jaeger-analysis

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Jaeger distributed tracing analysis. Use when investigating request latency, tracing errors across services, finding slow spans, or understanding service dependencies.

incidentfox By incidentfox schedule Updated 2/19/2026
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name: jaeger-analysis description: Jaeger distributed tracing analysis. Use when investigating request latency, tracing errors across services, finding slow spans, or understanding service dependencies. allowed-tools: Bash(python *)

Jaeger Tracing Analysis

Authentication

IMPORTANT: Credentials are injected automatically by a proxy layer. Do NOT check for JAEGER_URL or other credentials in environment variables - they won't be visible to you. Just run the scripts directly; authentication is handled transparently.

Configuration environment variables you CAN check (non-secret):

  • JAEGER_URL - Jaeger Query API URL (e.g., http://jaeger-query:16686)

MANDATORY: Statistics-First Investigation

NEVER dump all traces. Always follow this pattern:

SERVICES → OPERATIONS → STATISTICS → SAMPLE TRACES
  1. List Services - Know what services exist
  2. List Operations - Understand endpoints/operations per service
  3. Get Statistics - Error rates, latency percentiles
  4. Sample Traces - Get specific traces after understanding the landscape

Available Scripts

All scripts are in .claude/skills/observability-jaeger/scripts/

SERVICE DISCOVERY

list_services.py - List All Traced Services

python .claude/skills/observability-jaeger/scripts/list_services.py

# Output: List of all services sending traces to Jaeger

list_operations.py - List Operations for a Service

python .claude/skills/observability-jaeger/scripts/list_operations.py <service>

# Example:
python .claude/skills/observability-jaeger/scripts/list_operations.py frontend

TRACE INVESTIGATION

get_traces.py - Search for Traces

python .claude/skills/observability-jaeger/scripts/get_traces.py --service SERVICE [OPTIONS]

# Options:
#   --operation OPERATION  Filter by operation name
#   --tags KEY=VALUE       Filter by tags (can repeat)
#   --min-duration MS      Minimum duration in milliseconds
#   --max-duration MS      Maximum duration in milliseconds
#   --limit N              Max traces to return (default: 20)
#   --lookback HOURS       How far back to search (default: 1)

# Examples:
python .claude/skills/observability-jaeger/scripts/get_traces.py --service frontend --limit 10
python .claude/skills/observability-jaeger/scripts/get_traces.py --service checkout --min-duration 500
python .claude/skills/observability-jaeger/scripts/get_traces.py --service api --operation "HTTP GET /users" --limit 5
python .claude/skills/observability-jaeger/scripts/get_traces.py --service payment --tags error=true

get_trace.py - Get Full Trace by ID

python .claude/skills/observability-jaeger/scripts/get_trace.py <trace-id>

# Example:
python .claude/skills/observability-jaeger/scripts/get_trace.py abc123def456789

LATENCY ANALYSIS

get_slow_traces.py - Find Slow Traces

python .claude/skills/observability-jaeger/scripts/get_slow_traces.py --service SERVICE [OPTIONS]

# Options:
#   --min-duration MS      Minimum duration threshold (default: 1000)
#   --operation OPERATION  Filter by specific operation
#   --limit N              Max traces to return (default: 20)
#   --lookback HOURS       How far back to search (default: 1)

# Examples:
python .claude/skills/observability-jaeger/scripts/get_slow_traces.py --service checkout --min-duration 500
python .claude/skills/observability-jaeger/scripts/get_slow_traces.py --service api --operation "POST /orders"

get_latency_stats.py - Latency Statistics

python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service SERVICE [OPTIONS]

# Options:
#   --operation OPERATION  Filter by operation
#   --lookback HOURS       Time window (default: 1)

# Example:
python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service frontend
python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service checkout --operation "POST /checkout"

ERROR ANALYSIS

get_error_traces.py - Find Traces with Errors

python .claude/skills/observability-jaeger/scripts/get_error_traces.py --service SERVICE [OPTIONS]

# Options:
#   --operation OPERATION  Filter by operation
#   --limit N              Max traces (default: 20)
#   --lookback HOURS       Time window (default: 1)

# Example:
python .claude/skills/observability-jaeger/scripts/get_error_traces.py --service payment
python .claude/skills/observability-jaeger/scripts/get_error_traces.py --service api --operation "POST /checkout"

Investigation Workflow

Standard Latency Investigation

┌─────────────────────────────────────────────────────────────┐
│ 1. LIST SERVICES                                              │
│    python list_services.py                                    │
│    → Identify which service to investigate                    │
└─────────────────────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│ 2. GET LATENCY STATS                                          │
│    python get_latency_stats.py --service X                    │
│    → See p50, p95, p99 latencies per operation                │
└─────────────────────────────────────────────────────────────┘
                             │
                             ▼
                   High Latency Found?
               ┌─────────────┴─────────────┐
               │                           │
              YES                          NO
               │                           │
               ▼                           ▼
┌─────────────────────────────┐  ┌───────────────────────────────────────────┐
│ 3a. GET SLOW TRACES         │  │ 3b. CHECK ERRORS                          │
│    python get_slow_traces.py│  │    python get_error_traces.py --service X │
│    --service X              │  │    → Look for error patterns              │
│    → Analyze slow paths     │  └───────────────────────────────────────────┘
└─────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────┐
│ 4. ANALYZE SPECIFIC TRACE                                     │
│    python get_trace.py <trace-id>                             │
│    → See full span tree, find bottleneck                      │
└─────────────────────────────────────────────────────────────┘

Quick Commands Reference

Goal Command
List all services list_services.py
List operations list_operations.py <service>
Get latency stats get_latency_stats.py --service X
Find slow traces get_slow_traces.py --service X --min-duration 500
Find error traces get_error_traces.py --service X
Get specific trace get_trace.py <trace-id>
Search with tags get_traces.py --service X --tags http.status_code=500

Trace Anatomy

Span Structure

Trace (trace_id: abc123)
├── Span: frontend (span_id: 001, duration: 250ms)
│   ├── Span: api-gateway (span_id: 002, duration: 200ms)
│   │   ├── Span: auth-service (span_id: 003, duration: 50ms)
│   │   └── Span: order-service (span_id: 004, duration: 120ms) ← bottleneck
│   │       └── Span: database (span_id: 005, duration: 100ms) ← root cause
│   └── Span: cache-lookup (span_id: 006, duration: 5ms)

Common Tags

  • http.method - HTTP method (GET, POST, etc.)
  • http.url - Request URL
  • http.status_code - Response status code
  • error - Boolean, true if span has error
  • span.kind - client, server, producer, consumer
  • db.type - Database type (mysql, postgres, redis)
  • db.statement - Database query (may be truncated)

Finding Bottlenecks

  1. Sort spans by duration (longest first)
  2. Look for the critical path (spans on the main request flow)
  3. Check if slow span has child spans (slow child = propagated latency)
  4. Check tags for error=true or high status codes

Common Patterns

Find Slow Database Queries

# Find traces with slow DB operations
python .claude/skills/observability-jaeger/scripts/get_traces.py \
  --service order-service \
  --tags db.type=postgres \
  --min-duration 100

Find HTTP Errors

# Find 5xx errors
python .claude/skills/observability-jaeger/scripts/get_traces.py \
  --service api-gateway \
  --tags http.status_code=500

# Or use error traces script
python .claude/skills/observability-jaeger/scripts/get_error_traces.py --service api-gateway

Compare Latency Across Services

# Get stats for each service
python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service frontend
python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service api
python .claude/skills/observability-jaeger/scripts/get_latency_stats.py --service database

Anti-Patterns to Avoid

  1. NEVER fetch all traces - Always use filters (service, time, duration)
  2. Skip service discovery - Always start with list_services.py
  3. Ignore latency stats - Get percentiles before diving into individual traces
  4. Focus on single spans - Look at the full trace context
  5. Miss error tags - Always check for error=true in slow traces
  6. Unbounded time ranges - Always specify --lookback for time bounds

Output Format

When reporting trace findings, use this structure:

## Trace Analysis Summary

### Service: [service name]
### Time Window: [start] to [end]

### Latency Statistics
| Operation | p50 | p95 | p99 | Count |
|-----------|-----|-----|-----|-------|
| GET /api  | 50ms| 150ms| 300ms| 1000|

### Slow Traces Found
1. **Trace ID**: abc123
   - **Duration**: 2.5s
   - **Bottleneck**: database span (1.8s)
   - **Root Cause**: Slow query on orders table

### Error Traces Found
1. **Trace ID**: def456
   - **Error**: Connection refused to payment-service
   - **Impact**: 5xx returned to client

### Root Cause Hypothesis
[Based on trace analysis]

### Recommended Action
[Specific remediation step]
Install via CLI
npx skills add https://github.com/incidentfox/incidentfox --skill jaeger-analysis
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