Analyze Usage
openshift-eng/ai-helpers
A skill your agent uses when analyzing BigQuery usage patterns, costs, and query performance for a GCP project
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.
Install Cloud Trace Querying
$ npx skills add google/skills --skill cloud-trace-querying -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills cloud-trace-querying --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .claude/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .claude/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-queryingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill cloud-trace-querying -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills cloud-trace-querying --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .agents/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .agents/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill cloud-trace-querying -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills cloud-trace-querying --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .cursor/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .cursor/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/cloud-trace-querying--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill cloud-trace-querying -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills cloud-trace-querying --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .gemini/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .gemini/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills cloud-trace-queryingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill cloud-trace-querying -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .github/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .github/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill cloud-trace-querying -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills cloud-trace-querying --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/cloud-trace-querying .opencode/skills/cloud-trace-querying && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cloud-trace-querying" agent skill from https://github.com/google/skills/tree/main/skills/cloud/cloud-trace-querying into .opencode/skills/cloud-trace-querying/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cloud-trace-querying", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cloud-trace-querying- GitHub stars
- 21k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 594 words
- Files
- 118 (incl. scripts, references, assets)
- Skills in repo
- 147
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
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.
- Works in 8 steps: Identify Google Cloud Project ID → Search Traces (Latency & Errors) → Fetch Entire Trace Hierarchy → …
- Investigating slow requests
- SKILL.md covers Prerequisites & Setup, Quickstart Workflows, Reference Documentation and Official Documentation Links
- Calls bash and gcloud
What it does
Cloud Trace Querying is an agent skill from google/skills, published by the product's own GitHub organization. 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.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 129 other files, including scripts, reference files and assets (for example `assets/formatting/a2ui/single_span/display.a2ui.json`, `assets/formatting/a2ui/trace_chart/display.a2ui.json` and `assets/formatting/a2ui/trace_flow/display.a2ui.json`).
It sits in Databases, covering Query optimization. It works with Google Cloud. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Investigating slow requests
- Analyzing trace hierarchies
- Resolving latency regressions
- Querying non-GCP telemetry
Example prompts
- “/cloud-trace-querying”
Requirements
- Python 3
Workflow steps
8 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7d97937. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
bashgcloud
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
docs.cloud.google.comopentelemetry.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cloud Trace Querying loads about 1.7k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 594 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from google/skills at commit 7d97937, republished under its Apache-2.0 licence (© google). 594 words, ~1,721 tokens.
Download SKILL.mdSave it as
.claude/skills/cloud-trace-querying/SKILL.md (or your agent's skills folder). This skill also uses 117 other files; get the full folder from GitHub.- name
- cloud-trace-querying
- description
- 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.
- metadata.version
- 1.0.0
- metadata.category
- CloudObservabilityAndMonitoring
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
- Google Cloud SDK: Verify
gcloudis installed (Installation Guide). - Authentication: Authenticate CLI and application default credentials:
bash gcloud auth login && gcloud auth application-default login - Project & Billing: Identify the target
<PROJECT_ID>(from the user's prompt or viabash 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> - 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 scripts/get_trace_project/run.sh2. 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 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 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 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.
Show full SKILL.md (218 more words)Show less
5. Fetch Specific Trace Span
To inspect attributes and metadata of a single span within a trace:
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 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
- Master Index: Reference Documentation Index
Operational Workflows
- Troubleshoot Latency Workflow
- Correlated Logs Workflow
- Trace Querying Decision Tree
- Console Deep Links
- Workflows Overview
Query Syntax & Tools
- Filter Syntax (
root:,latency:,+span:,error:true) - API Limits and Constraints
- Observability Analytics (BigQuery SQL)
- Cloud Trace MCP Tools
Semantic Conventions
- Conventions Overview
- OpenTelemetry Core
- OpenTelemetry HTTP
- OpenTelemetry gRPC & RPC
- OpenTelemetry GenAI
- GCP App Hub & Service Conventions
- GCP Legacy Labels
Concepts & Architecture
- Observability Scopes
- Distributed Traces Concept
- Spans Concept
- Logs-to-Trace Correlation
- Knowledge Overview
Templates & Assets
Official Documentation Links
© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 117 other files (scripts, references, assets) in skills/cloud/cloud-trace-querying of google/skills.
- SKILL.md
- assets/formatting/a2ui/single_span/display.a2ui.json
- assets/formatting/a2ui/trace_chart/display.a2ui.json
- assets/formatting/a2ui/trace_flow/display.a2ui.json
- assets/formatting/a2ui/traces_heat_map/display.a2ui.json
- assets/formatting/ascii/single_span/display.txt
- assets/formatting/ascii/trace_chart/display.txt
- assets/formatting/ascii/trace_flow/display.txt
- assets/formatting/ascii/traces_heat_map/display.txt
- … and 109 more
Open the folder on GitHubat commit 7d97937
Compare with similar skills
Cloud Trace Querying next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cloud Trace Querying this skillgoogle/skills | 21k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Analyze Usageopenshift-eng/ai-helpers | 120 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| SQL Optimization Patternsynulihao/AgentSkillOS | 617 | 11 repos | ~3.3k | Automated safety check: Pass | None | |
| Query Engine Designrevfactory/claude-code-harness | 120 | — | ~474 | Automated safety check: Pass | None | |
| Query Plan Snapshot CLIeclipse-rdf4j/rdf4j | 420 | — | ~1.5k | Automated safety check: Pass | BSD-3-Clause | |
| Wp Acf And Content Modelingjorgerosal/wordpress-skills | 101 | — | ~3.2k | Automated safety check: Pass | MIT |
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Related
Works with
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Questions about Cloud Trace Querying
What does Cloud Trace Querying 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. Cloud Trace Querying is an agent skill from google/skills, published by the product's own GitHub organization. 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.
When should I use Cloud Trace Querying?
Cloud Trace Querying fits situations like: investigating slow requests; analyzing trace hierarchies; resolving latency regressions; querying non-GCP telemetry.
How do I install Cloud Trace Querying in Claude Code?
Run `npx skills add google/skills --skill cloud-trace-querying -a claude-code`. Or copy the skill folder (skills/cloud/cloud-trace-querying in google/skills) into .claude/skills/cloud-trace-querying in your project. Claude Code loads it when a task matches its description.
How do I install Cloud Trace Querying in Codex?
Run `npx skills add google/skills --skill cloud-trace-querying -a codex`. Or copy the skill folder (skills/cloud/cloud-trace-querying in google/skills) into .agents/skills/cloud-trace-querying in your project. Codex loads it when a task matches its description.
Can I use Cloud Trace Querying in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill cloud-trace-querying -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cloud-trace-querying, .gemini/skills/cloud-trace-querying, .github/skills/cloud-trace-querying and .opencode/skills/cloud-trace-querying in your project.
What does Cloud Trace Querying need to run?
Going by SKILL.md and its folder, Cloud Trace Querying needs the command-line tools its instructions call (bash and gcloud). Our summary lists: Python 3.
Does Cloud Trace Querying access the network?
SKILL.md names 2 domains. As links in the text: docs.cloud.google.com and opentelemetry.io. This is read from the text; nothing was executed.
Is Cloud Trace Querying safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Cloud Trace Querying use?
Cloud Trace Querying is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cloud Trace Querying use?
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.
What are the alternatives to Cloud Trace Querying?
Skills that share tags, products or a category with Cloud Trace Querying: Analyze Usage (openshift-eng/ai-helpers, 120 stars), SQL Optimization Patterns (ynulihao/AgentSkillOS, 617 stars), Query Engine Design (revfactory/claude-code-harness, 120 stars) and Query Plan Snapshot CLI (eclipse-rdf4j/rdf4j, 420 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cloud Trace Querying?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.