---

name: observability-pattern-detector description: Automated pattern recognition in Claude Code telemetry. Use when detecting failures, slowness, anomalies, trends, inefficiencies, conversation patterns, or tool sequences.

Observability Pattern Detector

Automated pattern recognition and anomaly detection in Claude Code telemetry data from enhanced hooks.

Data Source

Primary: {job="claude_code_enhanced"} in Loki

Operations

detect-failures

Group similar failures and identify patterns.

{job="claude_code_enhanced", event_type="tool_result", status="error"} | json

Algorithm: Group by error_type → Calculate frequency → Rank by impact. Output: Failure patterns with occurrences, affected tools, first/last seen, trend.

detect-slowness

Identify large response patterns (proxy for slowness).

{job="claude_code_enhanced", event_type="tool_result"} | json | response_length > 100000

Algorithm: Flag responses >100k chars → Group by tool → Identify patterns. Output: Slow operations with response sizes, affected tools.

detect-anomalies

Statistical anomaly detection in sessions.

{job="claude_code_enhanced", event_type="session_end"} | json | turn_count > 50

Methods: High turn count, long duration, many errors per session. Output: Anomalous sessions with metrics, likely cause.

detect-trends

Long-term trend analysis.

sum(count_over_time({job="claude_code_enhanced", event_type="tool_call"} [1d]))

Metrics: Tool usage trend, error rate trend, session frequency trend. Output: Trends with direction (increasing/decreasing/stable), rate.

detect-waste

Identify inefficiencies (redundant operations).

{job="claude_code_enhanced", event_type="tool_call"} | json | line_format "{{.tool_name}}:{{.previous_tool}}"

Patterns:

• Multiple reads of same file (Read→Read)
• Repeated failed operations
• Excessive Glob before Read
• Many small edits vs one large edit Output: Waste patterns with occurrences, recommendations.

detect-conversation-patterns

Analyze user prompt patterns.

sum by (pattern) (count_over_time({job="claude_code_enhanced", event_type="user_prompt"} | json [24h]))

Patterns:

• Question frequency (pattern="question")
• Debugging sessions (pattern="debugging")
• Creation tasks (pattern="creation")
• Ultrathink usage (pattern="ultrathink") Output: Conversation style distribution, trends.

detect-tool-sequences

Identify common tool call sequences.

{job="claude_code_enhanced", event_type="tool_call"} | json | line_format "{{.previous_tool}} → {{.tool_name}}"

Common Patterns:

• Glob → Read (file discovery)
• Read → Edit (modify after read)
• Grep → Read (search then open)
• Task → Task (parallel agents) Output: Sequence frequencies, unusual patterns.

detect-subagent-patterns

Analyze Task tool usage patterns.

{job="claude_code_enhanced", event_type="tool_call", tool="Task"} | json

Patterns:

• Subagent types distribution
• Parallel spawning patterns
• Subagent success rates Output: Subagent usage analytics, recommendations.

detect-context-issues

Identify context window problems.

{job="claude_code_enhanced", event_type="context_compact"} | json

Patterns:

• Frequent auto-compaction
• High context usage sessions
• Large response accumulation Output: Context management issues, optimization suggestions.

detect-permission-patterns

Analyze permission request patterns.

{job="claude_code_enhanced", event_type="permission_request"} | json

Patterns:

• Frequent permission requests
• Permission types distribution
• Permission denials Output: Permission friction points, automation opportunities.

detect-repo-patterns

Repository activity patterns.

sum by (repo) (count_over_time({job="claude_code_enhanced", event_type="tool_call"} | json [7d]))

Patterns:

• Most active repos
• Tool usage by repo
• Error rates by repo Output: Project-level insights, cross-repo comparisons.

Example Output

{
  "failure_patterns": [
    {
      "pattern_id": "file_not_found",
      "signature": "File does not exist",
      "occurrences": 23,
      "affected_tools": ["Read", "Edit"],
      "trend": "stable",
      "recommendation": "Add file existence check before operations"
    }
  ],
  "tool_sequence_patterns": [
    {
      "sequence": "Glob → Read → Edit",
      "occurrences": 156,
      "context": "Standard file modification flow"
    }
  ],
  "conversation_patterns": [
    {
      "pattern": "debugging",
      "percentage": 35,
      "avg_turns": 12,
      "common_tools": ["Bash", "Read", "Grep"]
    }
  ],
  "context_issues": [
    {
      "issue": "auto_compaction_frequent",
      "sessions_affected": 5,
      "recommendation": "Use more focused queries, split large tasks"
    }
  ]
}

Pattern Detection Queries

Failure Patterns

# Group errors by type
sum by (error_type, tool) (count_over_time({job="claude_code_enhanced", event_type="tool_result", status="error"} | json [24h]))

# Error timeline
{job="claude_code_enhanced", event_type="tool_result", status="error"} | json | line_format "{{.timestamp}} {{.tool_name}}: {{.error_type}}"

Tool Sequence Patterns

# Most common transitions
{job="claude_code_enhanced", event_type="tool_call"} | json | previous_tool != "" | line_format "{{.previous_tool}} → {{.tool_name}}"

Session Anomalies

# Long sessions
{job="claude_code_enhanced", event_type="session_end"} | json | duration_seconds > 3600

# High error sessions
{job="claude_code_enhanced", event_type="session_end"} | json | error_count > 5

# High turn sessions
{job="claude_code_enhanced", event_type="session_end"} | json | turn_count > 30

Context Patterns

# Auto compactions
{job="claude_code_enhanced", event_type="context_compact", trigger="auto"} | json

# High utilization
{job="claude_code_enhanced", event_type="context_utilization"} | json | context_percentage > 80

Scripts

• scripts/detect-failures.sh - Failure pattern detection
• scripts/detect-anomalies.sh - Statistical anomaly detection
• scripts/detect-trends.sh - Trend analysis
• scripts/detect-sequences.sh - Tool sequence analysis
• scripts/generate-pattern-report.sh - Full pattern report