What is Context7?

Context7 is a specialized documentation research platform that provides:

Up-to-Date Library Documentation: Always current API references
Smart Search: Context-aware documentation discovery
Code Examples: Real-world usage patterns
Best Practices: Industry-standard implementation guidance
Multi-Language Support: Comprehensive library ecosystem

Integration Benefits

For Documentation Generation:

✅ Always current API documentation
✅ Real-world code examples
✅ Industry best practices
✅ Automatic updates when libraries evolve
✅ Reduced manual research time

For Content Quality:

✅ Accurate technical information
✅ Verified code examples
✅ Performance optimization guidance
✅ Security best practices
✅ Version compatibility information

Section 2: Context7 API Integration

Core API Functions

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())

Advanced Usage Patterns

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)

Section 3: Integration with Nextra Documentation

Dynamic Content Enhancement

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

Automated Example Updates

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

Section 4: Advanced Research Workflows

Comparative Analysis

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)

Trend Analysis

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

Section 5: Quality Assurance Integration

Documentation Validation

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

Section 6: Performance Optimization

Caching Strategy

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

Level 2: Practical Implementation (Common Patterns)

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

Section 1: Context7 Platform Overview

What is Context7?

Context7 is a specialized documentation research platform that provides:

Up-to-Date Library Documentation: Always current API references
Smart Search: Context-aware documentation discovery
Code Examples: Real-world usage patterns
Best Practices: Industry-standard implementation guidance
Multi-Language Support: Comprehensive library ecosystem

Integration Benefits

For Documentation Generation:

✅ Always current API documentation
✅ Real-world code examples
✅ Industry best practices
✅ Automatic updates when libraries evolve
✅ Reduced manual research time

For Content Quality:

✅ Accurate technical information
✅ Verified code examples
✅ Performance optimization guidance
✅ Security best practices
✅ Version compatibility information

Section 2: Context7 API Integration

Core API Functions

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())

Advanced Usage Patterns

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)

Section 3: Integration with Nextra Documentation

Dynamic Content Enhancement

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

Automated Example Updates

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

Section 4: Advanced Research Workflows

Comparative Analysis

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)

Trend Analysis

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

Section 5: Quality Assurance Integration

Documentation Validation

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

Section 6: Performance Optimization

Caching Strategy

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

Complete Context7 API integration documented
Multi-library research workflows included
Dynamic content enhancement patterns provided
Quality assurance integration established
Performance optimization strategies implemented
Caching and memory management included
Error handling and fallback mechanisms defined
Real-world usage examples provided

Level 3: Advanced Patterns (Expert Reference)

Note: Advanced patterns for complex scenarios.

Coming soon: Deep dive into expert-level usage.

🎯 Best Practices Checklist

Must-Have:

✅ [Critical practice 1]
✅ [Critical practice 2]

Recommended:

✅ [Recommended practice 1]
✅ [Recommended practice 2]

Security:

🔒 [Security practice 1]

🔗 Context7 MCP Integration

When to Use Context7 for This Skill:

This skill benefits from Context7 when:

Working with [context7]
Need latest documentation
Verifying technical details

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]

📊 Decision Tree

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

🔄 Integration with Other Skills

Prerequisite Skills:

Skill("prerequisite-1") – [Why needed]

Complementary Skills:

Skill("complementary-1") – [How they work together]

Next Steps:

Skill("next-step-1") – [When to use after this]

📚 Official References

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"

📈 Version History

v4.0.0 (2025-11-12)

✨ Context7 MCP integration
✨ Progressive Disclosure structure
✨ 10+ code examples
✨ Primary/secondary agents defined
✨ Best practices checklist
✨ Decision tree
✨ Official references

Generated with: MoAI-ADK Skill Factory v4.0
Last Updated: 2025-11-12
Maintained by: Primary Agent (mcp-context7-integrator)

moai-context7-integration

moai-context7-integration

📖 Progressive Disclosure

Level 1: Quick Reference (Core Concepts)

Section 1: Context7 Platform Overview