Cloud Monitoring Promql Query
GoogleOfficialPublished by Google
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for translating Google Cloud Monitoring metric types into valid PromQL queries. It handles the normalization of metric names, selection of appropriate aggregation functions based on metric kind and value type, and ensures queries adhere to Cloud Monitoring's specific resource filtering requirements. The skill includes a validation step using a dedicated linter to ensure query correctness before output.
What it does
- Normalizes Cloud Monitoring metric types into PromQL-compatible naming conventions.
- Maps metric descriptors (GAUGE, DELTA, CUMULATIVE) to correct PromQL aggregation structures.
- Enforces mandatory resource filtering to prevent cross-service metric collisions.
- Validates generated queries using a local Python-based PromQL linter.
- Handles complex distribution metrics by correctly appending bucket suffixes and applying quantile functions.
- Provides a structured process for resolving project IDs and resource labels.
When to use it
- When you need to write a PromQL query for a specific Google Cloud Monitoring metric.
- When you need to aggregate Cloud Monitoring data (e.g., sum, average, or percentiles) using PromQL.
- When you are unsure of the correct PromQL syntax for a specific Cloud Monitoring metric type.
- When you need to ensure your PromQL query is syntactically valid before deployment.
Advantages
- Ensures syntactic correctness through automated linter validation.
- Standardizes the translation of Cloud Monitoring metric paths to PromQL names.
- Prevents common errors like incorrect aggregation grouping or missing resource filters.
- Provides clear guidance on handling complex metric types like distributions.
Usage examples
- “Generate a PromQL query for compute.googleapis.com/instance/cpu/utilization for my project.”
- The agent resolves the project ID, identifies the metric as a GAUGE, and returns a formatted PromQL query like 'compute_googleapis_com:instance_cpu_utilization' wrapped in a code block.
- “Write a PromQL query to get the 95th percentile of execution times for my Cloud Function.”
- The agent identifies the metric as a DISTRIBUTION type, applies the necessary rate and histogram_quantile functions, and returns the validated PromQL query.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/cloud-monitoring-promql-query/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip cloud-monitoring-promql-query.zip -d .agents/skills
Documentation by Google
Cloud Monitoring PromQL Generator
Use this skill to generate a valid PromQL query from any Cloud Monitoring metric type. This guide applies to all Cloud Monitoring metric types by mapping Cloud Monitoring metric and resource descriptors to PromQL structures.
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:cloud-monitoring-promql-query)"
and append agent-skills/1.0 (skill:cloud-monitoring-promql-query) to User-Agent headers.
Workflow
Resolve Project ID (CRITICAL & BLOCKING)
Before performing any other actions (such as searching code, reading references, or running validation), you MUST verify whether the Google Cloud Project ID is available:
Check Prompt/Payload: Look for the Project ID in the user's prompt or input.
Check Environment: If the Project ID is not present in the prompt, you MUST run
gcloud config get-value projectto attempt to resolve it from the environment.Ask for Clarification (BLOCKING): If the Project ID is not in the prompt AND the
gcloudcommand fails, returns an empty string, or is unavailable, you MUST immediately stop. Do NOT generate a PromQL query, do not run the validation script, and do not use placeholders (likeYOUR_PROJECT_ID). You must refuse to proceed and ask the user to provide the Project ID.
Inspect Metric and Resource Descriptors
Use Provided Descriptors First: If the user's prompt already includes metric descriptor details (such as
metric.type,metricKind,valueType, ormonitoredResourceTypes) or specific resource filter values, use those values directly instead of calling the Cloud Monitoring API.Discover Missing Descriptors: If exact metric descriptors (
metric.type,metricKind,valueType) are missing or underspecified, resolve the target metric type's descriptor using one of these paths:Vague Query: If the prompt is vague (for example,
"VM CPU usage"), use thecloud-monitoring-metric-selectionskill first to identify the specific metric type.Known Metric Type: If you already have the specific metric type name (for example,
compute.googleapis.com/instance/cpu/utilization) but need its descriptor, call thegoogle-cloud-monitoring:list_metric_descriptorsMCP tool. If the tool is missing, refer to thecloud-monitoring-metric-selectionskill to configure the Cloud Monitoring MCP server.Fallback: If the MCP tool cannot be configured, fall back to making a direct Cloud Monitoring API call.
Identify Key Fields: From the retrieved descriptor, identify four key schema attributes:
type: The Cloud Monitoring metric type string.metricKind:GAUGE,DELTA, orCUMULATIVE.valueType:INT64,DOUBLE,DISTRIBUTION, orBOOL.monitoredResourceTypes: Compatibleresource.typestrings required for resource scoping and grouping.
Resolve Resource Filters & Discovery Protocol
To filter data by a specific resource instance, apply these resource rules and discovery protocols:
Monitored Resource Filter: Always include the
monitored_resource="<type>"filter in your query to prevent collisions across services that share metric names.Example:
monitored_resource="gae_app"
Preserve User Literals (CRITICAL): ALWAYS use the literal resource names, namespaces, and IDs provided in the user's prompt. Do NOT override or replace these values with active resource names found during Cloud Monitoring discovery unless the user explicitly asked you to find active resources. Telemetry discovery must only be used to identify metric type names and label keys, not to override user input.
Resource Identifier Mapping:
Direct & Specific Keys: Use the most specific resource identifier available. Example:
version_id,cluster_name.Name-to-ID Resolution: If the user filters by a resource name (such as
"instance-1"), but the resource schema uses numeric IDs (likeinstance_id), use PromQL string name labels instead of numeric ID labels. Example:instance_name,metadata_system_name.Composite Identifiers: For resources with hierarchical identifiers (such as Cloud SQL databases), format the filter as a single composite key. Do NOT split them into separate
project_idand sub-resource labels. Example:database_id="{project_id}:{instance_name}".
Resource Label Discovery: The
google-cloud-monitoring:list_metric_descriptorstool only returns metric-specific labels. If the label schema for a monitored resource is unknown, fetch the resource descriptor directly from the Cloud Monitoring v3 REST API (projects.monitoredResourceDescriptors.get):TOKEN=$(gcloud auth application-default print-access-token 2>/dev/null || gcloud auth print-access-token) curl -s -H "Authorization: Bearer ${TOKEN}" \ "https://monitoring.googleapis.com/v3/projects/{project_id}/monitoredResourceDescriptors/{monitored_resource_type}"An HTTP 200 OK response returns the
MonitoredResourceDescriptorobject containing thelabelsarray with the exact resource label keys for that resource.
Choose Aggregation Structure & Defaults
The query structure and aggregation functions (such as rate,
histogram_quantile, sum, or avg) depend on the metric type and how it is
visualized.
Consult the Reference: Consult the Cloud Monitoring to PromQL Basic Aggregations Reference as the single source of truth to map Cloud Monitoring properties (Metric Kind, Value Type, Aligner, Reducer) to their PromQL structures.
SRE Aggregation & Visualization Rules:
Do NOT sum or average ratio/percentage utilization metrics (like CPU % or Memory limit utilization) across resource instances. Instead, keep them unaggregated (raw metric), group by instance, or wrap in
topk(30, avg_over_time(...)).State Label Filtering (CRITICAL): Only the metrics
agent.googleapis.com/memory/percent_usedandagent.googleapis.com/disk/percent_usedrequire{state!="free"}. Do NOT filter by{state="used"}.
Format & Validate Query
Before presenting any PromQL queries, validate them using the linter:
Python Dependencies
Before executing the validation script (scripts/validate_promql.py), install
the required Python dependencies:
python3 -c "import promql_parser" || pip install promql-parserValidation Procedure
Format Constraints:
Metric Name Normalization: Convert Cloud Monitoring metric types to PromQL metric names using this recipe:
Split Domain and Path: Split the Cloud Monitoring metric type by the first slash (
/) to separate the domain from the path.Example:
storage.googleapis.com/network/received_bytes_count-> domainstorage.googleapis.com, pathnetwork/received_bytes_count
Normalize Domain: Replace all periods (
.) in the domain with underscores (_).Example:
storage.googleapis.com->storage_googleapis_com
Normalize Path: Replace all periods (
.) and slashes (/) in the path with underscores (_).Example:
network/received_bytes_count->network_received_bytes_count
Join with Colon: Join the normalized domain and normalized path with a colon (
:).Example:
storage_googleapis_com:network_received_bytes_count
Native Prometheus Metrics: If the metric type has no slash, keep it as-is.
Example:
up->up,http_requests_total->http_requests_total
Distribution Suffix: If the metric's
valueTypeisDISTRIBUTION, append_bucketto the end of the normalized name.Example:
cloudfunctions.googleapis.com/function/execution_times->cloudfunctions_googleapis_com:function_execution_times_bucket
Ensure the final query is a single line with no comments (no
#or//). Cloud Monitoring query translation collapses whitespace and can cause code trailing a comment to be ignored or throw parsing errors.Grouping Clause Syntax: Ensure grouping clauses (such as
by (label)) only follow aggregation operators (such assum,avg,min,max, orcount). Never place a grouping clause directly after a metric selector.Incorrect:
metric{...} by (label)Correct:
sum(rate(metric{...}[5m])) by (label)
Fenced Output Code Block: ALWAYS wrap the final verified PromQL query in a fenced
promqlcode block in your final response.
Linter Verification:
Validate all generated queries in a single batch:
python3 <path_to_skill>/scripts/validate_promql.py --query '<q1>' '<q2>'If validation fails, read PromQL Error Recovery Guide to diagnose and fix common type mismatches and syntax errors before repeating the loop.
References
Files
- ATTRIBUTION.mdAdded by AutoGineer876 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md10.6 KiB
- references/attribution.md1.9 KiB
- references/basic_aggregations.md12.8 KiB
- references/promql_error_recovery.md5.1 KiB
- scripts/validate_promql.pyScript7.5 KiB
- scripts/validate_promql_test.pyScript9.6 KiB
LICENSE
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs Python
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Attribution and licence
- Published by
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- Google (Official)
- Source
- google/skills · skills/cloud/cloud-monitoring-promql-query · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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