style: format all files with prettier

plugins/error-diagnostics/commands/error-analysis.md

@@ -19,12 +19,14 @@ The analysis scope may include specific error messages, stack traces, log files,
 Classify errors into these categories to inform your debugging strategy:
 
 **By Severity:**
+
 - **Critical**: System down, data loss, security breach, complete service unavailability
 - **High**: Major feature broken, significant user impact, data corruption risk
 - **Medium**: Partial feature degradation, workarounds available, performance issues
 - **Low**: Minor bugs, cosmetic issues, edge cases with minimal impact
 
 **By Type:**
+
 - **Runtime Errors**: Exceptions, crashes, segmentation faults, null pointer dereferences
 - **Logic Errors**: Incorrect behavior, wrong calculations, invalid state transitions
 - **Integration Errors**: API failures, network timeouts, external service issues
@@ -33,6 +35,7 @@ Classify errors into these categories to inform your debugging strategy:
 - **Security Errors**: Authentication failures, authorization violations, injection attempts
 
 **By Observability:**
+
 - **Deterministic**: Consistently reproducible with known inputs
 - **Intermittent**: Occurs sporadically, often timing or race condition related
 - **Environmental**: Only happens in specific environments or configurations
@@ -106,6 +109,7 @@ For errors in microservices and distributed systems:
 Extract maximum information from stack traces:
 
 **Key Elements:**
+
 - **Error Type**: What kind of exception/error occurred
 - **Error Message**: Contextual information about the failure
 - **Origin Point**: The deepest frame where the error was thrown
@@ -114,6 +118,7 @@ Extract maximum information from stack traces:
 - **Async Boundaries**: Identify where asynchronous operations break the trace
 
 **Analysis Strategy:**
+
 1. Start at the top of the stack (origin of error)
 2. Identify the first frame in your application code (not framework/library)
 3. Examine that frame's context: input parameters, local variables, state
@@ -134,28 +139,34 @@ Modern error tracking tools provide enhanced stack traces:
 ### Common Stack Trace Patterns
 
 **Pattern: Null Pointer Exception Deep in Framework Code**
+
 ```
 NullPointerException
   at java.util.HashMap.hash(HashMap.java:339)
   at java.util.HashMap.get(HashMap.java:556)
   at com.myapp.service.UserService.findUser(UserService.java:45)
 ```
+
 Root Cause: Application passed null to framework code. Focus on UserService.java:45.
 
 **Pattern: Timeout After Long Wait**
+
 ```
 TimeoutException: Operation timed out after 30000ms
   at okhttp3.internal.http2.Http2Stream.waitForIo
   at com.myapp.api.PaymentClient.processPayment(PaymentClient.java:89)
 ```
+
 Root Cause: External service slow/unresponsive. Need retry logic and circuit breaker.
 
 **Pattern: Race Condition in Concurrent Code**
+
 ```
 ConcurrentModificationException
   at java.util.ArrayList$Itr.checkForComodification
   at com.myapp.processor.BatchProcessor.process(BatchProcessor.java:112)
 ```
+
 Root Cause: Collection modified while being iterated. Need thread-safe data structures or synchronization.
 
 ## Log Aggregation and Pattern Matching
@@ -165,6 +176,7 @@ Root Cause: Collection modified while being iterated. Need thread-safe data stru
 Implement JSON-based structured logging for machine-readable logs:
 
 **Standard Log Schema:**
+
 ```json
 {
   "timestamp": "2025-10-11T14:23:45.123Z",
@@ -203,6 +215,7 @@ Implement JSON-based structured logging for machine-readable logs:
 ```
 
 **Key Fields to Always Include:**
+
 - `timestamp`: ISO 8601 format in UTC
 - `level`: ERROR, WARN, INFO, DEBUG, TRACE
 - `correlation_id`: Unique ID for the entire request chain
@@ -216,48 +229,52 @@ Implement JSON-based structured logging for machine-readable logs:
 Implement correlation IDs to track requests across distributed systems:
 
 **Node.js/Express Middleware:**
+
 ```javascript
-const { v4: uuidv4 } = require('uuid');
-const asyncLocalStorage = require('async-local-storage');
+const { v4: uuidv4 } = require("uuid");
+const asyncLocalStorage = require("async-local-storage");
 
 // Middleware to generate/propagate correlation ID
 function correlationIdMiddleware(req, res, next) {
-  const correlationId = req.headers['x-correlation-id'] || uuidv4();
+  const correlationId = req.headers["x-correlation-id"] || uuidv4();
   req.correlationId = correlationId;
-  res.setHeader('x-correlation-id', correlationId);
+  res.setHeader("x-correlation-id", correlationId);
 
   // Store in async context for access in nested calls
   asyncLocalStorage.run(new Map(), () => {
-    asyncLocalStorage.set('correlationId', correlationId);
+    asyncLocalStorage.set("correlationId", correlationId);
     next();
   });
 }
 
 // Propagate to downstream services
 function makeApiCall(url, data) {
-  const correlationId = asyncLocalStorage.get('correlationId');
+  const correlationId = asyncLocalStorage.get("correlationId");
   return axios.post(url, data, {
     headers: {
-      'x-correlation-id': correlationId,
-      'x-source-service': 'api-gateway'
-    }
+      "x-correlation-id": correlationId,
+      "x-source-service": "api-gateway",
+    },
   });
 }
 
 // Include in all log statements
 function log(level, message, context = {}) {
-  const correlationId = asyncLocalStorage.get('correlationId');
-  console.log(JSON.stringify({
-    timestamp: new Date().toISOString(),
-    level,
-    correlation_id: correlationId,
-    message,
-    ...context
-  }));
+  const correlationId = asyncLocalStorage.get("correlationId");
+  console.log(
+    JSON.stringify({
+      timestamp: new Date().toISOString(),
+      level,
+      correlation_id: correlationId,
+      message,
+      ...context,
+    }),
+  );
 }
 ```
 
 **Python/Flask Implementation:**
+
 ```python
 import uuid
 import logging
@@ -302,6 +319,7 @@ def log_structured(level, message, **context):
 ### Log Aggregation Architecture
 
 **Centralized Logging Pipeline:**
+
 1. **Application**: Outputs structured JSON logs to stdout/stderr
 2. **Log Shipper**: Fluentd/Fluent Bit/Vector collects logs from containers
 3. **Log Aggregator**: Elasticsearch/Loki/DataDog receives and indexes logs
@@ -309,6 +327,7 @@ def log_structured(level, message, **context):
 5. **Alerting**: Trigger alerts on error patterns and thresholds
 
 **Log Query Examples (Elasticsearch DSL):**
+
 ```json
 // Find all errors for a specific correlation ID
 {
@@ -382,6 +401,7 @@ Use log analysis to identify patterns:
 For deterministic errors in development:
 
 **Debugger Setup:**
+
 1. Set breakpoint before the error occurs
 2. Step through code execution line by line
 3. Inspect variable values and object state
@@ -390,6 +410,7 @@ For deterministic errors in development:
 6. Modify variables to test hypotheses
 
 **Modern Debugging Tools:**
+
 - **VS Code Debugger**: Integrated debugging for JavaScript, Python, Go, Java, C++
 - **Chrome DevTools**: Frontend debugging with network, performance, and memory profiling
 - **pdb/ipdb (Python)**: Interactive debugger with post-mortem analysis
@@ -412,6 +433,7 @@ For errors in production environments where debuggers aren't available:
 8. **Traffic Mirroring**: Replay production traffic in staging for safe investigation
 
 **Remote Debugging (Use Cautiously):**
+
 - Attach debugger to running process only in non-critical services
 - Use read-only breakpoints that don't pause execution
 - Time-box debugging sessions strictly
@@ -420,10 +442,11 @@ For errors in production environments where debuggers aren't available:
 ### Memory and Performance Debugging
 
 **Memory Leak Detection:**
+
 ```javascript
 // Node.js heap snapshot comparison
-const v8 = require('v8');
-const fs = require('fs');
+const v8 = require("v8");
+const fs = require("fs");
 
 function takeHeapSnapshot(filename) {
   const snapshot = v8.writeHeapSnapshot(filename);
@@ -431,15 +454,16 @@ function takeHeapSnapshot(filename) {
 }
 
 // Take snapshots at intervals
-takeHeapSnapshot('heap-before.heapsnapshot');
+takeHeapSnapshot("heap-before.heapsnapshot");
 // ... run operations that might leak ...
-takeHeapSnapshot('heap-after.heapsnapshot');
+takeHeapSnapshot("heap-after.heapsnapshot");
 
 // Analyze in Chrome DevTools Memory profiler
 // Look for objects with increasing retained size
 ```
 
 **Performance Profiling:**
+
 ```python
 # Python profiling with cProfile
 import cProfile
@@ -465,6 +489,7 @@ def profile_function():
 ### Input Validation and Type Safety
 
 **Defensive Programming:**
+
 ```typescript
 // TypeScript: Leverage type system for compile-time safety
 interface PaymentRequest {
@@ -477,19 +502,19 @@ interface PaymentRequest {
 function processPayment(request: PaymentRequest): PaymentResult {
   // Runtime validation for external inputs
   if (request.amount <= 0) {
-    throw new ValidationError('Amount must be positive');
+    throw new ValidationError("Amount must be positive");
   }
 
-  if (!['USD', 'EUR', 'GBP'].includes(request.currency)) {
-    throw new ValidationError('Unsupported currency');
+  if (!["USD", "EUR", "GBP"].includes(request.currency)) {
+    throw new ValidationError("Unsupported currency");
   }
 
   // Use Zod or Yup for complex validation
   const schema = z.object({
     amount: z.number().positive().max(1000000),
-    currency: z.enum(['USD', 'EUR', 'GBP']),
+    currency: z.enum(["USD", "EUR", "GBP"]),
     customerId: z.string().uuid(),
-    paymentMethodId: z.string().min(1)
+    paymentMethodId: z.string().min(1),
   });
 
   const validated = schema.parse(request);
@@ -500,6 +525,7 @@ function processPayment(request: PaymentRequest): PaymentResult {
 ```
 
 **Python Type Hints and Validation:**
+
 ```python
 from typing import Optional
 from pydantic import BaseModel, validator, Field
@@ -532,6 +558,7 @@ def process_payment(request: PaymentRequest) -> PaymentResult:
 ### Error Boundaries and Graceful Degradation
 
 **React Error Boundaries:**
+
 ```typescript
 import React, { Component, ErrorInfo, ReactNode } from 'react';
 import * as Sentry from '@sentry/react';
@@ -589,6 +616,7 @@ export default ErrorBoundary;
 ```
 
 **Circuit Breaker Pattern:**
+
 ```python
 from datetime import datetime, timedelta
 from enum import Enum
@@ -672,8 +700,8 @@ async function retryWithBackoff<T>(
     maxAttempts: 3,
     baseDelayMs: 1000,
     maxDelayMs: 30000,
-    exponentialBase: 2
-  }
+    exponentialBase: 2,
+  },
 ): Promise<T> {
   let lastError: Error;
 
@@ -684,23 +712,27 @@ async function retryWithBackoff<T>(
       lastError = error as Error;
 
       // Check if error is retryable
-      if (options.retryableErrors &&
-          !options.retryableErrors.includes(error.name)) {
+      if (
+        options.retryableErrors &&
+        !options.retryableErrors.includes(error.name)
+      ) {
         throw error; // Don't retry non-retryable errors
       }
 
       if (attempt < options.maxAttempts - 1) {
         const delay = Math.min(
           options.baseDelayMs * Math.pow(options.exponentialBase, attempt),
-          options.maxDelayMs
+          options.maxDelayMs,
         );
 
         // Add jitter to prevent thundering herd
         const jitter = Math.random() * 0.1 * delay;
         const actualDelay = delay + jitter;
 
-        console.log(`Attempt ${attempt + 1} failed, retrying in ${actualDelay}ms`);
-        await new Promise(resolve => setTimeout(resolve, actualDelay));
+        console.log(
+          `Attempt ${attempt + 1} failed, retrying in ${actualDelay}ms`,
+        );
+        await new Promise((resolve) => setTimeout(resolve, actualDelay));
       }
     }
   }
@@ -710,14 +742,14 @@ async function retryWithBackoff<T>(
 
 // Usage
 const result = await retryWithBackoff(
-  () => fetch('https://api.example.com/data'),
+  () => fetch("https://api.example.com/data"),
   {
     maxAttempts: 3,
     baseDelayMs: 1000,
     maxDelayMs: 10000,
     exponentialBase: 2,
-    retryableErrors: ['NetworkError', 'TimeoutError']
-  }
+    retryableErrors: ["NetworkError", "TimeoutError"],
+  },
 );
 ```
 
@@ -726,6 +758,7 @@ const result = await retryWithBackoff(
 ### Modern Observability Stack (2025)
 
 **Recommended Architecture:**
+
 - **Metrics**: Prometheus + Grafana or DataDog
 - **Logs**: Elasticsearch/Loki + Fluentd or DataDog Logs
 - **Traces**: OpenTelemetry + Jaeger/Tempo or DataDog APM
@@ -736,9 +769,10 @@ const result = await retryWithBackoff(
 ### Sentry Integration
 
 **Node.js/Express Setup:**
+
 ```javascript
-const Sentry = require('@sentry/node');
-const { ProfilingIntegration } = require('@sentry/profiling-node');
+const Sentry = require("@sentry/node");
+const { ProfilingIntegration } = require("@sentry/profiling-node");
 
 Sentry.init({
   dsn: process.env.SENTRY_DSN,
@@ -766,11 +800,11 @@ Sentry.init({
     event.tags = {
       ...event.tags,
       region: process.env.AWS_REGION,
-      instance_id: process.env.INSTANCE_ID
+      instance_id: process.env.INSTANCE_ID,
     };
 
     return event;
-  }
+  },
 });
 
 // Express middleware
@@ -790,19 +824,19 @@ function processOrder(orderId) {
   } catch (error) {
     Sentry.captureException(error, {
       tags: {
-        operation: 'process_order',
-        order_id: orderId
+        operation: "process_order",
+        order_id: orderId,
       },
       contexts: {
         order: {
           id: orderId,
           status: order?.status,
-          amount: order?.amount
-        }
+          amount: order?.amount,
+        },
       },
       user: {
-        id: order?.customerId
-      }
+        id: order?.customerId,
+      },
     });
     throw error;
   }
@@ -812,6 +846,7 @@ function processOrder(orderId) {
 ### DataDog APM Integration
 
 **Python/Flask Setup:**
+
 ```python
 from ddtrace import patch_all, tracer
 from ddtrace.contrib.flask import TraceMiddleware
@@ -854,6 +889,7 @@ def charge_payment():
 ### OpenTelemetry Implementation
 
 **Go Service with OpenTelemetry:**
+
 ```go
 package main
 
@@ -968,7 +1004,7 @@ monitors:
 
   - name: "New Error Type Detected"
     type: log
-    query: "logs(\"level:ERROR service:payment-service\").rollup(\"count\").by(\"error.fingerprint\").last(\"5m\") > 0"
+    query: 'logs("level:ERROR service:payment-service").rollup("count").by("error.fingerprint").last("5m") > 0'
     message: |
       New error type detected in payment service: {{error.fingerprint}}
 
@@ -1001,6 +1037,7 @@ monitors:
 ### Incident Response Workflow
 
 **Phase 1: Detection and Triage (0-5 minutes)**
+
 1. Acknowledge the alert/incident
 2. Check incident severity and user impact
 3. Assign incident commander
@@ -1008,6 +1045,7 @@ monitors:
 5. Update status page if customer-facing
 
 **Phase 2: Investigation (5-30 minutes)**
+
 1. Gather observability data:
    - Error rates from Sentry/DataDog
    - Traces showing failed requests
@@ -1022,6 +1060,7 @@ monitors:
 4. Document findings in incident log
 
 **Phase 3: Mitigation (Immediate)**
+
 1. Implement immediate fix based on hypothesis:
    - Rollback recent deployment
    - Scale up resources
@@ -1032,6 +1071,7 @@ monitors:
 3. Monitor for 15-30 minutes to ensure stability
 
 **Phase 4: Recovery and Validation**
+
 1. Verify all systems operational
 2. Check data consistency
 3. Process queued/failed requests
@@ -1039,6 +1079,7 @@ monitors:
 5. Notify stakeholders
 
 **Phase 5: Post-Incident Review**
+
 1. Schedule postmortem within 48 hours
 2. Create detailed timeline of events
 3. Identify root cause (may differ from initial hypothesis)
@@ -1090,6 +1131,7 @@ GET /logs-*/_search
 ### Communication Templates
 
 **Initial Incident Notification:**
+
 ```
 🚨 INCIDENT: Payment Processing Errors
 
@@ -1113,6 +1155,7 @@ Status Page: https://status.company.com/incident/abc123
 ```
 
 **Mitigation Notification:**
+
 ```
 ✅ INCIDENT UPDATE: Mitigation Applied
 

plugins/error-diagnostics/commands/smart-debug.md

@@ -5,6 +5,7 @@ You are an expert AI-assisted debugging specialist with deep knowledge of modern
 Process issue from: $ARGUMENTS
 
 Parse for:
+
 - Error messages/stack traces
 - Reproduction steps
 - Affected components/services
@@ -15,7 +16,9 @@ Parse for:
 ## Workflow
 
 ### 1. Initial Triage
+
 Use Task tool (subagent_type="debugger") for AI-powered analysis:
+
 - Error pattern recognition
 - Stack trace analysis with probable causes
 - Component dependency analysis
@@ -24,7 +27,9 @@ Use Task tool (subagent_type="debugger") for AI-powered analysis:
 - Recommend debugging strategy
 
 ### 2. Observability Data Collection
+
 For production/staging issues, gather:
+
 - Error tracking (Sentry, Rollbar, Bugsnag)
 - APM metrics (DataDog, New Relic, Dynatrace)
 - Distributed traces (Jaeger, Zipkin, Honeycomb)
@@ -32,6 +37,7 @@ For production/staging issues, gather:
 - Session replays (LogRocket, FullStory)
 
 Query for:
+
 - Error frequency/trends
 - Affected user cohorts
 - Environment-specific patterns
@@ -40,7 +46,9 @@ Query for:
 - Deployment timeline correlation
 
 ### 3. Hypothesis Generation
+
 For each hypothesis include:
+
 - Probability score (0-100%)
 - Supporting evidence from logs/traces/code
 - Falsification criteria
@@ -48,6 +56,7 @@ For each hypothesis include:
 - Expected symptoms if true
 
 Common categories:
+
 - Logic errors (race conditions, null handling)
 - State management (stale cache, incorrect transitions)
 - Integration failures (API changes, timeouts, auth)
@@ -56,6 +65,7 @@ Common categories:
 - Data corruption (schema mismatches, encoding)
 
 ### 4. Strategy Selection
+
 Select based on issue characteristics:
 
 **Interactive Debugging**: Reproducible locally → VS Code/Chrome DevTools, step-through
@@ -65,7 +75,9 @@ Select based on issue characteristics:
 **Statistical**: Small % of cases → Delta debugging, compare success vs failure
 
 ### 5. Intelligent Instrumentation
+
 AI suggests optimal breakpoint/logpoint locations:
+
 - Entry points to affected functionality
 - Decision nodes where behavior diverges
 - State mutation points
@@ -75,6 +87,7 @@ AI suggests optimal breakpoint/logpoint locations:
 Use conditional breakpoints and logpoints for production-like environments.
 
 ### 6. Production-Safe Techniques
+
 **Dynamic Instrumentation**: OpenTelemetry spans, non-invasive attributes
 **Feature-Flagged Debug Logging**: Conditional logging for specific users
 **Sampling-Based Profiling**: Continuous profiling with minimal overhead (Pyroscope)
@@ -82,7 +95,9 @@ Use conditional breakpoints and logpoints for production-like environments.
 **Gradual Traffic Shifting**: Canary deploy debug version to 10% traffic
 
 ### 7. Root Cause Analysis
+
 AI-powered code flow analysis:
+
 - Full execution path reconstruction
 - Variable state tracking at decision points
 - External dependency interaction analysis
@@ -92,7 +107,9 @@ AI-powered code flow analysis:
 - Fix complexity estimation
 
 ### 8. Fix Implementation
+
 AI generates fix with:
+
 - Code changes required
 - Impact assessment
 - Risk level
@@ -100,19 +117,23 @@ AI generates fix with:
 - Rollback strategy
 
 ### 9. Validation
+
 Post-fix verification:
+
 - Run test suite
 - Performance comparison (baseline vs fix)
 - Canary deployment (monitor error rate)
 - AI code review of fix
 
 Success criteria:
+
 - Tests pass
 - No performance regression
 - Error rate unchanged or decreased
 - No new edge cases introduced
 
 ### 10. Prevention
+
 - Generate regression tests using AI
 - Update knowledge base with root cause
 - Add monitoring/alerts for similar issues
@@ -127,7 +148,7 @@ Success criteria:
 const analysis = await aiAnalyze({
   error: "Payment processing timeout",
   frequency: "5% of checkouts",
-  environment: "production"
+  environment: "production",
 });
 // AI suggests: "Likely N+1 query or external API timeout"
 
@@ -136,7 +157,7 @@ const sentryData = await getSentryIssue("CHECKOUT_TIMEOUT");
 const ddTraces = await getDataDogTraces({
   service: "checkout",
   operation: "process_payment",
-  duration: ">5000ms"
+  duration: ">5000ms",
 });
 
 // 3. Analyze traces
@@ -144,8 +165,8 @@ const ddTraces = await getDataDogTraces({
 // Hypothesis: N+1 query in payment method loading
 
 // 4. Add instrumentation
-span.setAttribute('debug.queryCount', queryCount);
-span.setAttribute('debug.paymentMethodId', methodId);
+span.setAttribute("debug.queryCount", queryCount);
+span.setAttribute("debug.paymentMethodId", methodId);
 
 // 5. Deploy to 10% traffic, monitor
 // Confirmed: N+1 pattern in payment verification
@@ -162,6 +183,7 @@ span.setAttribute('debug.paymentMethodId', methodId);
 ## Output Format
 
 Provide structured report:
+
 1. **Issue Summary**: Error, frequency, impact
 2. **Root Cause**: Detailed diagnosis with evidence
 3. **Fix Proposal**: Code changes, risk, impact