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azure-cosmosdb-performance-investigator

Use esta skill para investigação de desempenho do Azure Cosmos DB, especialmente picos de RU, latência de consulta, throttling, partições quentes, ineficiência de indexação, análise de desvio de partição, perfil de custo de requisição, revisão de logs de diagnóstico e planejamento de remediação baseado em evidências.

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Atualizado há 4 meses

Ver no GitHub ↗ Licença: Apache-2.0

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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](references/mcp-and-evidence.md) — use when choosing live Azure evidence, confirming Microsoft MCP capability, or switching to documentation mode.
  • [Workflow and output contract](references/workflow-and-output.md) — use when executing the full investigation, applying stress checks, or formatting the final answer.
  • [Data profiling playbook](references/data-profiling-playbook.md) — use when you need the detailed step-by-step profiling sequence.
  • [Official sources](references/official-sources.md) — 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.
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