Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
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Use when working with Azure Cosmos Db — azure Cosmos DB throughput analysis, partition key distribution, consistency levels, metrics monitoring, and database management via Azure CLI.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Manage and analyze Azure Cosmos DB accounts using az cosmosdb commands.
ALWAYS discover before acting. Never assume account names, database names, container names, or throughput values.
# Discover Cosmos DB accounts
az cosmosdb list --output json \
--query "[].{name:name, rg:resourceGroup, kind:kind, apiKind:databaseAccountOfferType, locations:locations[].locationName, consistencyPolicy:consistencyPolicy.defaultConsistencyLevel}"
ALL independent operations MUST run in parallel using background jobs (&) and wait.
for acct in $(echo "$accounts" | jq -c '.[]'); do
{
name=$(echo "$acct" | jq -r '.name')
rg=$(echo "$acct" | jq -r '.rg')
az cosmosdb sql database list --account-name "$name" --resource-group "$rg" --output json
} &
done
wait
# List SQL databases in an account
list_databases() {
local acct="$1" rg="$2"
az cosmosdb sql database list --account-name "$acct" --resource-group "$rg" --output json \
--query "[].{name:name, throughput:options.throughput, autoscaleMax:options.autoscaleMaxThroughput}"
}
# List containers in a database
list_containers() {
local acct="$1" rg="$2" db="$3"
az cosmosdb sql container list --account-name "$acct" --resource-group "$rg" --database-name "$db" --output json \
--query "[].{name:name, partitionKey:resource.partitionKey.paths[0], indexingMode:resource.indexingPolicy.indexingMode, ttl:resource.defaultTtl}"
}
# Get container throughput
get_throughput() {
local acct="$1" rg="$2" db="$3" container="$4"
az cosmosdb sql container throughput show --account-name "$acct" --resource-group "$rg" \
--database-name "$db" --name "$container" --output json \
--query "{throughput:resource.throughput, autoscaleMax:resource.autoscaleMaximumThroughput, minimumThroughput:resource.minimumThroughput}"
}
# Get Cosmos DB metrics
get_cosmos_metrics() {
local resource_id="$1" metric="$2"
az monitor metrics list --resource "$resource_id" --metric "$metric" \
--interval PT1H --aggregation Average Total --output json
}
accounts=$(az cosmosdb list --output json --query "[].{name:name, rg:resourceGroup}")
for acct in $(echo "$accounts" | jq -c '.[]'); do
{
name=$(echo "$acct" | jq -r '.name')
rg=$(echo "$acct" | jq -r '.rg')
echo "=== Account: $name ==="
az cosmosdb show --name "$name" --resource-group "$rg" --output json \
--query "{consistency:consistencyPolicy.defaultConsistencyLevel, multiWrite:enableMultipleWriteLocations, locations:locations[].{loc:locationName, failoverPriority:failoverPriority}, backupPolicy:backupPolicy.type}"
list_databases "$name" "$rg"
} &
done
wait
# Check RU consumption vs provisioned throughput
resource_id=$(az cosmosdb show --name "$ACCT" --resource-group "$RG" --query "id" -o tsv)
az monitor metrics list --resource "$resource_id" \
--metric "TotalRequestUnits" "NormalizedRUConsumption" "ProvisionedThroughput" \
--interval PT1H --aggregation Average Total Max --output json
# Check for 429 (throttled) requests
az monitor metrics list --resource "$resource_id" \
--metric "TotalRequests" --interval PT1H --aggregation Count \
--filter "StatusCode eq '429'" --output json
# Check partition key ranges and usage
az cosmosdb sql container show --account-name "$ACCT" --resource-group "$RG" \
--database-name "$DB" --name "$CONTAINER" --output json \
--query "{partitionKey:resource.partitionKey, conflictResolution:resource.conflictResolutionPolicy}"
# Get partition-level metrics (hot partitions)
az monitor metrics list --resource "$resource_id" \
--metric "NormalizedRUConsumption" --interval PT5M --aggregation Max \
--dimension "PartitionKeyRangeId" --output json
# Check account-level consistency
az cosmosdb show --name "$ACCT" --resource-group "$RG" --output json \
--query "{defaultConsistency:consistencyPolicy.defaultConsistencyLevel, maxStaleness:consistencyPolicy.maxStalenessPrefix, maxInterval:consistencyPolicy.maxIntervalInSeconds}"
az cosmosdb show --name "$ACCT" --resource-group "$RG" --output json \
--query "{backupPolicy:backupPolicy, locations:locations[].{name:locationName, priority:failoverPriority, isZoneRedundant:isZoneRedundant}, enableAutomaticFailover:enableAutomaticFailover}"
Present results as a structured report:
Azure Cosmos Db Report
══════════════════════
Resources discovered: [count]
Resource Status Key Metric Issues
──────────────────────────────────────────────
[name] [ok/warn] [value] [findings]
Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]
Target ≤50 lines of output. Use tables for multi-resource comparisons.
--help output.| Shortcut | Counter | Why |
|---|---|---|
| "I'll skip discovery and check known resources" | Always run Phase 1 discovery first | Resource names change, new resources appear — assumed names cause errors |
| "The user only asked for a quick check" | Follow the full discovery → analysis flow | Quick checks miss critical issues; structured analysis catches silent failures |
| "Default configuration is probably fine" | Audit configuration explicitly | Defaults often leave logging, security, and optimization features disabled |
| "Metrics aren't needed for this" | Always check relevant metrics when available | API/CLI responses show current state; metrics reveal trends and intermittent issues |
| "I don't have access to that" | Try the command and report the actual error | Assumed permission failures prevent useful investigation; actual errors are informative |
autoscaleMaximumThroughput is the max -- actual consumption may be much lower. Always check NormalizedRUConsumption metric.NormalizedRUConsumption on a single partition range indicates a hot partition. Check partition key design.capacityMode in account properties.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Cloudflare GraphQL Analytics for zone traffic, firewall events, Workers metrics, and schema exploration. Use when querying Cloudflare analytics data or exploring the GraphQL API.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Alloydb — google AlloyDB instance analysis, query insights, columnar engine optimization, maintenance windows, and cluster health.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Aqua — aqua Security platform analysis. Covers container runtime protection, image assurance policies, compliance frameworks, vulnerability management, workload protection, and registry scanning. Use when analyzing container security posture, reviewing image compliance, investigating runtime alerts, or auditing security policies.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Cassandra — apache Cassandra keyspace analysis, compaction strategies, repair status, nodetool operations, and cluster health monitoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan results.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.