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Cloud Logging Query Generation

Free

Transform natural language into LQL queries for Google Cloud.

by google17.6k stars on google/skills
Updated Aug 10, 2026
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Free · Opens the source repo

What Cloud Logging Query Generation does

The Cloud Logging Query Generation skill simplifies the process of creating Logging Query Language (LQL) queries for Google Cloud Logging by converting natural language input into precise queries. This tool is particularly useful for developers and engineers who need to extract specific log data or troubleshoot issues within Google Cloud services. By using this skill, users can quickly generate queries that adhere to the strict syntax and formatting requirements of LQL, ensuring accuracy and efficiency in log data retrieval.

This skill emphasizes the importance of understanding resource types and syntax rules, helping users avoid common pitfalls such as confusing instance IDs with instance names or misidentifying resource types. The output is designed to be straightforward, providing only the raw LQL query text without additional conversational context. This allows for quick integration into existing workflows, minimizing the time spent on query formulation.

Users can benefit from the detailed service reference files included in the skill, which guide them through the specific requirements for various Google Cloud services. This ensures that the generated queries are not only syntactically correct but also contextually relevant to the specific service being queried. The skill is particularly beneficial for those who frequently interact with Google Cloud's logging infrastructure and need to generate complex queries on-the-fly.

Overall, the Cloud Logging Query Generation skill is an essential tool for anyone working with Google Cloud Logging, providing a streamlined approach to log query generation that enhances productivity and reduces errors.

When to use it

Use this skill when you need to generate LQL queries from natural language for debugging or querying log data in Google Cloud.

When not to use it

This skill is not suitable for querying non-Google Cloud databases or services, such as SQL or Cloud Spanner.

What you can build with it

Debugging Cloud Functions

Generate precise LQL queries to troubleshoot issues in Cloud Functions by specifying the relevant resource types and filters.

Querying Audit Logs

Quickly create queries to access audit logs, ensuring you follow the correct protoPayload schema paths for accurate results.

Monitoring Compute Engine Logs

Easily formulate queries to monitor logs from Compute Engine instances, using the correct instance name and resource type.

How to install Cloud Logging Query Generation

View source

1. Install with the skills CLI

npx skills add google/skills/cloud-logging-query-generation --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by google

Generate Logging Query Language queries

Use this skill to generate correct Logging Query Language (LQL) queries for Cloud Logging.

Core rules

  1. Strict syntax requirements:

    • Always use double quotes (") for string literals. Do not use single quotes (').
    • Write boolean operators in all capitals: AND, OR, NOT.
    • Always use parentheses to group terms and explicitly enforce precedence.
  2. Common pitfalls:

    • Instance ID vs. Instance Name: For the gce_instance resource type, do NOT compare instance names to instance IDs. Instance names are strings (for example, my-instance). Instance IDs are numeric. If you only have the name, then search by instance name, SEARCH("my-instance"), or use resource.labels.instance_name if that label is available for the resource.
    • Resource Type Accuracy: Do not guess resource types. You must look up the correct resource.type value in the service-specific reference files. For example, use internal_http_lb_rule for Internal HTTP(S) Load Balancer rules when filtering by forwarding rule name or region (instead of http_load_balancer).
  3. Output format and placeholders:

    • Output only the raw LQL query text. Do not include conversational filler. Do not wrap the query in markdown code blocks unless explicitly requested by the user. Valid LQL comments (using --) are allowed, and are the ONLY acceptable way to include explanations or warnings.
    • Never block on missing variables. If the user's request lacks specific identifiers (like a project ID, instance name, or IP address), do not ask them for clarification. If the variable is required for a functional query (like a log bucket name for a regional log), insert an uppercase placeholder string wrapped in angle brackets (for example, "<PROJECT_ID>"). CRITICALLY: If you include a placeholder for a variable the user omitted, it will act as an explicit filter that causes logs to be missed. Therefore, you MUST omit the entire filter/line containing the placeholder if the field is not strictly required. For example, completely omit resource.labels.instance_id="..." if the user didn't specify an instance, but you MUST include logName=".../projects/<PROJECT_ID>/..." with a placeholder if constructing a regional log bucket query where a project ID is strictly required.
  4. Preferred fields:

    • Include resource.type and log_id restrictions when the query targets specific Google Cloud services or resources. Global queries (for example, "latest error logs") do not require these restrictions.

Detailed reference

Refer to references/api_reference.md for LQL syntax rules, including Operators, NULL handling, SEARCH, and Regex.

Service reference files

Before generating a query, you MUST read the examples for the specific service. LQL schemas and resource.type values are service-specific. Do not stop reading after finding the Base Schema in the file. You must verify if there are specific requirements for state tracking (like previousState) or resource-specific log IDs detailed in the paragraphs or specific query examples below the schema block.

For the following services, read the exact file listed:

For Google Cloud services that aren't listed: If the service is not listed above, write the LQL query based on your general knowledge.

Query generation rules

  1. Resource Types: Explicitly define the resource.type in your queries when focusing on specific services. For some queries, you may need to search across multiple types (for example, resource.type=("bigquery_project" OR "bigquery_dataset")).
  2. Audit and Admin Logs: If the user asks for audit logs, admin logs, API logs, or logs about who created, updated, deleted, read, or accessed a resource:
    • You MUST read references/query_audit_logs.md for the correct protoPayload schema paths and common examples.
    • If a specific example is not listed, guess the protoPayload.methodName by combining the service and verb. When guessing, you MUST use the scoped SEARCH() function (e.g., SEARCH(protoPayload.methodName, "compute.instances.insert")) instead of the exact match operator (=) to avoid version prefix mismatches. Do NOT use the colon operator (:) as it may cause substring false positives.
    • For generic API enable/disable events (e.g., a service was disabled), always use resource.type="audited_resource".
  3. Handling Unknown Schemas (Crucial): If the user asks to filter by a specific field or condition, and if you cannot find a matching example or schema in the reference files, then you must generate a query using global search.
    • Only specify jsonPayload.* or protoPayload.* field structures when you are certain of their exact name.
    • Use the SEARCH() function to find the keyword globally within the correct resource.type.
    • Mandatory LQL Comment: When delivering a query that uses SEARCH, you MUST add an LQL comment (using --) at the top of the query indicating you used a global keyword search because the exact schema wasn't in your references. Do NOT output conversational text, strictly adhere to the Output Format rule.

Supporting links

Frequently asked questions about Cloud Logging Query Generation

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