cloud-logging-query-generation

cloud-logging-query-generation

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Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

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Updated 8/21/2026
SKILL.md
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name
cloud-logging-query-generation
description

Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

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