- Non-root user (UID 1000)
- Health checks
- Volume mounts for data/configs/output
- MCP server port exposed (8765)
- Image size optimization
**Image Size:** ~400MB
**Platforms:** linux/amd64, linux/arm64
### 2. Dockerfile.mcp (MCP Server)
**File:** `Dockerfile.mcp` (65 lines)
**Features:**
- Specialized for MCP server deployment
- HTTP mode by default (--transport http)
- Health check endpoint
- Non-root user
- Environment configuration
- Volume persistence
**Image Size:** ~450MB
**Platforms:** linux/amd64, linux/arm64
### 3. Docker Compose
**File:** `docker-compose.yml` (120 lines)
**Services:**
1. **skill-seekers** - CLI application
2. **mcp-server** - MCP server (port 8765)
3. **weaviate** - Vector DB (port 8080)
4. **qdrant** - Vector DB (ports 6333/6334)
5. **chroma** - Vector DB (port 8000)
**Features:**
- Service orchestration
- Named volumes for persistence
- Network isolation
- Health checks
- Environment variable configuration
- Auto-restart policies
### 4. Docker Ignore
**File:** `.dockerignore` (80 lines)
**Optimizations:**
- Excludes tests, docs, IDE files
- Reduces build context size
- Faster build times
- Smaller image sizes
### 5. Environment Configuration
**File:** `.env.example` (40 lines)
**Variables:**
- API keys (Anthropic, Google, OpenAI)
- GitHub token
- MCP server configuration
- Resource limits
- Vector database ports
- Logging configuration
### 6. Comprehensive Documentation
**File:** `docs/DOCKER_GUIDE.md` (650+ lines)
**Sections:**
- Quick start guide
- Available images
- Service architecture
- Common use cases
- Volume management
- Environment variables
- Building locally
- Troubleshooting
- Production deployment
- Security hardening
- Monitoring & scaling
- Best practices
### 7. CI/CD Automation
**File:** `.github/workflows/docker-publish.yml` (130 lines)
**Features:**
- Automated builds on push/tag/PR
- Multi-platform builds (amd64 + arm64)
- Docker Hub publishing
- Image testing
- Metadata extraction
- Build caching (GitHub Actions cache)
- Docker Compose validation
---
## Key Features
### Multi-Stage Builds
**Stage 1: Builder**
- Install build dependencies
- Build Python packages
- Install all dependencies
**Stage 2: Runtime**
- Minimal production image
- Copy only runtime artifacts
- Remove build tools
- 40% smaller final image
### Security
✅ **Non-Root User**
- All containers run as UID 1000
- No privileged access
- Secure by default
✅ **Secrets Management**
- Environment variables
- Docker secrets support
- .gitignore for .env
✅ **Read-Only Filesystems**
- Configurable in production
- Temporary directories via tmpfs
✅ **Resource Limits**
- CPU and memory constraints
- Prevents resource exhaustion
### Orchestration
**Docker Compose Features:**
1. **Service Dependencies** - Proper startup order
2. **Named Volumes** - Persistent data storage
3. **Networks** - Service isolation
4. **Health Checks** - Automated monitoring
5. **Auto-Restart** - High availability
**Architecture:**
```
┌──────────────┐
│ skill-seekers│ CLI Application
└──────────────┘
│
┌──────────────┐
│ mcp-server │ MCP Server :8765
└──────────────┘
│
┌───┴───┬────────┬────────┐
│ │ │ │
┌──┴──┐ ┌──┴──┐ ┌───┴──┐ ┌───┴──┐
│Weav-│ │Qdrant│ │Chroma│ │FAISS │
│iate │ │ │ │ │ │(CLI) │
└─────┘ └──────┘ └──────┘ └──────┘
```
### CI/CD Integration
**GitHub Actions Workflow:**
1. **Build Matrix** - 2 images (CLI + MCP)
2. **Multi-Platform** - amd64 + arm64
3. **Automated Testing** - Health checks + command tests
4. **Docker Hub** - Auto-publish on tags
5. **Caching** - GitHub Actions cache
**Triggers:**
- Push to main
- Version tags (v*)
- Pull requests (test only)
- Manual dispatch
---
## Usage Examples
### Quick Start
```bash
# 1. Clone repository
git clone https://github.com/your-org/skill-seekers.git
cd skill-seekers
# 2. Configure environment
cp .env.example .env
# Edit .env with your API keys
# 3. Start services
docker-compose up -d
# 4. Verify
docker-compose ps
curl http://localhost:8765/health
```
### Scrape Documentation
```bash
docker-compose run skill-seekers \
skill-seekers scrape --config /configs/react.json
```
### Export to Vector Databases
```bash
docker-compose run skill-seekers bash -c "
for target in weaviate chroma faiss qdrant; do
python -c \"
import sys
from pathlib import Path
sys.path.insert(0, '/app/src')
from skill_seekers.cli.adaptors import get_adaptor
adaptor = get_adaptor('$target')
adaptor.package(Path('/output/react'), Path('/output'))
print('✅ $target export complete')
\"
done
"
```
### Run Quality Analysis
```bash
docker-compose run skill-seekers \
python3 -c "
import sys
from pathlib import Path
sys.path.insert(0, '/app/src')
from skill_seekers.cli.quality_metrics import QualityAnalyzer
analyzer = QualityAnalyzer(Path('/output/react'))
report = analyzer.generate_report()
print(analyzer.format_report(report))
"
```
---
## Production Deployment
### Resource Requirements
**Minimum:**
- CPU: 2 cores
- RAM: 2GB
- Disk: 5GB
**Recommended:**
- CPU: 4 cores
- RAM: 4GB
- Disk: 20GB (with vector DBs)
### Security Hardening
1. **Secrets Management**
```bash
# Docker secrets
echo "sk-ant-key" | docker secret create anthropic_key -
```
2. **Resource Limits**
```yaml
services:
mcp-server:
deploy:
resources:
limits:
cpus: '2.0'
memory: 2G
```
3. **Read-Only Filesystem**
```yaml
services:
mcp-server:
read_only: true
tmpfs:
- /tmp
```
### Monitoring
**Health Checks:**
```bash
# Check services
docker-compose ps
# Detailed health
docker inspect skill-seekers-mcp | grep Health
```
**Logs:**
```bash
# Stream logs
docker-compose logs -f
# Export logs
docker-compose logs > logs.txt
```
**Metrics:**
```bash
# Resource usage
docker stats
# Per-service metrics
docker-compose top
```
---
## Integration with Week 2 Features
Docker deployment supports all Week 2 capabilities:
| Feature | Docker Support |
|---------|----------------|
| **Vector Database Adaptors** | ✅ All 4 (Weaviate, Chroma, FAISS, Qdrant) |
| **MCP Server** | ✅ Dedicated container (HTTP/stdio) |
| **Streaming Ingestion** | ✅ Memory-efficient in containers |
| **Incremental Updates** | ✅ Persistent volumes |
| **Multi-Language** | ✅ Full language support |
| **Embedding Pipeline** | ✅ Cache persisted |
| **Quality Metrics** | ✅ Automated analysis |
---
## Performance Metrics
### Build Times
| Target | Duration | Cache Hit |
|--------|----------|-----------|
| CLI (first build) | 3-5 min | 0% |
| CLI (cached) | 30-60 sec | 80%+ |
| MCP (first build) | 3-5 min | 0% |
| MCP (cached) | 30-60 sec | 80%+ |
### Image Sizes
| Image | Size | Compressed |
|-------|------|------------|
| skill-seekers | ~400MB | ~150MB |
| skill-seekers-mcp | ~450MB | ~170MB |
| python:3.12-slim (base) | ~130MB | ~50MB |
### Runtime Performance
| Operation | Container | Native | Overhead |
|-----------|-----------|--------|----------|
| Scraping | 10 min | 9.5 min | +5% |
| Quality Analysis | 2 sec | 1.8 sec | +10% |
| Vector Export | 5 sec | 4.5 sec | +10% |
---
## Best Practices Implemented
### ✅ Image Optimization
1. **Multi-stage builds** - 40% size reduction
2. **Slim base images** - Python 3.12-slim
3. **.dockerignore** - Reduced build context
4. **Layer caching** - Faster rebuilds
### ✅ Security
1. **Non-root user** - UID 1000 (skillseeker)
2. **Secrets via env** - No hardcoded keys
3. **Read-only support** - Configurable
4. **Resource limits** - Prevent DoS
### ✅ Reliability
1. **Health checks** - All services
2. **Auto-restart** - unless-stopped
3. **Volume persistence** - Named volumes
4. **Graceful shutdown** - SIGTERM handling
### ✅ Developer Experience
1. **One-command start** - `docker-compose up`
2. **Hot reload** - Volume mounts
3. **Easy configuration** - .env file
4. **Comprehensive docs** - 650+ line guide
---
## Troubleshooting Guide
### Common Issues
1. **Port Already in Use**
```bash
# Check what's using the port
lsof -i :8765
# Use different port
MCP_PORT=8766 docker-compose up -d
```
2. **Permission Denied**
```bash
# Fix ownership
sudo chown -R $(id -u):$(id -g) data/ output/
```
3. **Out of Memory**
```bash
# Increase limits
docker-compose up -d --scale mcp-server=1 --memory=4g
```
4. **Slow Build**
```bash
# Enable BuildKit
export DOCKER_BUILDKIT=1
docker build -t skill-seekers:local .
```
---
## Next Steps (Week 3 Remaining)
With Task #21 complete, continue Week 3:
- **Task #22:** Kubernetes Helm charts
- **Task #23:** Multi-cloud storage (S3, GCS, Azure)
- **Task #24:** API server for embedding generation
- **Task #25:** Real-time documentation sync
- **Task #26:** Performance benchmarking suite
- **Task #27:** Production deployment guides
---
## Files Created
### Docker Infrastructure (6 files)
1. `Dockerfile` (70 lines) - Main CLI image
2. `Dockerfile.mcp` (65 lines) - MCP server image
3. `docker-compose.yml` (120 lines) - Service orchestration
4. `.dockerignore` (80 lines) - Build optimization
5. `.env.example` (40 lines) - Environment template
6. `docs/DOCKER_GUIDE.md` (650+ lines) - Comprehensive documentation
### CI/CD (1 file)
7. `.github/workflows/docker-publish.yml` (130 lines) - Automated builds
### Total Impact
- **New Files:** 7 (~1,155 lines)
- **Docker Images:** 2 (CLI + MCP)
- **Docker Compose Services:** 5
- **Supported Platforms:** 2 (amd64 + arm64)
- **Documentation:** 650+ lines
---
## Quality Achievements
### Deployment Readiness
- **Before:** Manual Python installation required
- **After:** One-command Docker deployment
- **Improvement:** 95% faster setup (10 min → 30 sec)
### Platform Support
- **Before:** Python 3.10+ only
- **After:** Docker (any OS with Docker)
- **Platforms:** Linux, macOS, Windows (via Docker)
### Production Features
- **Multi-stage builds** ✅
- **Health checks** ✅
- **Volume persistence** ✅
- **Resource limits** ✅
- **Security hardening** ✅
- **CI/CD automation** ✅
- **Comprehensive docs** ✅
---
**Task #21: Docker Deployment Infrastructure - COMPLETE ✅**
**Week 3 Progress:** 2/8 tasks complete (25%)
**Ready for Task #22:** Kubernetes Helm Charts

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# Week 2 Complete: Universal Infrastructure Features
**Completion Date:** February 7, 2026
**Branch:** `feature/universal-infrastructure-strategy`
**Status:** ✅ 100% Complete (9/9 tasks)
**Total Implementation:** ~4,000 lines of production code + 140+ tests
---
## 🎯 Week 2 Objective
Build universal infrastructure capabilities to support multiple vector databases, handle large-scale documentation, enable incremental updates, support multi-language content, and provide production-ready quality monitoring.
**Strategic Goal:** Transform Skill Seekers from a single-output tool into a flexible infrastructure layer that can adapt to any RAG pipeline, vector database, or deployment scenario.
---
## ✅ Completed Tasks (9/9)
### **Task #10: Weaviate Vector Database Adaptor**
**Commit:** `baccbf9`
**Files:** `src/skill_seekers/cli/adaptors/weaviate.py` (405 lines)
**Tests:** 11 tests passing
**Features:**
- REST API compatible output format
- Semantic schema with hybrid search support
- BM25 keyword search + vector similarity
- Property-based filtering capabilities
- Production-ready batching for ingestion
**Impact:** Enables enterprise-scale vector search with Weaviate (450K+ users)
---
### **Task #11: Chroma Vector Database Adaptor**
**Commit:** `6fd8474`
**Files:** `src/skill_seekers/cli/adaptors/chroma.py` (436 lines)
**Tests:** 12 tests passing
**Features:**
- ChromaDB collection format export
- Metadata filtering and querying
- Multi-modal embedding support
- Distance metrics: cosine, L2, IP
- Local-first development friendly
**Impact:** Supports popular open-source vector DB (800K+ developers)
---
### **Task #12: FAISS Similarity Search Adaptor**
**Commit:** `ff41968`
**Files:** `src/skill_seekers/cli/adaptors/faiss_helpers.py` (398 lines)
**Tests:** 10 tests passing
**Features:**
- Facebook AI Similarity Search integration
- Multiple index types: Flat, IVF, HNSW
- Billion-scale vector search
- GPU acceleration support
- Memory-efficient indexing
**Impact:** Ultra-fast local search for large-scale deployments
---
### **Task #13: Qdrant Vector Database Adaptor**
**Commit:** `359f266`
**Files:** `src/skill_seekers/cli/adaptors/qdrant.py` (466 lines)
**Tests:** 9 tests passing
**Features:**
- Point-based storage with payloads
- Native payload filtering
- UUID v5 generation for stable IDs
- REST API compatible output
- Advanced filtering capabilities
**Impact:** Modern vector search with rich metadata (100K+ users)
---
### **Task #14: Streaming Ingestion for Large Docs**
**Commit:** `5ce3ed4`
**Files:**
- `src/skill_seekers/cli/streaming_ingest.py` (397 lines)
- `src/skill_seekers/cli/adaptors/streaming_adaptor.py` (320 lines)
- Updated `package_skill.py` with streaming support
**Tests:** 10 tests passing
**Features:**
- Memory-efficient chunking with overlap (4000 chars default, 200 char overlap)
- Progress tracking for large batches
- Batch iteration (100 docs default)
- Checkpoint support for resume capability
- Streaming adaptor mixin for all platforms
**CLI:**
```bash
skill-seekers package output/react/ --streaming --chunk-size 4000 --chunk-overlap 200
```
**Impact:** Process 10GB+ documentation without memory issues (100x scale improvement)
---
### **Task #15: Incremental Updates with Change Detection**
**Commit:** `7762d10`
**Files:** `src/skill_seekers/cli/incremental_updater.py` (450 lines)
**Tests:** 12 tests passing
**Features:**
- SHA256 hashing for change detection
- Version tracking (major.minor.patch)
- Delta package generation
- Change classification: added/modified/deleted
- Detailed diff reports with line counts
**Update Types:**
- Full rebuild (major version bump)
- Delta update (minor version bump)
- Patch update (patch version bump)
**Impact:** 95% faster updates (45 min → 2 min for small changes)
---
### **Task #16: Multi-Language Documentation Support**
**Commit:** `261f28f`
**Files:** `src/skill_seekers/cli/multilang_support.py` (421 lines)
**Tests:** 22 tests passing
**Features:**
- 11 languages supported:
- English, Spanish, French, German, Portuguese
- Italian, Chinese, Japanese, Korean
- Russian, Arabic
- Filename pattern recognition:
- `file.en.md`, `file_en.md`, `file-en.md`
- Content-based language detection
- Translation status tracking
- Export by language
- Primary language auto-detection
**Impact:** Global reach for international developer communities (3B+ users)
---
### **Task #17: Custom Embedding Pipeline**
**Commit:** `b475b51`
**Files:** `src/skill_seekers/cli/embedding_pipeline.py` (435 lines)
**Tests:** 18 tests passing
**Features:**
- Provider abstraction: OpenAI, Local (extensible)
- Two-tier caching: memory + disk
- Cost tracking and estimation
- Batch processing with progress
- Dimension validation
- Deterministic local embeddings (development)
**OpenAI Models Supported:**
- text-embedding-ada-002 (1536 dims, $0.10/1M tokens)
- text-embedding-3-small (1536 dims, $0.02/1M tokens)
- text-embedding-3-large (3072 dims, $0.13/1M tokens)
**Impact:** 70% cost reduction via caching + flexible provider switching
---
### **Task #18: Quality Metrics Dashboard**
**Commit:** `3e8c913`
**Files:**
- `src/skill_seekers/cli/quality_metrics.py` (542 lines)
- `tests/test_quality_metrics.py` (18 tests)
**Tests:** 18/18 passing ✅
**Features:**
- 4-dimensional quality scoring:
1. **Completeness** (30% weight): SKILL.md, references, metadata
2. **Accuracy** (25% weight): No TODOs, no placeholders, valid JSON
3. **Coverage** (25% weight): Getting started, API docs, examples
4. **Health** (20% weight): No empty files, proper structure
- Grading system: A+ to F (11 grades)
- Smart recommendations (priority-based)
- Metric severity levels: INFO/WARNING/ERROR/CRITICAL
- Formatted dashboard output
- Statistics tracking (files, words, size)
- JSON export support
**Scoring Example:**
```
🎯 OVERALL SCORE
Grade: B+
Score: 82.5/100
📈 COMPONENT SCORES
Completeness: 85.0% (30% weight)
Accuracy: 90.0% (25% weight)
Coverage: 75.0% (25% weight)
Health: 85.0% (20% weight)
💡 RECOMMENDATIONS
🟡 Expand documentation coverage (API, examples)
```
**Impact:** Objective quality measurement (0/10 → 8.5/10 avg improvement)
---
## 📊 Week 2 Summary Statistics
### Code Metrics
- **Production Code:** ~4,000 lines
- **Test Code:** ~2,200 lines
- **Test Coverage:** 140+ tests (100% pass rate)
- **New Files:** 10 modules + 7 test files
### Capabilities Added
- **Vector Databases:** 4 adaptors (Weaviate, Chroma, FAISS, Qdrant)
- **Languages Supported:** 11 languages
- **Embedding Providers:** 2 (OpenAI, Local)
- **Quality Dimensions:** 4 dimensions with weighted scoring
- **Streaming:** Memory-efficient processing for 10GB+ docs
- **Incremental Updates:** 95% faster updates
### Platform Support Expanded
| Platform | Before | After | Improvement |
|----------|--------|-------|-------------|
| Vector DBs | 0 | 4 | +4 adaptors |
| Max Doc Size | 100MB | 10GB+ | 100x scale |
| Update Speed | 45 min | 2 min | 95% faster |
| Languages | 1 (EN) | 11 | Global reach |
| Quality Metrics | Manual | Automated | 8.5/10 avg |
---
## 🎯 Strategic Impact
### Before Week 2
- Single-format output (Claude skills)
- Memory-limited (100MB docs)
- Full rebuild required (45 min)
- English-only documentation
- No quality measurement
### After Week 2
- **4 vector database formats** (Weaviate, Chroma, FAISS, Qdrant)
- **Streaming ingestion** for unlimited scale (10GB+)
- **Incremental updates** (95% faster)
- **11 languages** for global reach
- **Custom embedding pipeline** (70% cost savings)
- **Quality metrics** (objective measurement)
### Market Expansion
- **Before:** RAG pipelines (5M users)
- **After:** RAG + Vector DBs + Multi-language + Enterprise (12M+ users)
---
## 🔧 Technical Achievements
### 1. Platform Adaptor Pattern
Consistent interface across 4 vector databases:
```python
from skill_seekers.cli.adaptors import get_adaptor
adaptor = get_adaptor('weaviate') # or 'chroma', 'faiss', 'qdrant'
adaptor.package(skill_dir='output/react/', output_path='output/')
```
### 2. Streaming Architecture
Memory-efficient processing for massive documentation:
```python
from skill_seekers.cli.streaming_ingest import StreamingIngester
ingester = StreamingIngester(chunk_size=4000, chunk_overlap=200)
for chunk, metadata in ingester.chunk_document(content, metadata):
# Process chunk without loading entire doc into memory
yield chunk, metadata
```
### 3. Incremental Update System
Smart change detection with version tracking:
```python
from skill_seekers.cli.incremental_updater import IncrementalUpdater
updater = IncrementalUpdater(skill_dir='output/react/')
changes = updater.detect_changes(previous_version='1.2.3')
# Returns: ChangeSet(added=[], modified=['api_reference.md'], deleted=[])
updater.generate_delta_package(changes, output_path='delta.zip')
```
### 4. Multi-Language Manager
Language detection and translation tracking:
```python
from skill_seekers.cli.multilang_support import MultiLanguageManager
manager = MultiLanguageManager()
manager.add_document('README.md', content, metadata)
manager.add_document('README.es.md', spanish_content, metadata)
status = manager.get_translation_status()
# Returns: TranslationStatus(source='en', translated=['es'], coverage=100%)
```
### 5. Embedding Pipeline
Provider abstraction with caching:
```python
from skill_seekers.cli.embedding_pipeline import EmbeddingPipeline, EmbeddingConfig
config = EmbeddingConfig(
provider='openai', # or 'local'
model='text-embedding-3-small',
dimension=1536,
batch_size=100
)
pipeline = EmbeddingPipeline(config)
result = pipeline.generate_batch(texts)
# Automatic caching reduces cost by 70%
```
### 6. Quality Analytics
Objective quality measurement:
```python
from skill_seekers.cli.quality_metrics import QualityAnalyzer
analyzer = QualityAnalyzer(skill_dir='output/react/')
report = analyzer.generate_report()
print(f"Grade: {report.overall_score.grade}") # e.g., "A-"
print(f"Score: {report.overall_score.total_score}") # e.g., 87.5
```
---
## 🚀 Integration Examples
### Example 1: Stream to Weaviate
```bash
# Generate skill with streaming + Weaviate format
skill-seekers scrape --config configs/react.json
skill-seekers package output/react/ \
--target weaviate \
--streaming \
--chunk-size 4000
```
### Example 2: Incremental Update to Chroma
```bash
# Initial build
skill-seekers scrape --config configs/react.json
skill-seekers package output/react/ --target chroma
# Update docs (only changed files)
skill-seekers scrape --config configs/react.json --incremental
skill-seekers package output/react/ --target chroma --delta-only
# 95% faster: 2 min vs 45 min
```
### Example 3: Multi-Language with Quality Checks
```bash
# Scrape multi-language docs
skill-seekers scrape --config configs/vue.json --detect-languages
# Check quality before deployment
skill-seekers analyze output/vue/
# Quality Grade: A- (87.5/100)
# ✅ Ready for production
# Package by language
skill-seekers package output/vue/ --target qdrant --language es
```
### Example 4: Custom Embeddings with Cost Tracking
```bash
# Generate embeddings with caching
skill-seekers embed output/react/ \
--provider openai \
--model text-embedding-3-small \
--cache-dir .embeddings_cache
# Result: $0.05 (vs $0.15 without caching = 67% savings)
```
---
## 🎯 Quality Improvements
### Measurable Impact
| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| Max Scale | 100MB | 10GB+ | 100x |
| Update Time | 45 min | 2 min | 95% faster |
| Language Support | 1 | 11 | 11x reach |
| Embedding Cost | $0.15 | $0.05 | 67% savings |
| Quality Score | Manual | 8.5/10 | Automated |
| Vector DB Support | 0 | 4 | +4 platforms |
### Test Coverage
- ✅ 140+ tests across all features
- ✅ 100% test pass rate
- ✅ Comprehensive edge case coverage
- ✅ Integration tests for all adaptors
---
## 📋 Files Changed
### New Modules (10)
1. `src/skill_seekers/cli/adaptors/weaviate.py` (405 lines)
2. `src/skill_seekers/cli/adaptors/chroma.py` (436 lines)
3. `src/skill_seekers/cli/adaptors/faiss_helpers.py` (398 lines)
4. `src/skill_seekers/cli/adaptors/qdrant.py` (466 lines)
5. `src/skill_seekers/cli/streaming_ingest.py` (397 lines)
6. `src/skill_seekers/cli/adaptors/streaming_adaptor.py` (320 lines)
7. `src/skill_seekers/cli/incremental_updater.py` (450 lines)
8. `src/skill_seekers/cli/multilang_support.py` (421 lines)
9. `src/skill_seekers/cli/embedding_pipeline.py` (435 lines)
10. `src/skill_seekers/cli/quality_metrics.py` (542 lines)
### Test Files (7)
1. `tests/test_weaviate_adaptor.py` (11 tests)
2. `tests/test_chroma_adaptor.py` (12 tests)
3. `tests/test_faiss_helpers.py` (10 tests)
4. `tests/test_qdrant_adaptor.py` (9 tests)
5. `tests/test_streaming_ingest.py` (10 tests)
6. `tests/test_incremental_updater.py` (12 tests)
7. `tests/test_multilang_support.py` (22 tests)
8. `tests/test_embedding_pipeline.py` (18 tests)
9. `tests/test_quality_metrics.py` (18 tests)
### Modified Files
- `src/skill_seekers/cli/adaptors/__init__.py` (added 4 adaptor registrations)
- `src/skill_seekers/cli/package_skill.py` (added streaming parameters)
---
## 🎓 Lessons Learned
### What Worked Well ✅
1. **Consistent abstractions** - Platform adaptor pattern scales beautifully
2. **Test-driven development** - 100% test pass rate prevented regressions
3. **Incremental approach** - 9 focused tasks easier than 1 monolithic task
4. **Streaming architecture** - Memory-efficient from day 1
5. **Quality metrics** - Objective measurement guides improvements
### Challenges Overcome ⚡
1. **Vector DB format differences** - Solved with adaptor pattern
2. **Memory constraints** - Streaming ingestion handles 10GB+ docs
3. **Language detection** - Pattern matching + content heuristics work well
4. **Cost optimization** - Two-tier caching reduces embedding costs 70%
5. **Quality measurement** - Weighted scoring balances multiple dimensions
---
## 🔮 Next Steps: Week 3 Preview
### Upcoming Tasks
- **Task #19:** MCP server integration for vector databases
- **Task #20:** GitHub Actions automation
- **Task #21:** Docker deployment
- **Task #22:** Kubernetes Helm charts
- **Task #23:** Multi-cloud storage (S3, GCS, Azure Blob)
- **Task #24:** API server for embedding generation
- **Task #25:** Real-time documentation sync
- **Task #26:** Performance benchmarking suite
- **Task #27:** Production deployment guides
### Strategic Goals
- Automation infrastructure (GitHub Actions, Docker, K8s)
- Cloud-native deployment
- Real-time sync capabilities
- Production-ready monitoring
- Comprehensive benchmarks
---
## 🎉 Week 2 Achievement
**Status:** ✅ 100% Complete
**Tasks Completed:** 9/9 (100%)
**Tests Passing:** 140+/140+ (100%)
**Code Quality:** All tests green, comprehensive coverage
**Timeline:** On schedule
**Strategic Impact:** Universal infrastructure foundation established
**Ready for Week 3:** Multi-cloud deployment and automation infrastructure
---
**Contributors:**
- Primary Development: Claude Sonnet 4.5 + @yusyus
- Testing: Comprehensive test suites
- Documentation: Inline code documentation
**Branch:** `feature/universal-infrastructure-strategy`
**Base:** `main`
**Ready for:** Merge after Week 3-4 completion