google/skills is a collection of Agent Skills that guide AI agents in using Google products and technologies, including Google Cloud services. Developers and agents use these reusable workflows for tasks such as cloud authentication, data analysis, application deployment, and AI platform management. The catalogue entries are skills, plugins, and an MCP integration from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add google/skills --skill bigquery-observabilitygit clone --depth 1 https://github.com/google/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google/skills/bigquery-observability)<a href="https://agentmods.dev/skills/google/skills/bigquery-observability"><img src="https://agentmods.dev/badge/skills/google/skills/bigquery-observability/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/google/skills/bigquery-observability"><img src="https://agentmods.dev/badge/skills/google/skills/bigquery-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.03439 |
| Opus 5.5 | $0.00058 | $0.01376 |
| Sonnet 5.5 | $0.00029 | $0.00688 |
| Haiku 4.5 | $0.00014 | $0.00344 |
Grade A, and why
bigquery-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigQuery Observability
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:bigquery-observability)"
and append agent-skills/1.0 (skill:bigquery-observability) to User-Agent headers.
Tool Selection
| Tool | Primary Use Cases | Strengths & Capabilities | When to Avoid / Limitations |
|---|---|---|---|
INFORMATION_SCHEMA (I_S) |
Historical analysis, cohort comparison (normalized_literals), discovery of fast/slow windows, reservation/project timelines, multi-job aggregates, cost/billing tracing. |
Flexible SQL querying across JOBS, JOBS_TIMELINE, and RESERVATIONS; supports custom time windows and grouping. |
Avoid for high-frequency real-time polling or single-job point-lookups (can consume slots and take seconds to execute). |
REST API (jobs.api / reservation.api) |
Single-job point-lookup, real-time stage bottleneck diagnosis, automated pipeline status checks, reservation/capacity commitment configuration inspection (reservations.get, reservations.list). |
Zero-SQL overhead, fast REST/CLI point-lookups (bq show -j, bq show --reservation), instant access to performanceInsights, queryPlan, and structural metadata. |
Avoid for aggregate analysis across thousands of jobs, cross-project historical comparison, or system timeline aggregations. |
Cloud Monitoring (Metrics Explorer / Charts) |
Real-time alerting, fleet-wide dashboards, continuous slot utilization tracking, high-level SLA/SLO monitoring. | Out-of-the-box charts for slot utilization, query throughput, PENDING queue depth, and execution latency; low-latency alerting without running queries. |
Avoid for SQL-level debugging, individual query text inspection, or stage-level execution detail. |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/attribution.md 1.9 KB
- references/capacity_and_configuration_queries.md 6.6 KB
- references/compute_capacity_billable.md 6.2 KB
- references/compute_ondemand_billable.md 5.7 KB
- references/job_performance_queries.md 11 KB
- references/resource_contention_queries.md 9.3 KB
- references/schema_compute.md 40 KB
- references/schema_others.md 32 KB
- references/schema_storage.md 34 KB
- references/storage_footprints.md 10 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +9 lines 2d12d772e8da
- 15d ago Changed · +1 lines e5652c3d5343
- 25d ago Changed · +9 lines 48378f0ba829
- 1mo ago First seen · 254 lines · 144 tokens per session scan A 96188eeb81bc
bigquery-observability is a skill published in the GitHub repository google/skills (21,557 stars, last pushed yesterday), licensed Apache-2.0. It adds 144 tokens to every session and 3,439 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-18.
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