القائمة
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/AJBcoding/claude-skill-eval --skill moai-context7-integrationيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Enterprise database architecture specialist with PostgreSQL 17, MySQL 8.4 LTS, MongoDB 8.0, Redis 7.4 expertise. Master connection pooling, query optimization, caching strategies, and database DevOps automation. Build scalable, resilient database systems with comprehensive monitoring and disaster recovery.
Enterprise Frontend Development with AI-powered modern architecture, Context7 integration, and intelligent component orchestration for scalable user interfaces
Enterprise-grade security expertise with production-ready patterns for OWASP Top 10 2021, zero-trust architecture, threat modeling (STRIDE, PASTA), secure SDLC, DevSecOps automation, cloud security, cryptography, identity & access management, and compliance frameworks (SOC 2, ISO 27001, GDPR, CCPA).
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | moai-context7-integration |
| version | 4.0.0 |
| created | 2025-11-12T00:00:00.000Z |
| updated | 2025-11-12T00:00:00.000Z |
| status | stable |
| tier | specialization |
| description | Enhanced context7 integration with AI-powered features |
| allowed-tools | Read, Glob, Grep, WebSearch, WebFetch, mcp__context7__resolve-library-id, mcp__context7__get-library-docs |
| primary-agent | mcp-context7-integrator |
| secondary-agents | ["alfred"] |
| keywords | ["context7","integration","api","frontend","security"] |
| tags | [] |
| orchestration | null |
| can_resume | true |
| typical_chain_position | middle |
| depends_on | [] |
Context7 Integration
Primary Agent: mcp-context7-integrator
Secondary Agents: alfred
Version: 4.0.0
Keywords: context7, integration, api, frontend, security
📚 Content
Context7 is a specialized documentation research platform that provides:
For Documentation Generation:
For Content Quality:
Library Resolution:
class Context7Client:
def __init__(self, api_key: str = None):
self.api_key = api_key or os.getenv('CONTEXT7_API_KEY')
self.base_url = 'https://api.context7.com/v1'
async def resolve_library(self, library_name: str) -> LibraryResolution:
"""
Resolve library name to Context7-compatible ID
Returns multiple matches with confidence scores
"""
endpoint = f'{self.base_url}/resolve'
params = {
'query': library_name,
'include_metadata': True,
'limit': 10
}
response = await self._make_request('GET', endpoint, params=params)
return LibraryResolution.from_response(response.json())
async def get_library_docs(self,
library_id: str,
topic: str = None,
tokens: int = 5000) -> LibraryDocumentation:
"""
Retrieve comprehensive library documentation
Args:
library_id: Context7-compatible library ID (/org/project[/version])
topic: Specific topic to focus on
tokens: Maximum tokens to retrieve
"""
endpoint = f'{self.base_url}/libraries/{library_id}/docs'
params = {}
if topic:
params['topic'] = topic
if tokens:
params['tokens'] = tokens
response = await self._make_request('GET', endpoint, params=params)
return LibraryDocumentation.from_response(response.json())
async def search_documentation(self,
query: str,
library_types: List[str] = None,
trust_score_min: int = 7) -> DocumentationSearch:
"""
Search across all Context7 documentation
Args:
query: Search query
library_types: Filter by library types (frontend, backend, etc.)
trust_score_min: Minimum trust score threshold
"""
endpoint = f'{self.base_url}/search'
params = {
'query': query,
'limit': 20
}
if library_types:
params['library_types'] = library_types
if trust_score_min:
params['trust_score_min'] = trust_score_min
response = await self._make_request('GET', endpoint, params=params)
return DocumentationSearch.from_response(response.json())
Multi-Library Research:
class MultiLibraryResearcher:
def __init__(self):
self.context7 = Context7Client()
self.cache = DocumentationCache()
async def research_topic(self, topic: str, libraries: List[str]) -> ResearchResult:
"""
Research a specific topic across multiple libraries
"""
results = {}
for library in libraries:
# Resolve library ID
resolution = await self.context7.resolve_library(library)
if not resolution.matches:
continue
best_match = resolution.get_best_match()
# Check cache first
cache_key = f"{best_match.id}:{topic}"
cached_docs = await self.cache.get(cache_key)
if cached_docs:
results[library] = cached_docs
else:
# Fetch fresh documentation
docs = await self.context7.get_library_docs(
library_id=best_match.id,
topic=topic,
tokens=3000
)
# Cache the result
await self.cache.set(cache_key, docs, ttl=3600)
results[library] = docs
return SearchResult(
topic=topic,
libraries=results,
synthesis=await self.synthesize_results(results)
)
async def synthesize_results(self, results: Dict[str, LibraryDocumentation]) -> str:
"""
Synthesize information from multiple library documentations
"""
synthesis = []
synthesis.append(f"# {topic} - Multi-Library Analysis\n")
for library, docs in results.items():
synthesis.append(f"## {library.title()}\n")
synthesis.append(f"**Source**: {docs.library_name} v{docs.version}\n")
# Extract key points
if docs.key_features:
synthesis.append("### Key Features\n")
for feature in docs.key_features:
synthesis.append(f"- {feature}")
if docs.best_practices:
synthesis.append("\n### Best Practices\n")
for practice in docs.best_practices:
synthesis.append(f"- {practice}")
synthesis.append("\n")
return '\n'.join(synthesis)
Real-time Documentation Updates:
class NextraContext7Enhancer:
def __init__(self):
self.context7 = Context7Client()
self.cache = {}
async def enhance_mdx_content(self, content: str, project_libraries: List[str]) -> str:
"""
Enhance MDX content with up-to-date Context7 information
"""
enhanced_content = content
# Extract library mentions from content
library_mentions = self.extract_library_references(content)
for library in library_mentions:
if library in project_libraries:
# Get latest documentation
docs = await self.get_cached_library_docs(library)
if docs:
# Enhance content with latest information
enhanced_content = self.enhance_library_section(
enhanced_content, library, docs
)
return enhanced_content
def extract_library_references(self, content: str) -> List[str]:
"""
Extract library names from content using regex patterns
"""
patterns = [
r'import\s+.*?from\s+[\'"]([^\'"]+)[\'"]', # Import statements
r'require\([\'"]([^\'"]+)[\'"]\)', # Require statements
r'\[`([^`]+)`\]', # Code references
r'npm install ([^\s]+)', # Installation commands
]
libraries = set()
for pattern in patterns:
matches = re.findall(pattern, content)
libraries.update(matches)
return list(libraries)
async def enhance_library_section(self, content: str, library: str, docs: LibraryDocumentation) -> str:
"""
Enhance specific library sections with latest documentation
"""
# Find library section in content
section_pattern = rf'(##\s+{re.escape(library)}[^#]*?)(?=##|$)'
def enhance_section(match):
original_section = match.group(1)
# Add Context7 version information
version_info = f"""
> **ℹ️ Documentation Updated**: {docs.last_updated} via Context7
>
> **Library Version**: {docs.version}
>
> **Trust Score**: {docs.trust_score}/10
"""
# Add latest best practices
if docs.best_practices:
best_practices = "\n### Latest Best Practices (Context7)\n"
for practice in docs.best_practices[:3]: # Top 3 practices
best_practices += f"- {practice}\n"
return original_section + version_info + best_practices
return original_section + version_info
return re.sub(section_pattern, enhance_section, content, flags=re.DOTALL)
async def add_context7_footnotes(self, content: str) -> str:
"""
Add Context7 attribution footnotes
"""
footnotes = """
---
*Documentation enhanced with Context7 - Real-time library documentation and best practices*
"""
return content + footnotes
Code Example Synchronization:
class CodeExampleManager:
def __init__(self):
self.context7 = Context7Client()
async def sync_examples_with_library(self, library_name: str, example_files: List[Path]):
"""
Synchronize code examples with latest library documentation
"""
# Resolve library
resolution = await self.context7.resolve_library(library_name)
if not resolution.matches:
raise ValueError(f"Library {library_name} not found in Context7")
library_id = resolution.get_best_match().id
# Get latest examples from Context7
docs = await self.context7.get_library_docs(
library_id=library_id,
topic="examples usage patterns",
tokens=4000
)
# Update local examples
for example_file in example_files:
await self.update_example_file(example_file, docs)
async def update_example_file(self, file_path: Path, docs: LibraryDocumentation):
"""
Update individual example file with latest patterns
"""
current_content = file_path.read_text()
# Find outdated patterns
outdated_patterns = self.find_outdated_patterns(current_content, docs)
if outdated_patterns:
# Update with latest examples
updated_content = self.update_patterns(current_content, outdated_patterns, docs)
# Add Context7 attribution
attribution = f"""
<!-- Examples updated via Context7 on {datetime.now().isoformat()} -->
<!-- Source: {docs.library_name} v{docs.version} -->
"""
file_path.write_text(updated_content + attribution)
def find_outdated_patterns(self, content: str, docs: LibraryDocumentation) -> List[OutdatedPattern]:
"""
Identify outdated code patterns in content
"""
outdated = []
# Check for deprecated APIs
if docs.deprecated_apis:
for deprecated_api in docs.deprecated_apis:
if deprecated_api.old_syntax in content:
outdated.append(OutdatedPattern(
type='deprecated_api',
old_pattern=deprecated_api.old_syntax,
new_pattern=deprecated_api.new_syntax,
description=deprecated_api.description
))
# Check for improved patterns
if docs.improved_patterns:
for improved in docs.improved_patterns:
if improved.old_pattern in content and improved.new_pattern not in content:
outdated.append(OutdatedPattern(
type='improvement',
old_pattern=improved.old_pattern,
new_pattern=improved.new_pattern,
description=improvement.description
))
return outdated
Library Comparison Research:
class LibraryComparator:
def __init__(self):
self.context7 = Context7Client()
async def compare_libraries(self, libraries: List[str], criteria: List[str]) -> ComparisonReport:
"""
Compare multiple libraries across specified criteria
"""
comparison_data = {}
for library in libraries:
# Resolve and get documentation
resolution = await self.context7.resolve_library(library)
if not resolution.matches:
continue
library_id = resolution.get_best_match().id
# Get comprehensive documentation
docs = await self.context7.get_library_docs(
library_id=library_id,
tokens=5000
)
# Extract comparison data
comparison_data[library] = self.extract_comparison_data(docs, criteria)
# Generate comparison report
return ComparisonReport(
libraries=libraries,
criteria=criteria,
data=comparison_data,
summary=self.generate_comparison_summary(comparison_data, criteria)
)
def extract_comparison_data(self, docs: LibraryDocumentation, criteria: List[str]) -> Dict:
"""
Extract specific comparison criteria from documentation
"""
data = {}
# Performance metrics
if 'performance' in criteria:
data['performance'] = {
'bundle_size': docs.bundle_size,
'startup_time': docs.startup_time,
'memory_usage': docs.memory_usage
}
# Features
if 'features' in criteria:
data['features'] = docs.key_features
# Ecosystem
if 'ecosystem' in criteria:
data['ecosystem'] = {
'community_size': docs.community_metrics.get('stars', 0),
'npm_downloads': docs.community_metrics.get('downloads', 0),
'maintainers': docs.community_metrics.get('maintainers', 0)
}
# Learning curve
if 'learning_curve' in criteria:
data['learning_curve'] = {
'documentation_quality': docs.documentation_score,
'examples_count': len(docs.examples),
'community_support': docs.community_score
}
return data
def generate_comparison_summary(self, data: Dict, criteria: List[str]) -> str:
"""
Generate readable comparison summary
"""
summary = ["# Library Comparison Summary\n"]
for criterion in criteria:
summary.append(f"## {criterion.title()}\n")
for library, lib_data in data.items():
if criterion in lib_data:
criterion_data = lib_data[criterion]
summary.append(f"### {library.title()}\n")
if isinstance(criterion_data, dict):
for key, value in criterion_data.items():
summary.append(f"- **{key.title()}**: {value}")
else:
summary.append(f"- {criterion_data}")
summary.append("")
summary.append("---\n")
return '\n'.join(summary)
Documentation Trend Monitoring:
class DocumentationTrendAnalyzer:
def __init__(self):
self.context7 = Context7Client()
self.trends_cache = {}
async def analyze_library_trends(self, library: str, timeframe: str = '30d') -> TrendReport:
"""
Analyze documentation trends for a library
"""
# Get current documentation
docs = await self.get_library_documentation(library)
# Analyze trending topics
trending_topics = await self.get_trending_topics(library, timeframe)
# Analyze community patterns
community_patterns = await self.analyze_community_patterns(library)
return TrendReport(
library=library,
current_version=docs.version,
trending_topics=trending_topics,
community_patterns=community_patterns,
recommendations=self.generate_trend_recommendations(trending_topics, docs)
)
async def get_trending_topics(self, library: str, timeframe: str) -> List[TrendingTopic]:
"""
Identify trending topics in library documentation
"""
# Search for recent mentions and patterns
search_results = await self.context7.search_documentation(
query=f"{library} best practices patterns 2024",
library_types=['framework', 'library', 'tool'],
trust_score_min=8
)
# Analyze frequency and recency
topic_frequency = {}
for result in search_results.results:
for topic in result.topics:
if topic not in topic_frequency:
topic_frequency[topic] = []
topic_frequency[topic].append(result.published_date)
# Generate trending topics
trending = []
for topic, dates in topic_frequency.items():
if len(dates) >= 3: # Mentions in at least 3 sources
trending.append(TrendingTopic(
topic=topic,
frequency=len(dates),
recency_score=self.calculate_recency_score(dates),
sources=[r.source for r in search_results.results if topic in r.topics]
))
return sorted(trending, key=lambda x: x.frequency * x.recency_score, reverse=True)
def generate_trend_recommendations(self, trending: List[TrendingTopic], docs: LibraryDocumentation) -> List[str]:
"""
Generate recommendations based on trending topics
"""
recommendations = []
for topic in trending[:5]: # Top 5 trending topics
if topic.frequency >= 5:
recommendations.append(
f"Consider adding {topic.topic} documentation - "
f"trending with {topic.frequency} recent mentions"
)
return recommendations
Context7-Powered Validation:
class Context7Validator:
def __init__(self):
self.context7 = Context7Client()
async def validate_documentation_quality(self, docs_path: Path) -> ValidationReport:
"""
Validate documentation against Context7 best practices
"""
validation_results = {}
# Extract all libraries mentioned in documentation
mentioned_libraries = self.extract_libraries_from_docs(docs_path)
for library in mentioned_libraries:
try:
# Get latest documentation from Context7
docs = await self.context7.get_library_docs(
library_id=library,
topic="best practices patterns",
tokens=3000
)
# Validate against Context7 standards
validation_results[library] = await self.validate_against_standards(
docs_path, library, docs
)
except Exception as e:
validation_results[library] = ValidationError(
library=library,
error=str(e),
severity='warning'
)
return ValidationReport(
path=str(docs_path),
libraries_validation=validation_results,
overall_score=self.calculate_overall_score(validation_results)
)
async def validate_against_standards(self, docs_path: Path, library: str, standards: LibraryDocumentation) -> LibraryValidation:
"""
Validate specific library documentation against Context7 standards
"""
issues = []
# Check for up-to-date examples
local_examples = self.extract_examples(docs_path, library)
for example in local_examples:
if not self.example_matches_best_practices(example, standards.best_practices):
issues.append(ValidationIssue(
type='outdated_example',
severity='medium',
description=f"Example may not follow current best practices for {library}",
suggestion="Update example using Context7 latest patterns"
))
# Check for deprecated APIs
deprecated_usage = self.find_deprecated_usage(docs_path, standards.deprecated_apis)
issues.extend(deprecated_usage)
# Check for missing security considerations
if standards.security_best_practices:
missing_security = self.find_missing_security_considerations(
docs_path, standards.security_best_practices
)
issues.extend(missing_security)
return LibraryValidation(
library=library,
standards_version=standards.version,
issues=issues,
score=self.calculate_library_score(issues)
)
def example_matches_best_practices(self, example: str, best_practices: List[str]) -> bool:
"""
Check if example matches current best practices
"""
example_lower = example.lower()
for practice in best_practices:
if any(keyword in example_lower for keyword in practice.lower().split()):
return True
return False
Intelligent Documentation Caching:
class Context7Cache:
def __init__(self, redis_client=None):
self.redis = redis_client
self.memory_cache = {}
self.cache_stats = CacheStats()
async def get(self, key: str) -> Optional[LibraryDocumentation]:
"""
Get cached documentation with multi-level caching
"""
# Check memory cache first
if key in self.memory_cache:
self.cache_stats.memory_hits += 1
return self.memory_cache[key]
# Check Redis cache
if self.redis:
try:
cached_data = await self.redis.get(key)
if cached_data:
self.cache_stats.redis_hits += 1
docs = LibraryDocumentation.from_json(cached_data)
# Cache in memory for faster access
self.memory_cache[key] = docs
return docs
except Exception as e:
logger.warning(f"Redis cache error: {e}")
self.cache_stats.misses += 1
return None
async def set(self, key: str, docs: LibraryDocumentation, ttl: int = 3600):
"""
Cache documentation with intelligent TTL
"""
# Cache in memory
self.memory_cache[key] = docs
# Cache in Redis if available
if self.redis:
try:
await self.redis.setex(
key,
ttl,
docs.to_json()
)
self.cache_stats.sets += 1
except Exception as e:
logger.warning(f"Redis cache set error: {e}")
def calculate_intelligent_ttl(self, docs: LibraryDocumentation) -> int:
"""
Calculate intelligent TTL based on library characteristics
"""
base_ttl = 3600 # 1 hour
# Reduce TTL for rapidly changing libraries
if docs.library_type in 'frontend framework':
return base_ttl * 2 # 2 hours
# Increase TTL for stable libraries
if docs.library_type in 'utility library':
return base_ttl * 24 # 24 hours
# Adjust based on update frequency
if hasattr(docs, 'update_frequency'):
frequency = docs.update_frequency
if frequency == 'daily':
return base_ttl
elif frequency == 'weekly':
return base_ttl * 7
elif frequency == 'monthly':
return base_ttl * 30
return base_ttl
Metadata
skill_id: moai-context7-integration
skill_name: Context7 Integration Expert
version: 1.0.0
created_date: 2025-11-11
updated_date: 2025-11-11
language: english
word_count: 1400
triggers:
- keywords: [context7, documentation research, library lookup, best practices, API reference]
- contexts: [context7-integration, docs-research, library-documentation, best-practices-sync]
agents:
- docs-manager
- doc-syncer
- research-specialist
freedom_level: high
context7_references:
- url: "https://context7.com"
topic: "Context7 platform documentation and API reference"
- url: "https://github.com/context7/context7-claude"
topic: "Context7 Claude integration patterns and best practices"
📚 Content
Context7 is a specialized documentation research platform that provides:
For Documentation Generation:
For Content Quality:
Library Resolution:
class Context7Client:
def __init__(self, api_key: str = None):
self.api_key = api_key or os.getenv('CONTEXT7_API_KEY')
self.base_url = 'https://api.context7.com/v1'
async def resolve_library(self, library_name: str) -> LibraryResolution:
"""
Resolve library name to Context7-compatible ID
Returns multiple matches with confidence scores
"""
endpoint = f'{self.base_url}/resolve'
params = {
'query': library_name,
'include_metadata': True,
'limit': 10
}
response = await self._make_request('GET', endpoint, params=params)
return LibraryResolution.from_response(response.json())
async def get_library_docs(self,
library_id: str,
topic: str = None,
tokens: int = 5000) -> LibraryDocumentation:
"""
Retrieve comprehensive library documentation
Args:
library_id: Context7-compatible library ID (/org/project[/version])
topic: Specific topic to focus on
tokens: Maximum tokens to retrieve
"""
endpoint = f'{self.base_url}/libraries/{library_id}/docs'
params = {}
if topic:
params['topic'] = topic
if tokens:
params['tokens'] = tokens
response = await self._make_request('GET', endpoint, params=params)
return LibraryDocumentation.from_response(response.json())
async def search_documentation(self,
query: str,
library_types: List[str] = None,
trust_score_min: int = 7) -> DocumentationSearch:
"""
Search across all Context7 documentation
Args:
query: Search query
library_types: Filter by library types (frontend, backend, etc.)
trust_score_min: Minimum trust score threshold
"""
endpoint = f'{self.base_url}/search'
params = {
'query': query,
'limit': 20
}
if library_types:
params['library_types'] = library_types
if trust_score_min:
params['trust_score_min'] = trust_score_min
response = await self._make_request('GET', endpoint, params=params)
return DocumentationSearch.from_response(response.json())
Multi-Library Research:
class MultiLibraryResearcher:
def __init__(self):
self.context7 = Context7Client()
self.cache = DocumentationCache()
async def research_topic(self, topic: str, libraries: List[str]) -> ResearchResult:
"""
Research a specific topic across multiple libraries
"""
results = {}
for library in libraries:
# Resolve library ID
resolution = await self.context7.resolve_library(library)
if not resolution.matches:
continue
best_match = resolution.get_best_match()
# Check cache first
cache_key = f"{best_match.id}:{topic}"
cached_docs = await self.cache.get(cache_key)
if cached_docs:
results[library] = cached_docs
else:
# Fetch fresh documentation
docs = await self.context7.get_library_docs(
library_id=best_match.id,
topic=topic,
tokens=3000
)
# Cache the result
await self.cache.set(cache_key, docs, ttl=3600)
results[library] = docs
return SearchResult(
topic=topic,
libraries=results,
synthesis=await self.synthesize_results(results)
)
async def synthesize_results(self, results: Dict[str, LibraryDocumentation]) -> str:
"""
Synthesize information from multiple library documentations
"""
synthesis = []
synthesis.append(f"# {topic} - Multi-Library Analysis\n")
for library, docs in results.items():
synthesis.append(f"## {library.title()}\n")
synthesis.append(f"**Source**: {docs.library_name} v{docs.version}\n")
# Extract key points
if docs.key_features:
synthesis.append("### Key Features\n")
for feature in docs.key_features:
synthesis.append(f"- {feature}")
if docs.best_practices:
synthesis.append("\n### Best Practices\n")
for practice in docs.best_practices:
synthesis.append(f"- {practice}")
synthesis.append("\n")
return '\n'.join(synthesis)
Real-time Documentation Updates:
class NextraContext7Enhancer:
def __init__(self):
self.context7 = Context7Client()
self.cache = {}
async def enhance_mdx_content(self, content: str, project_libraries: List[str]) -> str:
"""
Enhance MDX content with up-to-date Context7 information
"""
enhanced_content = content
# Extract library mentions from content
library_mentions = self.extract_library_references(content)
for library in library_mentions:
if library in project_libraries:
# Get latest documentation
docs = await self.get_cached_library_docs(library)
if docs:
# Enhance content with latest information
enhanced_content = self.enhance_library_section(
enhanced_content, library, docs
)
return enhanced_content
def extract_library_references(self, content: str) -> List[str]:
"""
Extract library names from content using regex patterns
"""
patterns = [
r'import\s+.*?from\s+[\'"]([^\'"]+)[\'"]', # Import statements
r'require\([\'"]([^\'"]+)[\'"]\)', # Require statements
r'\[`([^`]+)`\]', # Code references
r'npm install ([^\s]+)', # Installation commands
]
libraries = set()
for pattern in patterns:
matches = re.findall(pattern, content)
libraries.update(matches)
return list(libraries)
async def enhance_library_section(self, content: str, library: str, docs: LibraryDocumentation) -> str:
"""
Enhance specific library sections with latest documentation
"""
# Find library section in content
section_pattern = rf'(##\s+{re.escape(library)}[^#]*?)(?=##|$)'
def enhance_section(match):
original_section = match.group(1)
# Add Context7 version information
version_info = f"""
> **ℹ️ Documentation Updated**: {docs.last_updated} via Context7
>
> **Library Version**: {docs.version}
>
> **Trust Score**: {docs.trust_score}/10
"""
# Add latest best practices
if docs.best_practices:
best_practices = "\n### Latest Best Practices (Context7)\n"
for practice in docs.best_practices[:3]: # Top 3 practices
best_practices += f"- {practice}\n"
return original_section + version_info + best_practices
return original_section + version_info
return re.sub(section_pattern, enhance_section, content, flags=re.DOTALL)
async def add_context7_footnotes(self, content: str) -> str:
"""
Add Context7 attribution footnotes
"""
footnotes = """
---
*Documentation enhanced with Context7 - Real-time library documentation and best practices*
"""
return content + footnotes
Code Example Synchronization:
class CodeExampleManager:
def __init__(self):
self.context7 = Context7Client()
async def sync_examples_with_library(self, library_name: str, example_files: List[Path]):
"""
Synchronize code examples with latest library documentation
"""
# Resolve library
resolution = await self.context7.resolve_library(library_name)
if not resolution.matches:
raise ValueError(f"Library {library_name} not found in Context7")
library_id = resolution.get_best_match().id
# Get latest examples from Context7
docs = await self.context7.get_library_docs(
library_id=library_id,
topic="examples usage patterns",
tokens=4000
)
# Update local examples
for example_file in example_files:
await self.update_example_file(example_file, docs)
async def update_example_file(self, file_path: Path, docs: LibraryDocumentation):
"""
Update individual example file with latest patterns
"""
current_content = file_path.read_text()
# Find outdated patterns
outdated_patterns = self.find_outdated_patterns(current_content, docs)
if outdated_patterns:
# Update with latest examples
updated_content = self.update_patterns(current_content, outdated_patterns, docs)
# Add Context7 attribution
attribution = f"""
<!-- Examples updated via Context7 on {datetime.now().isoformat()} -->
<!-- Source: {docs.library_name} v{docs.version} -->
"""
file_path.write_text(updated_content + attribution)
def find_outdated_patterns(self, content: str, docs: LibraryDocumentation) -> List[OutdatedPattern]:
"""
Identify outdated code patterns in content
"""
outdated = []
# Check for deprecated APIs
if docs.deprecated_apis:
for deprecated_api in docs.deprecated_apis:
if deprecated_api.old_syntax in content:
outdated.append(OutdatedPattern(
type='deprecated_api',
old_pattern=deprecated_api.old_syntax,
new_pattern=deprecated_api.new_syntax,
description=deprecated_api.description
))
# Check for improved patterns
if docs.improved_patterns:
for improved in docs.improved_patterns:
if improved.old_pattern in content and improved.new_pattern not in content:
outdated.append(OutdatedPattern(
type='improvement',
old_pattern=improved.old_pattern,
new_pattern=improved.new_pattern,
description=improvement.description
))
return outdated
Library Comparison Research:
class LibraryComparator:
def __init__(self):
self.context7 = Context7Client()
async def compare_libraries(self, libraries: List[str], criteria: List[str]) -> ComparisonReport:
"""
Compare multiple libraries across specified criteria
"""
comparison_data = {}
for library in libraries:
# Resolve and get documentation
resolution = await self.context7.resolve_library(library)
if not resolution.matches:
continue
library_id = resolution.get_best_match().id
# Get comprehensive documentation
docs = await self.context7.get_library_docs(
library_id=library_id,
tokens=5000
)
# Extract comparison data
comparison_data[library] = self.extract_comparison_data(docs, criteria)
# Generate comparison report
return ComparisonReport(
libraries=libraries,
criteria=criteria,
data=comparison_data,
summary=self.generate_comparison_summary(comparison_data, criteria)
)
def extract_comparison_data(self, docs: LibraryDocumentation, criteria: List[str]) -> Dict:
"""
Extract specific comparison criteria from documentation
"""
data = {}
# Performance metrics
if 'performance' in criteria:
data['performance'] = {
'bundle_size': docs.bundle_size,
'startup_time': docs.startup_time,
'memory_usage': docs.memory_usage
}
# Features
if 'features' in criteria:
data['features'] = docs.key_features
# Ecosystem
if 'ecosystem' in criteria:
data['ecosystem'] = {
'community_size': docs.community_metrics.get('stars', 0),
'npm_downloads': docs.community_metrics.get('downloads', 0),
'maintainers': docs.community_metrics.get('maintainers', 0)
}
# Learning curve
if 'learning_curve' in criteria:
data['learning_curve'] = {
'documentation_quality': docs.documentation_score,
'examples_count': len(docs.examples),
'community_support': docs.community_score
}
return data
def generate_comparison_summary(self, data: Dict, criteria: List[str]) -> str:
"""
Generate readable comparison summary
"""
summary = ["# Library Comparison Summary\n"]
for criterion in criteria:
summary.append(f"## {criterion.title()}\n")
for library, lib_data in data.items():
if criterion in lib_data:
criterion_data = lib_data[criterion]
summary.append(f"### {library.title()}\n")
if isinstance(criterion_data, dict):
for key, value in criterion_data.items():
summary.append(f"- **{key.title()}**: {value}")
else:
summary.append(f"- {criterion_data}")
summary.append("")
summary.append("---\n")
return '\n'.join(summary)
Documentation Trend Monitoring:
class DocumentationTrendAnalyzer:
def __init__(self):
self.context7 = Context7Client()
self.trends_cache = {}
async def analyze_library_trends(self, library: str, timeframe: str = '30d') -> TrendReport:
"""
Analyze documentation trends for a library
"""
# Get current documentation
docs = await self.get_library_documentation(library)
# Analyze trending topics
trending_topics = await self.get_trending_topics(library, timeframe)
# Analyze community patterns
community_patterns = await self.analyze_community_patterns(library)
return TrendReport(
library=library,
current_version=docs.version,
trending_topics=trending_topics,
community_patterns=community_patterns,
recommendations=self.generate_trend_recommendations(trending_topics, docs)
)
async def get_trending_topics(self, library: str, timeframe: str) -> List[TrendingTopic]:
"""
Identify trending topics in library documentation
"""
# Search for recent mentions and patterns
search_results = await self.context7.search_documentation(
query=f"{library} best practices patterns 2024",
library_types=['framework', 'library', 'tool'],
trust_score_min=8
)
# Analyze frequency and recency
topic_frequency = {}
for result in search_results.results:
for topic in result.topics:
if topic not in topic_frequency:
topic_frequency[topic] = []
topic_frequency[topic].append(result.published_date)
# Generate trending topics
trending = []
for topic, dates in topic_frequency.items():
if len(dates) >= 3: # Mentions in at least 3 sources
trending.append(TrendingTopic(
topic=topic,
frequency=len(dates),
recency_score=self.calculate_recency_score(dates),
sources=[r.source for r in search_results.results if topic in r.topics]
))
return sorted(trending, key=lambda x: x.frequency * x.recency_score, reverse=True)
def generate_trend_recommendations(self, trending: List[TrendingTopic], docs: LibraryDocumentation) -> List[str]:
"""
Generate recommendations based on trending topics
"""
recommendations = []
for topic in trending[:5]: # Top 5 trending topics
if topic.frequency >= 5:
recommendations.append(
f"Consider adding {topic.topic} documentation - "
f"trending with {topic.frequency} recent mentions"
)
return recommendations
Context7-Powered Validation:
class Context7Validator:
def __init__(self):
self.context7 = Context7Client()
async def validate_documentation_quality(self, docs_path: Path) -> ValidationReport:
"""
Validate documentation against Context7 best practices
"""
validation_results = {}
# Extract all libraries mentioned in documentation
mentioned_libraries = self.extract_libraries_from_docs(docs_path)
for library in mentioned_libraries:
try:
# Get latest documentation from Context7
docs = await self.context7.get_library_docs(
library_id=library,
topic="best practices patterns",
tokens=3000
)
# Validate against Context7 standards
validation_results[library] = await self.validate_against_standards(
docs_path, library, docs
)
except Exception as e:
validation_results[library] = ValidationError(
library=library,
error=str(e),
severity='warning'
)
return ValidationReport(
path=str(docs_path),
libraries_validation=validation_results,
overall_score=self.calculate_overall_score(validation_results)
)
async def validate_against_standards(self, docs_path: Path, library: str, standards: LibraryDocumentation) -> LibraryValidation:
"""
Validate specific library documentation against Context7 standards
"""
issues = []
# Check for up-to-date examples
local_examples = self.extract_examples(docs_path, library)
for example in local_examples:
if not self.example_matches_best_practices(example, standards.best_practices):
issues.append(ValidationIssue(
type='outdated_example',
severity='medium',
description=f"Example may not follow current best practices for {library}",
suggestion="Update example using Context7 latest patterns"
))
# Check for deprecated APIs
deprecated_usage = self.find_deprecated_usage(docs_path, standards.deprecated_apis)
issues.extend(deprecated_usage)
# Check for missing security considerations
if standards.security_best_practices:
missing_security = self.find_missing_security_considerations(
docs_path, standards.security_best_practices
)
issues.extend(missing_security)
return LibraryValidation(
library=library,
standards_version=standards.version,
issues=issues,
score=self.calculate_library_score(issues)
)
def example_matches_best_practices(self, example: str, best_practices: List[str]) -> bool:
"""
Check if example matches current best practices
"""
example_lower = example.lower()
for practice in best_practices:
if any(keyword in example_lower for keyword in practice.lower().split()):
return True
return False
Intelligent Documentation Caching:
class Context7Cache:
def __init__(self, redis_client=None):
self.redis = redis_client
self.memory_cache = {}
self.cache_stats = CacheStats()
async def get(self, key: str) -> Optional[LibraryDocumentation]:
"""
Get cached documentation with multi-level caching
"""
# Check memory cache first
if key in self.memory_cache:
self.cache_stats.memory_hits += 1
return self.memory_cache[key]
# Check Redis cache
if self.redis:
try:
cached_data = await self.redis.get(key)
if cached_data:
self.cache_stats.redis_hits += 1
docs = LibraryDocumentation.from_json(cached_data)
# Cache in memory for faster access
self.memory_cache[key] = docs
return docs
except Exception as e:
logger.warning(f"Redis cache error: {e}")
self.cache_stats.misses += 1
return None
async def set(self, key: str, docs: LibraryDocumentation, ttl: int = 3600):
"""
Cache documentation with intelligent TTL
"""
# Cache in memory
self.memory_cache[key] = docs
# Cache in Redis if available
if self.redis:
try:
await self.redis.setex(
key,
ttl,
docs.to_json()
)
self.cache_stats.sets += 1
except Exception as e:
logger.warning(f"Redis cache set error: {e}")
def calculate_intelligent_ttl(self, docs: LibraryDocumentation) -> int:
"""
Calculate intelligent TTL based on library characteristics
"""
base_ttl = 3600 # 1 hour
# Reduce TTL for rapidly changing libraries
if docs.library_type in 'frontend framework':
return base_ttl * 2 # 2 hours
# Increase TTL for stable libraries
if docs.library_type in 'utility library':
return base_ttl * 24 # 24 hours
# Adjust based on update frequency
if hasattr(docs, 'update_frequency'):
frequency = docs.update_frequency
if frequency == 'daily':
return base_ttl
elif frequency == 'weekly':
return base_ttl * 7
elif frequency == 'monthly':
return base_ttl * 30
return base_ttl
✅ Validation Checklist
Note: Advanced patterns for complex scenarios.
Coming soon: Deep dive into expert-level usage.
Must-Have:
Recommended:
Security:
When to Use Context7 for This Skill:
This skill benefits from Context7 when:
Example Usage:
# Fetch latest documentation
from moai_adk.integrations import Context7Helper
helper = Context7Helper()
docs = await helper.get_docs(
library_id="/org/library",
topic="context7",
tokens=5000
)
Relevant Libraries:
| Library | Context7 ID | Use Case |
|---|---|---|
| [Library 1] | /org/lib1 | [When to use] |
When to use moai-context7-integration:
Start
├─ Need context7?
│ ├─ YES → Use this skill
│ └─ NO → Consider alternatives
└─ Complex scenario?
├─ YES → See Level 3
└─ NO → Start with Level 1
Prerequisite Skills:
Complementary Skills:
Next Steps:
Metadata
skill_id: moai-context7-integration
skill_name: Context7 Integration Expert
version: 1.0.0
created_date: 2025-11-11
updated_date: 2025-11-11
language: english
word_count: 1400
triggers:
- keywords: [context7, documentation research, library lookup, best practices, API reference]
- contexts: [context7-integration, docs-research, library-documentation, best-practices-sync]
agents:
- docs-manager
- doc-syncer
- research-specialist
freedom_level: high
context7_references:
- url: "https://context7.com"
topic: "Context7 platform documentation and API reference"
- url: "https://github.com/context7/context7-claude"
topic: "Context7 Claude integration patterns and best practices"
v4.0.0 (2025-11-12)
Generated with: MoAI-ADK Skill Factory v4.0
Last Updated: 2025-11-12
Maintained by: Primary Agent (mcp-context7-integrator)