Google Cloud Professional Services is a repository of example solutions and tools for building systems with Google Cloud products. Teams can use them as references or adapt them for workloads involving services such as GKE, BigQuery, Dataflow, and machine learning APIs. The catalogue entries are skills related to this repository.
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 GoogleCloudPlatform/professional-services --skill agent-eval-workflowgit clone --depth 1 https://github.com/GoogleCloudPlatform/professional-servicesWrote 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/googlecloudplatform/professional-services/agent-eval-workflow)<a href="https://agentmods.dev/skills/googlecloudplatform/professional-services/agent-eval-workflow"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/professional-services/agent-eval-workflow/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/googlecloudplatform/professional-services/agent-eval-workflow"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/professional-services/agent-eval-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00123 | $0.02732 |
| Opus 5.5 | $0.00049 | $0.01093 |
| Sonnet 5.5 | $0.00025 | $0.00546 |
| Haiku 4.5 | $0.00012 | $0.00273 |
Grade A, and why
agent-eval-workflow 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 1mo 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 — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent evaluation: the process
Commands change. The process does not. This skill is about how to evaluate an
agent well — the reasoning that stays true whether you drive it with
agent-eval, agents-cli eval, or whatever replaces them.
The loop: Hypothesize. Test. Validate.
State a falsifiable claim about the agent, write metrics that could prove you wrong, run them, and let the results — not your intuition — decide what to fix.
1. Start from a hypothesis, not from "add some metrics"
A metric exists to settle an argument. Before generating anything, answer:
- What do I believe this agent gets wrong?
- What observable evidence would prove that?
- What would prove me wrong?
Read the agent's own instructions and tool code first. Stated rules are testable claims — "always confirm before deleting", "never approve above 10%", "always cite a source". Those sentences convert directly into criteria.
When a generator asks what to focus on, give it the hypothesis in plain language naming real tools and thresholds. Vague guidance produces vague rubrics.
Good: "The agent must never call
approve_discountabove 10%; it should route larger requests tosync_ask_for_approvalinstead of being rejected and retrying."Weak: "Test discount handling."
Prefer binary rubrics (0/1). LLM judges have poor inter-rater reliability on 1–5 scales. Several sharp binary metrics beat one fuzzy graded one, and the pass rate across a dataset gives you a continuous score with better statistics.
2. Make the agent measurable before measuring harder
A judge reading prose is fuzzy. A state variable written by the tool itself is deterministic. If a tool already makes a decision, record it:
def approve_discount(discount_type: str, value: float, reason: str,
tool_context: ToolContext) -> dict:
if value > MAX_DISCOUNT_RATE:
tool_context.state["discount_status"] = "rejected" # hard evidence
return {"status": "rejected", "message": "discount too large."}
tool_context.state["discount_status"] = "approved"
return {"status": "ok"}
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.
- 1mo ago First seen · 266 lines · 123 tokens per session scan A 1dd4c8053fdd
agent-eval-workflow is a skill published in the GitHub repository GoogleCloudPlatform/professional-services (3,082 stars, last pushed yesterday), licensed Apache-2.0. It adds 123 tokens to every session and 2,732 once invoked, about $0.0005 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 2026-08-30.
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