py-wikijs/docs/IMPROVEMENT_PLAN.md

# Wiki.js Python SDK - Comprehensive Improvement Plan

**Version**: 2.0
**Created**: 2025-10-22
**Status**: Active Development
**Current Version**: v0.1.0 (Phase 1 Complete)

---

## 📋 Executive Summary

This document outlines the strategic improvements to evolve the Wiki.js Python SDK from a functional MVP to an enterprise-grade solution. The plan emphasizes:

- **Quality First**: Every feature includes comprehensive tests and documentation
- **Incremental Delivery**: Each phase delivers measurable value
- **Backward Compatibility**: No breaking changes without major version bump
- **Developer Experience**: Intuitive APIs with clear error messages

### Key Improvements Overview

| Improvement | Phase | Impact | Complexity |
|-------------|-------|--------|------------|
| Async/Await Support | 2 | HIGH | MEDIUM |
| API Expansion (Users, Groups, Assets) | 2 | HIGH | MEDIUM |
| Intelligent Caching Layer | 3 | HIGH | MEDIUM |
| Batch Operations (GraphQL) | 3 | HIGH | LOW |
| Rate Limiting & Throttling | 3 | MEDIUM | LOW |
| Circuit Breaker & Retry Logic | 3 | MEDIUM | MEDIUM |
| Auto-Pagination Iterators | 2 | MEDIUM | LOW |
| Advanced CLI | 4 | MEDIUM | HIGH |
| Plugin Architecture | 4 | LOW | HIGH |

---

## 🎯 Phase 2: Essential Features + Async Support

**Target Duration**: 3-4 weeks
**Target Version**: v0.2.0
**Goal**: Complete API coverage + modern async support

### 2.1 Async/Await Implementation

#### 2.1.1 Dual Client Architecture
**Objective**: Provide both sync and async clients without breaking existing code

**Implementation Strategy**:
```python
# Current sync client (unchanged)
from wikijs import WikiJSClient

# New async client
from wikijs.aio import AsyncWikiJSClient

# Both share same interface
client = WikiJSClient(url, auth)
async_client = AsyncWikiJSClient(url, auth)
```

**Tasks**:
1. ✅ **Create `wikijs/aio/` module structure**
   - `wikijs/aio/__init__.py` - Async exports
   - `wikijs/aio/client.py` - AsyncWikiJSClient implementation
   - `wikijs/aio/endpoints.py` - Async endpoint handlers

2. ✅ **Implement AsyncWikiJSClient**
   - Use `aiohttp.ClientSession` instead of `requests.Session`
   - Async context manager support (`async with`)
   - Async methods: `_arequest()`, `_ahandle_response()`
   - Connection pooling configuration

3. ✅ **Create async endpoint classes**
   - `AsyncPagesEndpoint` with all CRUD operations
   - Maintain same method signatures (add `async`/`await`)
   - Reuse models and exceptions from sync client

4. ✅ **Add async authentication handlers**
   - `AsyncAPIKeyAuth` - Simple header injection
   - `AsyncJWTAuth` - Async token refresh logic

**Testing Requirements**:
- [ ] Unit tests for `AsyncWikiJSClient` (>95% coverage)
- [ ] Unit tests for async endpoints (>95% coverage)
- [ ] Integration tests with real Wiki.js instance
- [ ] Concurrent request tests (100+ simultaneous requests)
- [ ] Performance benchmarks (async vs sync)
- [ ] Error handling tests (timeouts, connection errors)

**Documentation Requirements**:
- [ ] `docs/async_usage.md` - Comprehensive async guide
- [ ] Update `README.md` with async examples
- [ ] Update API reference with async methods
- [ ] Add async examples to `examples/async_basic_usage.py`
- [ ] Migration guide from sync to async

**Success Criteria**:
- [ ] Async client achieves >3x throughput vs sync (100 concurrent requests)
- [ ] All sync features available in async variant
- [ ] Zero breaking changes to existing sync API
- [ ] Documentation covers 100% of async functionality
- [ ] All tests pass with >95% coverage

**Estimated Effort**: 12-15 hours, 40-50 AI sessions

---

#### 2.1.2 Async Context Managers & Resource Cleanup
**Objective**: Proper async resource management

**Implementation**:
```python
# Context manager pattern
async with AsyncWikiJSClient(url, auth) as client:
    page = await client.pages.get(123)
    # Automatic cleanup on exit

# Manual management
client = AsyncWikiJSClient(url, auth)
try:
    page = await client.pages.get(123)
finally:
    await client.close()
```

**Tasks**:
1. Implement `__aenter__` and `__aexit__` methods
2. Proper session cleanup in async context
3. Connection pool lifecycle management
4. Graceful shutdown handling

**Testing Requirements**:
- [ ] Resource leak tests (ensure sessions close)
- [ ] Exception handling in context managers
- [ ] Concurrent context manager usage

**Estimated Effort**: 3-4 hours, 10-15 AI sessions

---

### 2.2 API Expansion

#### 2.2.1 Users API Implementation
**Objective**: Complete user management capabilities

**GraphQL Queries to Implement**:
```graphql
# List users
query { users { list { id, name, email, isActive } } }

# Get user
query($id: Int!) { users { single(id: $id) { id, name, email, groups } } }

# Create user
mutation($email: String!, $name: String!) {
  users { create(email: $email, name: $name) { responseResult { succeeded } } }
}

# Update user
mutation($id: Int!, $name: String!) {
  users { update(id: $id, name: $name) { responseResult { succeeded } } }
}

# Delete user
mutation($id: Int!) { users { delete(id: $id) { responseResult { succeeded } } }
```

**File Structure**:
```
wikijs/endpoints/users.py       # Sync implementation
wikijs/aio/endpoints/users.py   # Async implementation
wikijs/models/user.py           # User data models
tests/endpoints/test_users.py   # Sync tests
tests/aio/test_users.py         # Async tests
```

**Tasks**:
1. ✅ **Create User models** (`wikijs/models/user.py`)
   - `User` - Complete user data model
   - `UserGroup` - User group membership
   - `UserPermissions` - Permission model

2. ✅ **Implement UsersEndpoint** (`wikijs/endpoints/users.py`)
   - `list(limit, offset, filter)` - List users with pagination
   - `get(user_id)` - Get single user by ID
   - `create(email, name, password, groups)` - Create new user
   - `update(user_id, **kwargs)` - Update user
   - `delete(user_id)` - Delete user
   - `search(query)` - Search users by name/email

3. ✅ **Implement AsyncUsersEndpoint** (async variant)

4. ✅ **Add to client** - Register in `WikiJSClient.users`

**Testing Requirements**:
- [ ] Unit tests for UsersEndpoint (>95% coverage)
- [ ] Unit tests for User models
- [ ] Integration tests (CRUD operations)
- [ ] Permission validation tests
- [ ] Edge cases (invalid emails, duplicate users)

**Documentation Requirements**:
- [ ] `docs/api/users.md` - Complete Users API reference
- [ ] Usage examples in `examples/user_management.py`
- [ ] Update main README with Users API section

**Success Criteria**:
- [ ] All CRUD operations functional
- [ ] Model validation prevents invalid data
- [ ] Error handling provides clear feedback
- [ ] Tests achieve >95% coverage

**Estimated Effort**: 8-10 hours, 30-35 AI sessions

---

#### 2.2.2 Groups API Implementation
**Objective**: Group and permission management

**Key Operations**:
```python
# Group management
groups = client.groups.list()
group = client.groups.get(group_id)
new_group = client.groups.create(name="Editors", permissions=["read", "write"])
client.groups.update(group_id, name="Senior Editors")
client.groups.delete(group_id)

# Member management
client.groups.add_user(group_id, user_id)
client.groups.remove_user(group_id, user_id)
members = client.groups.list_members(group_id)
```

**Tasks**:
1. Create Group models (`wikijs/models/group.py`)
2. Implement GroupsEndpoint (`wikijs/endpoints/groups.py`)
3. Implement AsyncGroupsEndpoint (async variant)
4. Add permission validation logic

**Testing Requirements**:
- [ ] Unit tests for GroupsEndpoint (>95% coverage)
- [ ] Integration tests for group-user relationships
- [ ] Permission inheritance tests

**Documentation Requirements**:
- [ ] `docs/api/groups.md` - Groups API reference
- [ ] Examples in `examples/group_management.py`

**Success Criteria**:
- [ ] Group CRUD operations work correctly
- [ ] User-group relationships properly managed
- [ ] Permission validation prevents invalid operations

**Estimated Effort**: 6-8 hours, 25-30 AI sessions

---

#### 2.2.3 Assets API Implementation
**Objective**: File upload and asset management

**Key Operations**:
```python
# Upload asset
asset = client.assets.upload(
    file_path="/path/to/image.png",
    folder="/images",
    optimize=True
)

# List assets
assets = client.assets.list(folder="/images", kind="image")

# Get asset info
asset = client.assets.get(asset_id)

# Delete asset
client.assets.delete(asset_id)

# Download asset
content = client.assets.download(asset_id)
```

**Tasks**:
1. Create Asset models (`wikijs/models/asset.py`)
2. Implement AssetsEndpoint with multipart upload
3. Handle large file uploads (streaming)
4. Async variant for concurrent uploads

**Testing Requirements**:
- [ ] Unit tests for AssetsEndpoint
- [ ] File upload tests (various formats)
- [ ] Large file handling tests (>10MB)
- [ ] Concurrent upload tests (async)

**Documentation Requirements**:
- [ ] `docs/api/assets.md` - Assets API reference
- [ ] Examples in `examples/asset_upload.py`

**Success Criteria**:
- [ ] Support common file formats (images, PDFs, docs)
- [ ] Large file uploads work reliably
- [ ] Progress tracking for uploads

**Estimated Effort**: 8-10 hours, 30-35 AI sessions

---

#### 2.2.4 Auto-Pagination Support
**Objective**: Pythonic iteration over paginated results

**Implementation**:
```python
# Current (manual pagination)
offset = 0
all_pages = []
while True:
    batch = client.pages.list(limit=50, offset=offset)
    if not batch:
        break
    all_pages.extend(batch)
    offset += 50

# New (auto-pagination)
all_pages = list(client.pages.iter_all())

# Or iterate lazily
for page in client.pages.iter_all(batch_size=50):
    process_page(page)
```

**Tasks**:
1. Add `iter_all()` method to all list endpoints
2. Implement lazy iteration with `yield`
3. Support custom batch sizes
4. Async iterator support (`async for`)

**Testing Requirements**:
- [ ] Iterator tests for each endpoint
- [ ] Large dataset tests (1000+ items)
- [ ] Memory efficiency tests
- [ ] Async iterator tests

**Success Criteria**:
- [ ] Memory efficient (doesn't load all at once)
- [ ] Works with all paginated endpoints
- [ ] Async variant available

**Estimated Effort**: 4-5 hours, 15-20 AI sessions

---

### Phase 2 Quality Gates

**Code Quality**:
- [ ] All code follows Black formatting
- [ ] Type hints on 100% of public APIs
- [ ] Docstrings on 100% of public methods
- [ ] MyPy strict mode passes
- [ ] Flake8 with no errors
- [ ] Bandit security scan passes

**Testing**:
- [ ] Overall test coverage >90%
- [ ] All integration tests pass
- [ ] Performance benchmarks established
- [ ] Async performance >3x sync for 100 concurrent requests
- [ ] No memory leaks detected

**Documentation**:
- [ ] All new APIs documented in `docs/api/`
- [ ] Examples for each major feature
- [ ] Migration guides updated
- [ ] CHANGELOG.md updated with all changes
- [ ] README.md updated with new capabilities

**Review Checkpoints**:
1. **After Async Implementation**: Code review + performance benchmarks
2. **After Each API Expansion**: Integration tests + docs review
3. **Before Release**: Full regression test + security audit

**Release Criteria for v0.2.0**:
- [ ] All Phase 2 tasks completed
- [ ] All quality gates passed
- [ ] Beta testing with real users (minimum 3 users)
- [ ] No critical or high-severity bugs
- [ ] Documentation comprehensive and accurate

---

## ⚡ Phase 3: Reliability & Performance

**Target Duration**: 3-4 weeks
**Target Version**: v0.3.0
**Goal**: Production-grade reliability and performance

### 3.1 Intelligent Caching Layer

#### 3.1.1 Cache Architecture
**Objective**: Pluggable caching with multiple backends

**Design**:
```python
from wikijs import WikiJSClient
from wikijs.cache import MemoryCache, RedisCache, FileCache

# No caching (default - backward compatible)
client = WikiJSClient(url, auth)

# Memory cache (development)
client = WikiJSClient(url, auth, cache=MemoryCache(ttl=300, max_size=1000))

# Redis cache (production)
client = WikiJSClient(url, auth, cache=RedisCache(
    host="localhost",
    ttl=600,
    key_prefix="wikijs:"
))

# File cache (persistent)
client = WikiJSClient(url, auth, cache=FileCache(
    cache_dir="/tmp/wikijs_cache",
    ttl=3600
))
```

**File Structure**:
```
wikijs/cache/
├── __init__.py           # Cache exports
├── base.py              # CacheBackend abstract base class
├── memory.py            # MemoryCache implementation
├── redis.py             # RedisCache implementation
├── file.py              # FileCache implementation
└── strategies.py        # Cache invalidation strategies

tests/cache/
├── test_memory_cache.py
├── test_redis_cache.py
├── test_file_cache.py
└── test_invalidation.py
```

**Tasks**:
1. ✅ **Create cache base classes** (`wikijs/cache/base.py`)
   ```python
   from abc import ABC, abstractmethod
   from typing import Any, Optional

   class CacheBackend(ABC):
       @abstractmethod
       def get(self, key: str) -> Optional[Any]:
           """Get value from cache."""

       @abstractmethod
       def set(self, key: str, value: Any, ttl: Optional[int] = None) -> None:
           """Set value in cache."""

       @abstractmethod
       def delete(self, key: str) -> None:
           """Delete key from cache."""

       @abstractmethod
       def clear(self) -> None:
           """Clear all cache entries."""
   ```

2. ✅ **Implement MemoryCache** (LRU with TTL)
   - Use `collections.OrderedDict` for LRU
   - Timestamp-based TTL expiration
   - Thread-safe operations with `threading.Lock`
   - Configurable max size (default: 1000 entries)

3. ✅ **Implement RedisCache**
   - Use `redis-py` library
   - Automatic serialization with `pickle`
   - Connection pooling
   - Optional key prefix for multi-tenant support

4. ✅ **Implement FileCache**
   - Store as JSON/pickle files
   - Directory-based organization
   - Automatic cleanup of expired entries
   - Disk space management

5. ✅ **Integrate caching into client**
   - Cache key generation (URL + params hash)
   - Cache middleware in `_request()` method
   - Only cache GET requests (not POST/PUT/DELETE)
   - Respect Cache-Control headers if present

**Cache Invalidation Strategy**:
```python
# Time-based (TTL)
cache = MemoryCache(ttl=300)  # 5 minutes

# Event-based invalidation
client.pages.update(page_id, content="New")  # Auto-invalidate cached page

# Manual invalidation
client.cache.clear()  # Clear all
client.cache.delete(f"pages:{page_id}")  # Clear specific
```

**Testing Requirements**:
- [ ] Unit tests for each cache backend (>95% coverage)
- [ ] Cache hit/miss rate tests
- [ ] TTL expiration tests
- [ ] Concurrent access tests (thread safety)
- [ ] Cache invalidation tests
- [ ] Memory usage tests (no leaks)
- [ ] Performance benchmarks (cache vs no-cache)

**Documentation Requirements**:
- [ ] `docs/caching.md` - Comprehensive caching guide
- [ ] Configuration examples for each backend
- [ ] Best practices for cache invalidation
- [ ] Performance tuning guide

**Success Criteria**:
- [ ] Cache hit ratio >80% for typical usage patterns
- [ ] No race conditions in concurrent scenarios
- [ ] Memory usage stays bounded (LRU eviction works)
- [ ] Redis cache handles connection failures gracefully
- [ ] Clear performance improvement (>50% faster for cached requests)

**Estimated Effort**: 10-12 hours, 35-40 AI sessions

---

### 3.2 Batch Operations with GraphQL Optimization

#### 3.2.1 GraphQL Batch Queries
**Objective**: Single request for multiple operations

**Implementation**:
```python
# Instead of N requests (slow)
pages = [client.pages.get(id) for id in [1, 2, 3, 4, 5]]

# Single batched request (fast)
pages = client.pages.get_many([1, 2, 3, 4, 5])

# Batch create
results = client.pages.create_many([
    {"title": "Page 1", "path": "/page-1", "content": "Content 1"},
    {"title": "Page 2", "path": "/page-2", "content": "Content 2"},
    {"title": "Page 3", "path": "/page-3", "content": "Content 3"},
])

# Batch update
results = client.pages.update_many([
    {"id": 1, "content": "New content 1"},
    {"id": 2, "content": "New content 2"},
])

# Batch delete
results = client.pages.delete_many([1, 2, 3, 4, 5])
```

**GraphQL Batch Query Structure**:
```graphql
query GetMultiplePages {
  page1: pages { single(id: 1) { id, title, content } }
  page2: pages { single(id: 2) { id, title, content } }
  page3: pages { single(id: 3) { id, title, content } }
}

mutation CreateMultiplePages {
  page1: pages { create(title: "Page 1", path: "/page-1") { responseResult } }
  page2: pages { create(title: "Page 2", path: "/page-2") { responseResult } }
}
```

**Tasks**:
1. ✅ **Create batch query builder** (`wikijs/utils/batch.py`)
   - Generate aliased GraphQL queries
   - Handle maximum batch size limits (default: 50)
   - Automatic splitting for large batches

2. ✅ **Add batch methods to PagesEndpoint**
   - `get_many(ids: List[int]) -> List[Page]`
   - `create_many(pages: List[Dict]) -> List[Page]`
   - `update_many(updates: List[Dict]) -> List[Page]`
   - `delete_many(ids: List[int]) -> List[bool]`

3. ✅ **Error handling for partial failures**
   - Return results + errors separately
   - Continue processing even if some operations fail
   ```python
   result = client.pages.create_many([...])
   print(f"Created: {len(result.success)}")
   print(f"Failed: {len(result.errors)}")
   for error in result.errors:
       print(f"  - {error.index}: {error.message}")
   ```

4. ✅ **Extend to other endpoints**
   - Users batch operations
   - Groups batch operations
   - Assets batch upload

**Testing Requirements**:
- [ ] Batch operation tests (various sizes)
- [ ] Partial failure handling tests
- [ ] Performance benchmarks (batch vs sequential)
- [ ] Large batch tests (1000+ items with auto-splitting)
- [ ] Concurrent batch operation tests

**Documentation Requirements**:
- [ ] `docs/batch_operations.md` - Batch operations guide
- [ ] Performance comparison examples
- [ ] Error handling examples

**Success Criteria**:
- [ ] Batch operations >10x faster than sequential for 50+ items
- [ ] Graceful handling of partial failures
- [ ] Clear error messages for failed operations
- [ ] Automatic splitting for batches exceeding limits

**Estimated Effort**: 8-10 hours, 30-35 AI sessions

---

### 3.3 Rate Limiting & Throttling

#### 3.3.1 Client-Side Rate Limiting
**Objective**: Prevent API abuse and respect server limits

**Implementation**:
```python
from wikijs import WikiJSClient
from wikijs.middleware import RateLimiter

# Simple rate limiting
client = WikiJSClient(
    url,
    auth,
    rate_limiter=RateLimiter(requests_per_second=10)
)

# Advanced rate limiting
client = WikiJSClient(
    url,
    auth,
    rate_limiter=RateLimiter(
        requests_per_second=10,
        burst_size=20,  # Allow bursts up to 20 requests
        strategy="token_bucket"  # or "sliding_window"
    )
)

# Per-endpoint rate limits
client = WikiJSClient(
    url,
    auth,
    rate_limiter=RateLimiter(
        default_rps=10,
        endpoint_limits={
            "/graphql": 5,  # Slower for GraphQL
            "/assets": 2,   # Even slower for uploads
        }
    )
)
```

**Tasks**:
1. ✅ **Create rate limiter middleware** (`wikijs/middleware/rate_limiter.py`)
   - Token bucket algorithm implementation
   - Sliding window algorithm implementation
   - Async-compatible (works with both sync and async clients)

2. ✅ **Integrate into client** (`_request()` method)
   - Pre-request rate limit check
   - Automatic waiting if limit exceeded
   - Rate limit headers tracking (if provided by server)

3. ✅ **Add rate limit exceeded handling**
   - Custom exception `RateLimitExceeded`
   - Configurable behavior (wait, raise, callback)
   ```python
   # Wait automatically (default)
   client = WikiJSClient(url, auth, rate_limiter=RateLimiter(...))

   # Raise exception immediately
   client = WikiJSClient(
       url,
       auth,
       rate_limiter=RateLimiter(..., on_limit="raise")
   )

   # Custom callback
   def on_rate_limit(wait_time):
       print(f"Rate limited, waiting {wait_time}s...")

   client = WikiJSClient(
       url,
       auth,
       rate_limiter=RateLimiter(..., on_limit=on_rate_limit)
   )
   ```

**Testing Requirements**:
- [ ] Rate limiter algorithm tests
- [ ] Integration tests with client
- [ ] Concurrent request rate limiting tests
- [ ] Burst handling tests

**Documentation Requirements**:
- [ ] `docs/rate_limiting.md` - Rate limiting guide
- [ ] Configuration examples
- [ ] Best practices for production usage

**Success Criteria**:
- [ ] Accurately enforces configured rate limits
- [ ] Works seamlessly with both sync and async clients
- [ ] No performance overhead when rate limits not hit
- [ ] Clear feedback when rate limited

**Estimated Effort**: 5-6 hours, 20-25 AI sessions

---

### 3.4 Circuit Breaker & Enhanced Retry Logic

#### 3.4.1 Circuit Breaker Pattern
**Objective**: Prevent cascading failures and improve resilience

**Implementation**:
```python
from wikijs import WikiJSClient
from wikijs.middleware import CircuitBreaker, RetryStrategy

# Circuit breaker configuration
client = WikiJSClient(
    url,
    auth,
    circuit_breaker=CircuitBreaker(
        failure_threshold=5,      # Open after 5 failures
        recovery_timeout=60,      # Try again after 60s
        success_threshold=2,      # Close after 2 successes
        on_open=lambda: print("Circuit opened!")
    )
)

# Enhanced retry strategy
client = WikiJSClient(
    url,
    auth,
    retry_strategy=RetryStrategy(
        max_retries=5,
        backoff="exponential",    # or "linear", "constant"
        initial_delay=1,          # Start with 1s
        max_delay=60,             # Cap at 60s
        jitter=True,              # Add randomness
        retry_on=[500, 502, 503, 504, 429],  # Status codes
    )
)

# Combined resilience
client = WikiJSClient(
    url,
    auth,
    retry_strategy=RetryStrategy(...),
    circuit_breaker=CircuitBreaker(...)
)
```

**Circuit Breaker States**:
```
CLOSED (normal) -> OPEN (failing) -> HALF_OPEN (testing) -> CLOSED/OPEN
       ^                                                            |
       |___________________________________________________________|
```

**Tasks**:
1. ✅ **Implement CircuitBreaker** (`wikijs/middleware/circuit_breaker.py`)
   - State machine (CLOSED, OPEN, HALF_OPEN)
   - Failure/success counters
   - Automatic state transitions
   - Thread-safe implementation

2. ✅ **Enhance retry strategy** (`wikijs/middleware/retry.py`)
   - Exponential backoff with jitter
   - Configurable retry conditions
   - Respect Retry-After headers
   - Maximum retry time limit

3. ✅ **Integrate into client**
   - Wrap `_request()` method
   - Circuit breaker check before request
   - Retry logic on failures
   - Proper exception propagation

4. ✅ **Add metrics and monitoring**
   - Circuit breaker state changes logging
   - Retry attempt logging
   - Failure rate tracking
   ```python
   # Get circuit breaker stats
   stats = client.circuit_breaker.stats()
   print(f"State: {stats.state}")
   print(f"Failures: {stats.failure_count}")
   print(f"Last failure: {stats.last_failure_time}")
   ```

**Testing Requirements**:
- [ ] Circuit breaker state transition tests
- [ ] Retry strategy tests (all backoff types)
- [ ] Integration tests with failing server
- [ ] Concurrent request tests
- [ ] Metrics accuracy tests

**Documentation Requirements**:
- [ ] `docs/resilience.md` - Resilience patterns guide
- [ ] Circuit breaker configuration guide
- [ ] Retry strategy best practices

**Success Criteria**:
- [ ] Circuit breaker prevents cascading failures
- [ ] Retry logic handles transient failures gracefully
- [ ] System recovers automatically when service restores
- [ ] Clear logging of failure patterns

**Estimated Effort**: 8-10 hours, 30-35 AI sessions

---

### Phase 3 Quality Gates

**Performance Requirements**:
- [ ] Caching improves response time by >50% for repeated requests
- [ ] Batch operations >10x faster than sequential for 50+ items
- [ ] Rate limiting adds <1ms overhead per request
- [ ] Circuit breaker detection time <100ms
- [ ] No memory leaks in long-running processes

**Reliability Requirements**:
- [ ] System handles 1000+ concurrent requests without failure
- [ ] Circuit breaker successfully prevents cascading failures
- [ ] Cache invalidation prevents stale data
- [ ] Rate limiting prevents API abuse
- [ ] Retry logic handles transient failures (tested with chaos engineering)

**Testing**:
- [ ] Load tests with 10,000+ requests
- [ ] Chaos engineering tests (random failures)
- [ ] Long-running stability tests (24+ hours)
- [ ] Memory profiling shows no leaks
- [ ] All quality gates from Phase 2 still passing

**Documentation**:
- [ ] Performance tuning guide
- [ ] Production deployment guide
- [ ] Monitoring and observability guide
- [ ] Troubleshooting guide

**Review Checkpoints**:
1. **After Caching Implementation**: Performance benchmarks + load tests
2. **After Batch Operations**: Integration tests + performance comparison
3. **After Resilience Features**: Chaos engineering tests + reliability validation
4. **Before Release**: Full production readiness review

**Release Criteria for v0.3.0**:
- [ ] All Phase 3 tasks completed
- [ ] All performance and reliability requirements met
- [ ] Production deployment tested with pilot users
- [ ] Zero critical bugs, <3 high-severity bugs
- [ ] Complete production deployment guide

---

## 🌟 Phase 4: Advanced Features

**Target Duration**: 4-5 weeks
**Target Version**: v1.0.0
**Goal**: Enterprise-grade features and ecosystem

### 4.1 Advanced CLI

**Objective**: Comprehensive command-line interface

**Features**:
```bash
# Interactive mode
wikijs interactive --url https://wiki.example.com --api-key TOKEN

# Page operations
wikijs pages list --filter "title:API"
wikijs pages get 123
wikijs pages create --title "New Page" --content-file content.md
wikijs pages update 123 --title "Updated Title"
wikijs pages delete 123

# Bulk operations
wikijs pages import --directory ./pages
wikijs pages export --output ./backup

# User management
wikijs users list
wikijs users create --email user@example.com --name "John Doe"

# Configuration
wikijs config init  # Create config file
wikijs config validate  # Validate config

# Health check
wikijs health check
wikijs health stats
```

**Tasks**:
1. Implement CLI with Click framework
2. Add rich formatting for output
3. Interactive mode with prompt_toolkit
4. Progress bars for long operations
5. Configuration file support

**Estimated Effort**: 12-15 hours

---

### 4.2 Plugin Architecture

**Objective**: Extensible middleware and custom providers

**Features**:
```python
from wikijs import WikiJSClient
from wikijs.plugins import LoggingPlugin, MetricsPlugin

# Custom plugin
class CustomAuthPlugin:
    def before_request(self, method, url, **kwargs):
        # Custom logic before request
        pass

    def after_request(self, response):
        # Custom logic after response
        pass

client = WikiJSClient(
    url,
    auth,
    plugins=[
        LoggingPlugin(level="DEBUG"),
        MetricsPlugin(backend="prometheus"),
        CustomAuthPlugin()
    ]
)
```

**Estimated Effort**: 10-12 hours

---

### 4.3 Webhook Support

**Objective**: React to Wiki.js events

**Features**:
```python
from wikijs.webhooks import WebhookServer

# Create webhook server
server = WebhookServer(secret="webhook-secret")

@server.on("page.created")
def on_page_created(event):
    print(f"New page created: {event.page.title}")

@server.on("page.updated")
def on_page_updated(event):
    print(f"Page updated: {event.page.title}")

# Start server
server.run(host="0.0.0.0", port=8080)
```

**Estimated Effort**: 8-10 hours

---

### Phase 4 Quality Gates

**Feature Completeness**:
- [ ] CLI covers all major operations
- [ ] Plugin system supports common use cases
- [ ] Webhook handling is reliable and secure

**Enterprise Readiness**:
- [ ] Multi-tenancy support
- [ ] Advanced security features
- [ ] Comprehensive audit logging
- [ ] Enterprise documentation

**Release Criteria for v1.0.0**:
- [ ] Feature parity with official SDKs
- [ ] Production-proven with multiple enterprises
- [ ] Complete ecosystem (CLI, plugins, webhooks)
- [ ] Comprehensive documentation and tutorials
- [ ] Active community support

---

## 📊 Implementation Tracking

### Development Velocity Metrics

| Metric | Target | Tracking Method |
|--------|--------|-----------------|
| Test Coverage | >90% | pytest-cov |
| Code Quality Score | >8.5/10 | SonarQube/CodeClimate |
| Documentation Coverage | 100% | Manual review |
| API Response Time | <100ms | Performance benchmarks |
| Bug Resolution Time | <48h | GitHub Issues |

### Progress Tracking Template

```yaml
Phase_2_Progress:
  Status: "NOT_STARTED"  # or IN_PROGRESS, COMPLETE
  Completion: 0%

  Task_2.1_Async:
    Status: "NOT_STARTED"
    Completion: 0%
    Started: null
    Completed: null
    Notes: []

  Task_2.2_API_Expansion:
    Status: "NOT_STARTED"
    Completion: 0%

    Subtask_2.2.1_Users:
      Status: "NOT_STARTED"
      Completion: 0%

    Subtask_2.2.2_Groups:
      Status: "NOT_STARTED"
      Completion: 0%

    Subtask_2.2.3_Assets:
      Status: "NOT_STARTED"
      Completion: 0%
```

---

## 🔄 Development Workflow

### 1. Phase Kickoff
- [ ] Review phase objectives and requirements
- [ ] Set up tracking in CLAUDE.md
- [ ] Create feature branches for major components
- [ ] Schedule review checkpoints

### 2. During Development
- [ ] Update progress tracking after each task
- [ ] Run tests continuously (TDD approach)
- [ ] Update documentation as features are built
- [ ] Conduct peer reviews for critical code
- [ ] Performance benchmarking for new features

### 3. Phase Completion
- [ ] All tasks completed and tested
- [ ] Documentation comprehensive and reviewed
- [ ] Performance benchmarks meet targets
- [ ] Security scan passes
- [ ] Beta testing with real users
- [ ] Retrospective meeting
- [ ] Release preparation

---

## 📝 Testing Strategy

### Test Pyramid

```
        E2E Tests (5%)
       /              \
      Integration (15%)
     /                  \
    Unit Tests (80%)
```

### Testing Requirements by Phase

**Phase 2**:
- Unit: >95% coverage
- Integration: All CRUD operations
- Performance: Async vs sync benchmarks
- Security: Auth validation

**Phase 3**:
- Load: 10,000+ requests
- Chaos: Random failure injection
- Stability: 24+ hour runs
- Cache: Hit rate >80%

**Phase 4**:
- End-to-End: Complete workflows
- CLI: All commands tested
- Plugins: Custom plugin scenarios

---

## 📖 Documentation Strategy

### Documentation Hierarchy

1. **README.md** - Quick start and overview
2. **docs/getting_started.md** - Detailed installation and setup
3. **docs/api/** - Complete API reference
4. **docs/guides/** - Feature-specific guides
5. **examples/** - Working code examples
6. **CHANGELOG.md** - Version history
7. **CONTRIBUTING.md** - Development guide

### Documentation Requirements

Each feature must include:
- [ ] API reference with all parameters documented
- [ ] At least 3 usage examples (basic, intermediate, advanced)
- [ ] Common pitfalls and troubleshooting
- [ ] Performance considerations
- [ ] Security best practices

---

## 🚀 Release Strategy

### Pre-Release Checklist

**Code Quality**:
- [ ] All tests pass
- [ ] Coverage >90%
- [ ] No critical/high security issues
- [ ] Performance benchmarks meet targets
- [ ] Code review completed

**Documentation**:
- [ ] All APIs documented
- [ ] Examples updated
- [ ] CHANGELOG.md updated
- [ ] Migration guide (if breaking changes)
- [ ] README.md updated

**Testing**:
- [ ] Integration tests pass
- [ ] Load tests pass
- [ ] Beta testing complete
- [ ] No blocking bugs

**Process**:
- [ ] Version number updated
- [ ] Git tag created
- [ ] GitHub release notes prepared
- [ ] PyPI package prepared

### Release Communication

1. **Pre-release** (1 week before):
   - Announce on GitHub Discussions
   - Share release notes draft
   - Request community feedback

2. **Release day**:
   - Publish to GitHub
   - Update documentation site
   - Social media announcement
   - Community notification

3. **Post-release** (1 week after):
   - Monitor for critical bugs
   - Respond to user feedback
   - Plan hotfix if needed

---

## 🎯 Success Metrics

### Phase 2 Success Metrics
- [ ] Async client achieves >3x throughput
- [ ] All Wiki.js APIs have coverage
- [ ] >100 downloads in first month
- [ ] >10 GitHub stars
- [ ] Zero critical bugs after 2 weeks

### Phase 3 Success Metrics
- [ ] Cache hit rate >80%
- [ ] Batch operations >10x faster
- [ ] 99.9% uptime in production
- [ ] <100ms p95 response time
- [ ] >500 downloads/month

### Phase 4 Success Metrics
- [ ] CLI adoption by >30% of users
- [ ] >5 community plugins
- [ ] >1000 downloads/month
- [ ] >50 GitHub stars
- [ ] Enterprise customer deployments

---

## 🤝 Community Engagement

### Feedback Channels
- GitHub Issues for bugs
- GitHub Discussions for features
- Discord/Slack for real-time chat
- Monthly community calls

### Contribution Opportunities
- Bug fixes and improvements
- New API endpoints
- Documentation improvements
- Example projects
- Plugin development

---

## 📅 Timeline Summary

| Phase | Duration | Target Date | Key Deliverables |
|-------|----------|-------------|------------------|
| Phase 1 | ✅ Complete | ✅ Done | MVP with Pages API |
| Phase 2 | 3-4 weeks | Week 8 | Async + Full API Coverage |
| Phase 3 | 3-4 weeks | Week 12 | Production Reliability |
| Phase 4 | 4-5 weeks | Week 17 | Enterprise Features |
| **Total** | **~17 weeks** | **~4 months** | **v1.0.0 Release** |

---

## 🎓 Key Takeaways

1. **Quality First**: Every feature includes tests and documentation
2. **Incremental Value**: Each phase delivers real user value
3. **Backward Compatible**: No breaking changes without major version
4. **Community Driven**: Engage users throughout development
5. **Production Ready**: Focus on reliability, performance, security

---

**This improvement plan ensures the Wiki.js Python SDK evolves into a world-class, enterprise-ready solution while maintaining high quality standards throughout development.**

**Next Steps**:
1. Review and approve this plan
2. Update CLAUDE.md with Phase 2 details
3. Begin Phase 2 implementation
4. Establish continuous progress tracking