Azure Cosmos DB Performance Investigator

SkillCloud & infra

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Azure Cosmos DB Performance Investigator skill

What this skill tells your AI

The instructions your AI receives, as published by vincentchuwaichow/vanguard-frontier-agentic in skills/azure/azure-cosmosdb-performance-investigator/SKILL.md and read by ahel’s review.

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 Microsoft Learn documentation through the user's configured documentation MCP, then sampled read-only Azure evidence when the active client exposes it, then 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:

  • Operations guide — use for service-specific pitfalls, design rules, verification targets, and pushback criteria.
  • MCP and evidence path — use when choosing documentation-based evidence, sampled read-only Azure evidence, or sanitized user evidence.
  • Safety checklist — use for evidence labels, risk gates, mutation boundaries, approval rules, and credential boundaries.
  • 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.

Signals

GitHub stars
22
Forks
3
Last commit
Sep 2026

ahel review

  • S4info
    community integration — published by vincentchuwaichow, not azure

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Catalog kind
skill
Gateway key
azure-cosmosdb-performance-investigator
Source
github.com/vincentchuwaichow/vanguard-frontier-agentic