{"id":"0587659a-acaa-4123-a09c-bf5eab188d2f","shortId":"JXqTMG","kind":"skill","title":"dt-obs-problems","tagline":">-","description":"# Problem Analysis Skill\n\nAnalyze Dynatrace AI-detected problems including root cause identification, impact assessment, and correlation with logs and metrics.\n\n---\n\n## Use Cases\n\n### 1. Active Problem Triage\n- **Goal:** List and prioritize currently active problems\n- **Trigger:** \"active problems\", \"what problems are open\", \"current issues\", \"availability issues\"\n- **Done:** Prioritized list of active problems with category, user impact, and display IDs\n\n### 2. Root Cause Investigation\n- **Goal:** Identify the root cause entity for a specific problem\n- **Trigger:** \"root cause of P-12345\", \"what caused this problem\", \"which entity is the root cause\"\n- **Done:** Root cause entity identified with affected entity list and blast radius\n\n### 3. Problem Trending\n- **Goal:** Analyze problem patterns over time to identify recurring issues\n- **Trigger:** \"recurring problems\", \"problem history\", \"problem trends last 30 days\"\n- **Done:** Trend data showing problem frequency, recurring root causes, and resolution times\n\n---\n\n## Overview\n\nDynatrace automatically detects anomalies, performance degradations, and failures across your environment, creating **problems** that aggregate related alert, warning and info-level events and provide root cause and impact insights.\n\n### What are Problems?\n\nProblems are automatically detected, software and infrastructure health and resilience issues that:\n\n- **Automatically correlate** related alert, warning, and info-level events across services, infrastructure, frontend applications, and user sessions\n- **Identify root causes** using causal analysis of Smartscape dependencies\n- **Assess business impact** by tracking affected users and services\n- **Reduce alert noise** by grouping related symptoms into single problems that share the same root cause and impact\n- **Track problem lifecycle** from early detection through resolution\n\n### Event Kinds\n\nThe `event.kind` field (stable, permission) identifies the high-level event type:\n\n| `event.kind` value | Description |\n|---|---|\n| `DAVIS_EVENT` | Davis-detected infrastructure/application events |\n| `BIZ_EVENT` | Business events (ingested via API or captured from spans) |\n| `RUM_EVENT` | Real User Monitoring events |\n| `AUDIT_EVENT` | Administrative/security audit events |\n\n`event.provider` (stable, permission) identifies the event source.\n\n## Problem Categories\n\nCommon `event.category` values:\n\n| Category | Description | Example |\n|----------|-------------|---------|\n| **AVAILABILITY** | Infrastructure or service unavailable | Web service returns no data, synthetic test actively fails, database connection lost |\n| **ERROR** | Increased error rates beyond baseline | API error rate jumped from 0.1% to 15% |\n| **SLOWDOWN** | Performance degradation | Response time increased from 200ms to 5000ms |\n| **RESOURCE** | Resource saturation | Container memory at 95%, causing OOM kills |\n| **CUSTOM** | Custom anomaly detections | Business KPI (orders/minute) dropped below threshold |\n\n## Problem Lifecycle\n\n```text\nDetection → ACTIVE → Under Investigation → CLOSED\n```\n\n- **ACTIVE**: Currently occurring issues requiring attention\n- **CLOSED**: Resolved issues used for historical analysis\n\n## Essential Fields\n\n### Common Field Name Mistakes\n\n| ❌ WRONG | ✅ CORRECT | Description |\n|---------|-----------|-------------|\n| `title` | `event.name` | Problem title/description |\n| `status` | `event.status` | Problem lifecycle status |\n| `severity` | `event.category` | Problem type/category |\n| `start` | `event.start` | Problem start time |\n\n### Correct Status Values\n\n```dql\n// ✅ CORRECT: Use these status values\nfetch dt.davis.problems\n| filter event.status == \"ACTIVE\"   // Currently occurring problems\n//     or event.status == \"CLOSED\"  // Resolved problems\n// ❌ INCORRECT: event.status == \"OPEN\" does not exist!\n| limit 1\n```\n\n### Key Fields Reference\n\n```dql\nfetch dt.davis.problems, from:now() - 1h\n| filter not(dt.davis.is_duplicate)\n| fields\n    event.start,                          // Problem start timestamp\n    event.end,                            // Problem end timestamp (if closed)\n    display_id,                           // Human-readable problem ID (P-XXXXX)\n    event.name,                           // Problem title\n    event.description,                    // Detailed description\n    event.category,                       // Problem type\n    event.status,                         // ACTIVE or CLOSED\n    dt.smartscape_source.id,              // The smartscape ID for the affected resource\n    dt.davis.affected_users_count,        // Number of affected users\n    smartscape.affected_entity.ids,        // Array of affected entity IDs\n    dt.smartscape.service,                // Affected services (may be array)\n    dt.davis.root_cause_entity,           // Entity identified as root cause\n    root_cause_entity_id,                 // Root cause entity ID\n    root_cause_entity_name,               // Human-readable root cause name\n    dt.davis.is_duplicate,                // Whether duplicate detection\n    dt.davis.is_rootcause                 // Root cause vs. symptom\n| limit 10\n```\n\n## Standard Query Pattern\n\nAlways start problem queries with this foundation:\n\n```dql\nfetch dt.davis.problems, from:now() - 2h\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| fields event.start, display_id, event.name, event.category\n| sort event.start desc\n| limit 20\n```\n\n**Key components:**\n\n- `fetch dt.davis.problems` - The problems data source\n- `not(dt.davis.is_duplicate)` - Filter out duplicate detections\n- `event.status == \"ACTIVE\"` - Show only active problems\n- Time range - Always specify a reasonable window\n\n## Common Query Patterns\n\n### Active Problems by Category\n\n```dql\nfetch dt.davis.problems\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| summarize problem_count = count(), by: {event.category}\n| sort problem_count desc\n```\n\n### High-Impact Active Problems (affecting many users)\n\n```dql\nfetch dt.davis.problems\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| filter dt.davis.affected_users_count > 100\n| fields event.start, display_id, event.name, dt.davis.affected_users_count, event.category\n| sort dt.davis.affected_users_count desc\n```\n\n### High-Impact Active Problems (affecting many smartscape entities)\n\n```dql\nfetch dt.davis.problems\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| filter arraySize(affected_entity_ids) > 5\n| fields event.start, display_id, event.name, affected_entity_ids, event.category, impacted_entity_count = arraySize(affected_entity_ids)\n| sort impacted_entity_count desc\n```\n\n### Specific Problem Details\n\n```dql\nfetch dt.davis.problems\n| filter display_id == \"P-XXXXXXXXXX\"\n| fields event.start, event.end, event.name, event.description, affected_entity_ids, dt.davis.affected_users_count, root_cause_entity_id, root_cause_entity_name\n```\n\n### Service-Specific Problem History\n\n```dql\nfetch dt.davis.problems, from:now() - 7d\n| filter not(dt.davis.is_duplicate)\n| filter in(dt.smartscape.service, toSmartscapeId(\"SERVICE-XXXXXXXXX\"))\n| summarize problems = count(), by: {event.category, event.status}\n```\n\n## Root Cause Analysis Patterns\n\n### Basic Root Cause Query\n\n```dql\nfetch dt.davis.problems, from:now() - 24h\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| fields\n    display_id,\n    event.name,\n    event.description,\n    root_cause_entity_id,\n    root_cause_entity_name,\n    smartscape.affected_entity.ids\n```\n\n### Root Cause by Entity Type\n\nIdentify which entity types most frequently cause problems:\n\n```dql\nfetch dt.davis.problems, from:now() - 7d\n| filter not(dt.davis.is_duplicate)\n| filter isNotNull(root_cause_entity_id)\n| summarize problem_count = count(), by:{root_cause_entity_name}\n| sort problem_count desc\n| limit 20\n```\n\n### Affected entity is an AWS resource \n\n```dql\nfetch dt.davis.problems, from:now() - 24h\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| filter matchesPhrase(arrayToString(smartscape.affected_entity.types, delimiter:\",\"), \"AWS_\")\n```\n\n\n### Infrastructure Root Cause with Service Impact\n\n```dql\nfetch dt.davis.problems, from:now() - 30m\n| filter not(dt.davis.is_duplicate) and event.status == \"ACTIVE\"\n| filter matchesPhrase(root_cause_entity_id, \"HOST-\")\n| filter isNotNull(dt.smartscape.service)\n| fields display_id, event.name, root_cause_entity_name, dt.smartscape.service\n```\n\n### Problem Blast Radius\n\nCalculate entity impact per root cause:\n\n```dql\nfetch dt.davis.problems, from:now() - 7d\n| filter not(dt.davis.is_duplicate)\n| filter isNotNull(root_cause_entity_id)\n| fieldsAdd affected_count = arraySize(smartscape.affected_entity.ids)\n| summarize\n    avg_affected = avg(affected_count),\n    max_affected = max(affected_count),\n    problem_count = count(),\n    by:{root_cause_entity_name}\n| sort avg_affected desc\n```\n\n### Recurring Root Causes\n\nIdentify entities repeatedly causing problems:\n\n```dql\nfetch dt.davis.problems, from:now() - 24h\n| filter not(dt.davis.is_duplicate)\n| filter isNotNull(root_cause_entity_id)\n| summarize\n    problem_count = count(),\n    first_occurrence = min(event.start),\n    last_occurrence = max(event.start),\n    by:{root_cause_entity_id, root_cause_entity_name}\n| filter problem_count > 3\n| sort problem_count desc\n```\n\n### Cause Category vs. Root Cause Entity\n\nThese are different questions — pick the right approach:\n\n- **\"What causes problems?\"** / **\"most common cause\"** → Summarize by `event.category`\n  (SLOWDOWN, ERROR, RESOURCE, AVAILABILITY, CUSTOM). Explain what triggers each category.\n- **\"Which entity causes problems?\"** / **\"root cause entity\"** → Group by\n  `root_cause_entity_name`. Lists specific services, hosts, or apps.\n\n**Cause category breakdown** (use when asked about common causes, patterns, or types):\n\n```dql\nfetch dt.davis.problems, from:now() - 30d\n| filter not(dt.davis.is_duplicate)\n| summarize problem_count = count(), by: {event.category}\n| sort problem_count desc\n```\n\nThen for each category, explain what triggers it using the Problem Categories table and\ncite specific entities from the tenant data as examples.\n\n## Problem Trending and Pattern Analysis\n\nTrack problem trends over time, identify recurring issues, and analyze resolution performance.\n\n**Primary Files:**\n- `references/problem-trending.md` - Timeseries analysis and pattern detection\n\n**Common Use Cases:**\n- Active problems over time with `makeTimeseries`\n- Problem creation rate by category\n- Recurring problem detection by schedule\n- Resolution time trends and P95 duration analysis\n\n**Key Techniques:**\n- **`makeTimeseries`** vs **`bin()`**: Choose the right approach for lifecycle spans vs discrete events\n- **NULL handling**: Use `coalesce(event.end, now())` for active problems\n- **Peak hours analysis**: Identify when problems occur most frequently\n- **Impact trending**: Track user impact changes over time\n\nSee `references/problem-trending.md` for complete query patterns and best practices.\n\n## Cross-Domain Problem Queries\n\n### Problems Associated with Kubernetes Clusters\n\nUse `affected_entity_ids` or `dt.smartscape_source.id` to find problems related to Kubernetes:\n\n```dql\nfetch dt.davis.problems, from:now() - 7d\n| filter not(dt.davis.is_duplicate)\n| filter matchesPhrase(dt.smartscape_source.id, \"KUBERNETES_CLUSTER\")\n    OR matchesPhrase(dt.smartscape_source.id, \"K8S_\")\n| fields event.start, display_id, event.name, event.category, event.status,\n    dt.smartscape_source.id, affected_entity_ids\n| sort event.start desc\n```\n\nAlternative: expand affected entities and filter for K8s entity types:\n\n```dql\nfetch dt.davis.problems, from:now() - 7d\n| filter not(dt.davis.is_duplicate)\n| expand entity_id = affected_entity_ids\n| filter matchesPhrase(entity_id, \"KUBERNETES_CLUSTER\")\n    OR matchesPhrase(entity_id, \"K8S_\")\n| fields event.start, display_id, event.name, event.category, entity_id\n| sort event.start desc\n```\n\n### Simple Problem Listing\n\nList all problems from the last 24 hours (common request):\n\n```dql\nfetch dt.davis.problems, from:now() - 24h\n| filter not(dt.davis.is_duplicate)\n| fields event.start, event.end, display_id, event.name, event.category, event.status\n| sort event.start desc\n```\n\n## Response Construction\n\n### Problem Cause Summaries\n\nWhen summarizing problem causes, categories, or patterns, provide a **comprehensive\nbreakdown** across all standard categories present in the data: AVAILABILITY, ERROR,\nSLOWDOWN, RESOURCE, and CUSTOM. For each category found:\n\n1. **Category name** and count of problems\n2. **What triggers it** — brief explanation (e.g., RESOURCE = CPU/memory/disk threshold\n   exceeded; AVAILABILITY = service or entity became unreachable)\n3. **Specific examples** from the tenant's data (affected entity names, problem IDs)\n\nDo not stop after the first two categories — users expect the full picture. Reference\nthe Problem Categories table above for trigger descriptions.\n\n### Analysis Results\n\nWhen presenting query results:\n- Include **entity names** (not just IDs) — but choose the efficient method:\n  - **Few entities (< 5):** `get-entity-name` calls are fine\n  - **Many entities:** Use `query-problems` tool which returns names directly, or\n    include `root_cause_entity_name` / `entityName()` in the DQL query to resolve\n    names inline. Avoid calling `get-entity-name` in a loop for 10+ entities —\n    this can exhaust the tool call limit and return no answer at all.\n- Provide **actionable recommendations** aligned to the identified causes\n- Organize by frequency or impact for easy prioritization\n\n## Best Practices\n\n### Essential Rules\n\n1. **Always filter duplicates**: Use `not(dt.davis.is_duplicate)` to avoid counting the same problem multiple times\n2. **Use correct status values**: `\"ACTIVE\"` or `\"CLOSED\"`, never `\"OPEN\"`\n3. **Specify time ranges**: Always include time bounds to optimize performance\n4. **Include display_id**: Essential for problem identification and linking\n5. **Test incrementally**: Add one filter or field at a time when building queries\n6. **Filter early**: Apply `not(dt.davis.is_duplicate)` immediately after fetch\n\n### Query Development\n\n- **Start simple**: Begin with basic filtering, then add complexity\n- **Test fields first**: Run with `| limit 1` to verify field names exist\n- **Use meaningful time ranges**: Too broad wastes resources, too narrow misses data\n- **Document problem IDs**: Always capture and store `display_id` for reference\n\n### Root Cause Verification\n\n- Always filter `isNotNull(root_cause_entity_id)` when required\n- Cross-reference events using `dt.davis.event_ids`\n- Consider time delays: root cause may appear in logs minutes before problem\n\n### Time Range Guidelines\n\n```dql\n// ✅ GOOD - Specific time range\nfetch dt.davis.problems, from:now() - 4h\n```\n\n```dql\n// ❌ BAD - Scans all historical data\nfetch dt.davis.problems\n```\n\n## Troubleshooting\n\n| Problem | Cause | Solution |\n|---------|-------|----------|\n| No problems returned | Using `event.status == \"OPEN\"` | Use `\"ACTIVE\"` or `\"CLOSED\"` — `\"OPEN\"` does not exist |\n| Duplicate problems in results | Missing deduplication filter | Add `filter not(dt.davis.is_duplicate)` immediately after fetch |\n| Wrong field name (`title`, `status`, `severity`) | SQL-like naming | Use `event.name`, `event.status`, `event.category` — see field name table above |\n| `root_cause_entity_id` is null | Not all problems have identified root causes | Add `filter isNotNull(root_cause_entity_id)` when querying root causes |\n| Query scans too much data / times out | Missing time range | Always specify `from:now() - <duration>` on the fetch command |\n| `affected_entity_ids` is empty array | Problem has no mapped affected entities | Check `dt.smartscape.service` or `dt.smartscape_source.id` as alternatives |\n\n## When to Load References\n\n### Load [problem-trending.md](references/problem-trending.md) when:\n- Analyzing problem frequency over time\n- Detecting recurring problems on a schedule\n- Calculating resolution time trends and P95 durations\n- Comparing problem creation rates by category\n\n### Load [problem-correlation.md](references/problem-correlation.md) when:\n- Correlating problems with logs or other telemetry\n- Investigating events that preceded a problem\n- Linking problems to deployment or config changes\n\n### Load [impact-analysis.md](references/impact-analysis.md) when:\n- Assessing business impact (affected users, services)\n- Calculating blast radius for a root cause entity\n- Prioritizing problems by technical and user impact\n\n## References\n\n- [problem-trending.md](references/problem-trending.md) — Problem trending and timeseries analysis patterns\n- [problem-correlation.md](references/problem-correlation.md) — Correlating problems with logs and other telemetry\n- [impact-analysis.md](references/impact-analysis.md) — Business and technical impact assessment\n- [problem-merging.md](references/problem-merging.md) — When and why DAVIS merges events into problems\n\n## Related Skills\n\n- **dt-dql-essentials** - Core DQL syntax and query structure for problem queries\n- **dt-obs-logs** - Correlate problems with application and infrastructure logs\n- **dt-obs-tracing** - Investigate problems through distributed 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