Azure Cosmos DB Performance Investigator
SkillCloud & infraUse 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.
No other account needed.
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