LO

logging-api-requests

Implement secure, structured API request logging for better auditability and performance tracking.

Install

mkdir -p .claude/skills/logging-api-requests && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6019" && unzip -o skill.zip -d .claude/skills/logging-api-requests && rm skill.zip

Installs to .claude/skills/logging-api-requests

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Monitor and log API requests with correlation IDs, performance metrics,
71 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Capture API request metadata in JSON format
  • Generate and propagate correlation IDs
  • Redact PII from request and response bodies
  • Log performance metrics and response timing
  • Implement security audit trails for sensitive operations

How it works

The skill adds middleware to intercept API requests, generating or propagating correlation IDs and logging metadata. It applies PII redaction rules and tags sensitive operations for audit indexing.

Inputs & outputs

You give it
API request and response stream
You get back
Structured JSON log entries with correlation IDs and audit tags

When to use logging-api-requests

  • Add request logging middleware
  • Implement security audit trails
  • Track API performance metrics

About this skill

Logging API Requests

Overview

Implement structured API request logging with correlation IDs, performance timing, security audit trails, and PII redaction. Capture request/response metadata in JSON format suitable for aggregation in ELK Stack, Loki, or CloudWatch Logs, enabling debugging, performance analysis, and compliance auditing across distributed services.

Prerequisites

  • Structured logging library: Pino or Winston (Node.js), structlog (Python), Logback with JSON encoder (Java)
  • Log aggregation system: ELK Stack (Elasticsearch, Logstash, Kibana), Grafana Loki, or CloudWatch Logs
  • Correlation ID propagation mechanism (middleware-injected or from incoming X-Request-ID header)
  • PII data classification for the API domain (which fields contain personal data requiring redaction)
  • Log retention and rotation policy defined per compliance requirements

Instructions

  1. Examine existing logging configuration using Grep and Read to identify current log format, output destinations, and any structured logging already in place.
  2. Implement request logging middleware that captures: timestamp (ISO 8601), correlation ID, HTTP method, URL path (without query string PII), status code, response time (ms), request size, response size, and client IP.
  3. Generate a unique correlation ID (X-Request-ID) for each request if not provided by the caller, and propagate it to all downstream service calls and log entries within the request scope.
  4. Add PII redaction rules that mask sensitive fields (passwords, tokens, SSNs, email addresses) in logged request/response bodies using configurable field-path patterns.
  5. Implement log levels per context: info for successful requests, warn for 4xx client errors, error for 5xx server errors with stack traces, and debug for request/response bodies (development only).
  6. Configure response body logging for error responses only (4xx/5xx), capturing the error payload for debugging while skipping successful response bodies to reduce log volume.
  7. Add security audit logging for sensitive operations: authentication attempts, permission changes, data exports, and admin actions, tagged with audit: true for separate indexing.
  8. Set up log rotation and retention policies: 30 days for application logs, 90 days for audit logs, with automatic compression of logs older than 7 days.
  9. Write tests verifying that PII redaction works correctly, correlation IDs propagate through nested calls, and log output matches expected JSON structure.

See ${CLAUDE_SKILL_DIR}/references/implementation.md for the full implementation guide.

Output

  • ${CLAUDE_SKILL_DIR}/src/middleware/request-logger.js - Structured request/response logging middleware
  • ${CLAUDE_SKILL_DIR}/src/middleware/correlation-id.js - Correlation ID generation and propagation
  • ${CLAUDE_SKILL_DIR}/src/utils/pii-redactor.js - Field-level PII redaction with configurable patterns
  • ${CLAUDE_SKILL_DIR}/src/utils/audit-logger.js - Security audit event logger for sensitive operations
  • ${CLAUDE_SKILL_DIR}/src/config/logging.js - Log level, format, and output destination configuration
  • ${CLAUDE_SKILL_DIR}/tests/logging/ - Logging middleware tests including PII redaction verification

Error Handling

ErrorCauseSolution
Log volume overwhelming storageHigh-traffic endpoint logging full request/response bodiesLog bodies only for errors; sample successful request bodies at configurable rate (1%)
PII leak in logsNew field added to API response containing personal data not covered by redaction rulesMaintain allowlist of loggable fields rather than blocklist; audit log output regularly
Correlation ID missingUpstream service does not propagate X-Request-ID headerGenerate new correlation ID when header is absent; log warning about missing upstream propagation
Log parsing failureLog message contains unescaped characters breaking JSON structureUse structured logging library that handles serialization; never concatenate user input into log strings
Audit log gapAsync logging dropped events during high-load periodUse synchronous logging for audit events; implement write-ahead buffer for audit trail completeness

Refer to ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error patterns.

Examples

Structured JSON log entry: {"timestamp":"2026-03-10T14:30:00Z","correlationId":"abc-123","method":"POST","path":"/api/users","status":201,"durationMs":45,"userId":"usr_456","audit":false} -- every field queryable in log aggregation.

Distributed tracing correlation: Propagate X-Request-ID from API gateway through 3 microservices, enabling a single Kibana query to show the complete request lifecycle across all services.

Compliance audit trail: Tag all data modification operations (POST, PUT, DELETE) with audit: true, capturing the authenticated user, modified resource ID, and change summary for SOC 2 compliance evidence.

See ${CLAUDE_SKILL_DIR}/references/examples.md for additional examples.

Resources

When not to use it

  • High-traffic endpoints where logging full bodies causes storage overflow
  • Environments lacking structured log aggregation systems

Prerequisites

Structured logging library like Pino, Winston, or structlogLog aggregation system such as ELK, Loki, or CloudWatchPII data classification for the API domain

Limitations

  • Asynchronous logging may drop events during high-load periods
  • Log volume can overwhelm storage if full bodies are logged for all endpoints

How it compares

Unlike manual logging, this approach enforces structured JSON output and automated correlation ID propagation across distributed services.

Compared to similar skills

logging-api-requests side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
logging-api-requests (this skill)126dReviewIntermediate
analyzing-logs1426dReviewBeginner
obsidian-observability526dReviewIntermediate
instruments-profiling32moNo flagsAdvanced

Try saying

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