py-wikijs/docs/plans/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:

# 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:

# 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:

# 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:

# 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:

# 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:

# 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:

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)

   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:

# 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:

# 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:

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

   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:

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)

   # 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:

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

   # 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:

# 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:

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:

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

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