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Cloud Trace Querying

OrganizationPopular
google
cloud-trace-querying

Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services. Use when investigating slow requests, analyzing trace hierarchies, or resolving latency regressions. Do NOT use for querying non-GCP telemetry or database query optimization outside Cloud Trace.

Overview

Publishergoogle
Repositoryskills
Skill namecloud-trace-querying
Stars
21.2K
Forks
1.8K
Bundled files
88
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 88 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Cloud Trace Querying AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cloud/cloud-trace-querying .claude/skills/cloud-trace-querying
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloud Trace Querying in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cloud Trace Querying on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cloud Trace Querying is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cloud Trace Querying Skill (cloud-trace-querying)

This skill equips the agent to interact with Cloud Trace by providing standard query patterns, diagnostic workflows for latency and errors, and tools to fetch and inspect trace hierarchies.

[!IMPORTANT] AUTONOMOUS TOOL EXECUTION MANDATE: You MUST execute the tool scripts directly in bash to query telemetry. Do NOT just describe query strings or write Python scripts. Run the provided bash tools directly and report the exact results.

Prerequisites & Setup

  1. Google Cloud SDK: Verify gcloud is installed (Installation Guide).
  2. Authentication: Authenticate CLI and application default credentials: 
    bash
    gcloud auth login && gcloud auth application-default login
  3. Project & Billing: Identify the target <PROJECT_ID> (from the user's prompt or via bash scripts/get_trace_project/run.sh), ensure an active billing account is attached, and configure the project if not already set: 
    bash
    gcloud config set project <PROJECT_ID>
  4. API Enablement: Verify Cloud Trace and Cloud Logging APIs are enabled: 
    bash
    gcloud services enable cloudtrace.googleapis.com logging.googleapis.com

Quickstart Workflows

1. Identify Google Cloud Project ID

If the user specified a Google Cloud project ID in their prompt, use it.

If they did not, ALWAYS determine the Google Cloud project ID by running:

bash
bash scripts/get_trace_project/run.sh

2. Search Traces (Latency & Errors)

To find traces matching specific criteria, such as a root span of /v1/booking/book with a duration of at least 1.5s:

bash
bash scripts/search_traces/run.sh --filter 'root:/v1/booking/book latency:1.5s' --projects '<PROJECT_ID>'

Note: --filter is required. To list traces without specific filtering, pass --filter '' or --filter 'root:'. Filter syntax: The Cloud Trace API uses latency:<duration> (such as latency:1.5s for duration >= 1.5s), which corresponds to "Span duration" in Trace Explorer. The script outputs JSON where each cluster's examples array contains sample trace objects with trace_id, duration_seconds, span count, and error status.

3. Fetch Entire Trace Hierarchy

To view all spans and status codes/labels within a trace:

bash
bash scripts/fetch_entire_trace/run.sh --trace-id '<TRACE_ID>' --project '<PROJECT_ID>'

4. Fetch Correlated Application Logs

To retrieve application log entries correlated with a trace ID:

bash
bash scripts/fetch_related_logs/run.sh --trace-id '<TRACE_ID>' --trace-projects '<PROJECT_ID>'

Application log entries are useful when inspecting stack traces, exception messages, and log events emitted during span execution. When diagnosing errors or service crashes, inspect the returned log entries for severity: "ERROR" to confirm root causes or uncover unhandled exceptions. Note: fetch_related_logs queries the default log scope (_Default), searching across configured log views in the target project. An empty response can occur if: (1) no log entries match the criteria, (2) the caller lacks IAM permissions to view the matching entries, or (3) matching entries reside in log views outside the searched log scope.

5. Fetch Specific Trace Span

To inspect attributes and metadata of a single span within a trace:

bash
bash scripts/fetch_trace_span/run.sh --trace-id '<TRACE_ID>' --span-id '<SPAN_ID>' --project '<PROJECT_ID>'

6. Generate Cloud Console Deep Links

To generate direct Cloud Console URLs for browser inspection of traces or correlated logs:

bash
bash scripts/generate_links/run.sh --trace-id '<TRACE_ID>' --project '<PROJECT_ID>' [--span-id '<SPAN_ID>']

7. Format Output

  • Terminal / CLI: Render span trees as indented ASCII timelines using |--.
  • UI Presentation: Render as A2UI JSON graph structures when requested for UI.

8. Upstream Service Error Handling

If the Cloud Trace API or Cloud Logging API returns an upstream HTTP service error (such as 500 Internal Server Error, 503 Service Unavailable, or GoogleAPICallError), report the upstream service outage directly to the user. Do not attempt to inspect virtual environments, modify internal scripts, or debug local libraries.

Reference Documentation

Operational Workflows

Query Syntax & Tools

Semantic Conventions

Concepts & Architecture

Templates & Assets

Official Documentation Links

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 28 more files.

Frequently asked questions

What does the Cloud Trace Querying AI skill do?

Query Cloud Trace spans, filter by latency thresholds or error status, correlate distributed traces with Cloud Logging, and diagnose latency bottlenecks across Google Cloud services. Use when investigating slow requests, analyzing trace hierarchies, or resolving latency regressions. Do NOT use for querying non-GCP telemetry or database query optimization outside Cloud Trace.

Why use Cloud Trace Querying on TypingMind?

Because you install it once and use it with any model. Cloud Trace Querying is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cloud Trace Querying in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cloud Trace Querying?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cloud Trace Querying?

As many as you like. As long as a model supports skills, you can use Cloud Trace Querying with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cloud Trace Querying AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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