Json logging

Skill GNSubrahmanyam/agentic-skills/skills/json-logging

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Comprehensive structured JSON logging framework with schema design, implementation patterns, security considerations, and enterprise best practices for observability and monitoring

SKILL.md

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Structured JSON Logging Best Practices

Comprehensive framework for implementing structured JSON logging across applications, covering schema design, framework setup, contextual logging, security, performance optimization, and enterprise integration patterns. Enables machine-readable logs for effective monitoring, debugging, and observability in distributed systems.

When to Apply

Reference these guidelines when:

Implementing logging in new applications or services
Migrating from unstructured text logging to structured formats
Setting up centralized logging and monitoring systems
Designing log schemas for microservices architectures
Implementing observability and tracing in distributed systems
Establishing logging standards across development teams
Optimizing log performance and storage efficiency
Ensuring log security and compliance requirements
Integrating logs with monitoring and alerting systems

Rule Categories by Priority

Priority	Category	Impact	Files	Rules
1	Schema Design	CRITICAL	3	9
2	Framework Setup	CRITICAL	3	9
3	Contextual Logging	HIGH	3	8
4	Security & Compliance	HIGH	2	7
5	Performance Optimization	MEDIUM-HIGH	2	5
6	Integration & Monitoring	MEDIUM-HIGH	2	4
7	Migration & Adoption	MEDIUM	1	3
Total	7 Categories		12 Files	45 Rules

Quick Reference

1. Schema Design (CRITICAL)

schema-base-fields: Standard fields for all log entries
schema-naming-conventions: Consistent field naming patterns
schema-data-types: Appropriate data types for different values
schema-contextual-fields: Request and trace correlation
schema-custom-fields: Application-specific field definitions
schema-validation: Schema validation and enforcement

2. Framework Setup (CRITICAL)

python-structlog: Python structured logging with structlog
python-json-logger: Python JSON logging with python-json-logger
javascript-winston: Node.js structured logging with Winston
go-logrus: Go structured logging with logrus
java-logback: Java structured logging with Logback
dotnet-serilog: .NET structured logging with Serilog
fastapi-logging: FastAPI structured logging integration
django-logging: Django structured logging integration
express-logging: Express.js structured logging middleware

3. Contextual Logging (HIGH)

correlation-ids: Request and trace ID propagation
user-context: User and session context logging
business-context: Business logic context enrichment
error-context: Error and exception context capture
performance-context: Performance and timing context

4. Security & Compliance (HIGH)

sensitive-data-protection: Preventing sensitive data in logs
pii-masking: Personal identifiable information masking
audit-logging: Security event and audit trail logging
compliance-fields: Compliance-required log fields
encryption-security: Log encryption and secure transport
access-control: Log access control and permissions

5. Performance Optimization (MEDIUM-HIGH)

log-buffering: Efficient log buffering and batching
async-logging: Non-blocking asynchronous logging
log-compression: Log compression for storage efficiency
sampling-strategies: Log sampling for high-volume scenarios
resource-limits: Memory and CPU limits for logging

6. Integration & Monitoring (MEDIUM-HIGH)

elk-integration: ELK stack structured logging integration
prometheus-metrics: Log-based metrics and alerting
opentelemetry-tracing: Distributed tracing integration
log-aggregation: Centralized log aggregation patterns

7. Migration & Adoption (MEDIUM)

migration-strategy: Gradual migration from unstructured logging
legacy-integration: Integrating with existing logging systems
team-adoption: Team training and adoption strategies

How to Use

Read individual rule files for detailed explanations and code examples:

rules/schema-base-fields.md                    # Standard log schema
rules/python-structlog.md                       # Python implementation
rules/correlation-ids.md                        # Request correlation
rules/sensitive-data-protection.md             # Security best practices
rules/elk-integration.md                       # ELK stack integration

Each rule file contains:

Impact level and category
Problem description with consequences
Solution with code examples
Common mistakes to avoid
When to apply the rule

Reference Documentation

Detailed guides for complex topics:

references/log-schema-design.md         # Complete log schema design
references/framework-comparison.md      # Logging framework comparison
references/enterprise-patterns.md        # Enterprise logging patterns

Full Compiled Document

For the complete structured JSON logging guide with all rules expanded: AGENTS.md

Structured JSON Logging Version: Compatible with modern logging frameworks and ELK stack Focus: Production-ready, enterprise-grade logging for observability and monitoring</content> <parameter name="filePath">skills/structured-json-logging-skill/SKILL.md