Azure Cosmos DB Performance Investigator
Purpose
Investigate Azure Cosmos DB performance pathologies with evidence-first profiling instead of lazy “add more RUs” advice.
This skill is for deep performance work across:
- RU inefficiency and unexpected request-charge spikes,
- query latency and scan-heavy query behavior,
- hot partitions and partition-key skew,
- throttling, retry inflation, and client-perceived latency,
- indexing gaps and poor query/index alignment,
- container, partition, and workload-level profiling,
- diagnostic-log and metrics-backed remediation planning.
When to use
Use this skill when the user asks for:
- slow Azure Cosmos DB queries or workload latency,
- high RU cost or suspicious request-charge behavior,
- 429 throttling analysis,
- hot partition or partition-skew investigation,
- indexing or query-performance tuning,
- a step-by-step Cosmos DB profiling plan.
Do not use this skill as a substitute for:
- initial data-model design when the main problem is greenfield schema modeling,
- pure account/platform governance review when performance is incidental,
- generic application debugging unrelated to Cosmos DB workload behavior,
- vector-search-specific Mongo vCore tuning unless the user explicitly asks for that API surface.
Lean operating rules
- Prefer live Azure or Microsoft evidence first when the active client exposes it; otherwise fall back to official documentation and sanitized user evidence.
- Separate confirmed facts from inference. If state was not queried or shown, say so.
- Challenge throughput-first fixes that ignore partition skew, query scans, indexing, or client retry inflation.
- Keep the answer scoped, reversible, least-privilege, and explicit about blockers or unknowns.
References
Load these only when needed:
- MCP and evidence path - use when choosing live Azure evidence, confirming Microsoft MCP capability, or switching to documentation mode.
- Workflow and output contract - use when executing the full investigation, applying stress checks, or formatting the final answer.
- Data profiling playbook - use when you need the detailed step-by-step profiling sequence.
- Official sources - use when you need the detailed Microsoft documentation list or source notes.
Response minimum
Return, at minimum:
- the scoped target and evidence level,
- the main performance pathologies observed or still unproven,
- the safest next profiling or remediation steps,
- the assumptions or blockers that prevent stronger conclusions.
1---2name: techtide-azure-cosmosdb-performance-investigator3description: Use this skill for Azure Cosmos DB performance investigation, especially RU spikes, query latency, throttling, hot partitions, indexing inefficiency, partition-skew analysis, request-charge profiling, diagnostic-log review, and evidence-driven remediation planning.4---56# Azure Cosmos DB Performance Investigator78## Purpose910Investigate Azure Cosmos DB performance pathologies with evidence-first profiling instead of lazy “add more RUs” advice.1112This skill is for deep performance work across:1314- RU inefficiency and unexpected request-charge spikes,15- query latency and scan-heavy query behavior,16- hot partitions and partition-key skew,17- throttling, retry inflation, and client-perceived latency,18- indexing gaps and poor query/index alignment,19- container, partition, and workload-level profiling,20- diagnostic-log and metrics-backed remediation planning.2122## When to use2324Use this skill when the user asks for:2526- slow Azure Cosmos DB queries or workload latency,27- high RU cost or suspicious request-charge behavior,28- 429 throttling analysis,29- hot partition or partition-skew investigation,30- indexing or query-performance tuning,31- a step-by-step Cosmos DB profiling plan.3233Do not use this skill as a substitute for:3435- initial data-model design when the main problem is greenfield schema modeling,36- pure account/platform governance review when performance is incidental,37- generic application debugging unrelated to Cosmos DB workload behavior,38- vector-search-specific Mongo vCore tuning unless the user explicitly asks for that API surface.3940## Lean operating rules4142- Prefer live Azure or Microsoft evidence first when the active client exposes it; otherwise fall back to official documentation and sanitized user evidence.43- Separate confirmed facts from inference. If state was not queried or shown, say so.44- Challenge throughput-first fixes that ignore partition skew, query scans, indexing, or client retry inflation.45- Keep the answer scoped, reversible, least-privilege, and explicit about blockers or unknowns.4647## References4849Load these only when needed:5051- [MCP and evidence path](references/mcp-and-evidence.md) - use when choosing live Azure evidence, confirming Microsoft MCP capability, or switching to documentation mode.52- [Workflow and output contract](references/workflow-and-output.md) - use when executing the full investigation, applying stress checks, or formatting the final answer.53- [Data profiling playbook](references/data-profiling-playbook.md) - use when you need the detailed step-by-step profiling sequence.54- [Official sources](references/official-sources.md) - use when you need the detailed Microsoft documentation list or source notes.5556## Response minimum5758Return, at minimum:5960- the scoped target and evidence level,61- the main performance pathologies observed or still unproven,62- the safest next profiling or remediation steps,63- the assumptions or blockers that prevent stronger conclusions.
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