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-evalgit 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)<a href="https://agentmods.dev/skills/googlecloudplatform/professional-services/agent-eval"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/professional-services/agent-eval/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"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/professional-services/agent-eval.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 | $0.00136 | $0.01593 |
| Opus 5.5 | $0.00054 | $0.00637 |
| Sonnet 5.5 | $0.00027 | $0.00319 |
| Haiku 4.5 | $0.00014 | $0.00159 |
Grade A, and why
agent-eval 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation & Continuous Optimization Framework
This skill defines the end-to-end evaluation, benchmarking, automated optimization, and publication workflow using the agent-eval CLI Pipeline aligned with google/agents-cli and the ADK Quality Flywheel (adk.dev/optimize).
1. Reference Architecture & Deep Guides
| Reference Guide | Contents |
|---|---|
references/dataset_schema.md |
Canonical dataset.jsonl schema (single-turn, multi-turn, multi-agent topologies). |
references/metrics_guide.md |
Declarative eval_config.yaml specification across 6 metric kinds. |
references/gepa_optimization.md |
Automated genetic prompt evolution via GEPARootAgentPromptOptimizer. |
references/failure_triage.md |
2-Tier loss clustering and Context Engineering remediation strategies. |
2. The Standard agent-eval run Benchmark Command
Always execute benchmark sweeps using the standardized --feature, --tag, and --publish taxonomy against an active API server endpoint (--base-url):
export AGENT_EVAL_NO_PAUSES=1
export GOOGLE_GENAI_USE_VERTEXAI=1
export GOOGLE_CLOUD_PROJECT=<PROJECT_ID>
agent-eval run \
--agent-dir app \
--base-url http://localhost:8080 \
--feature "<feature_or_branch_name>" \
--tag "<short_iteration_tag>" \
--description "<one-line summary of changes tested>" \
--sim-parallelism 6 \
--publish \
--compare-to "<baseline_run_id_or_path>"
Key CLI Flags & Defaults
--feature: Git feature branch or capability under test (defaults to active git branch).--tag: Concise iteration slug (e.g.direct-bypass-v1,calibrated-prompt-v2).--description: Human-readable context saved intoeval_summary.jsonand rendered in the dashboard.--sim-parallelism 6: Runs 6 scenarios in parallel, cutting multi-turn sweeps down to ~2.5 minutes.--publish: Automatically syncs the entire output run directory to Google Cloud Storage (gs://<PROJECT_ID>-eval-artifacts/runs/<run_id>/).--compare-to: Resolves a baseline run (locally or directly from GCS) and generates delta percentage scorecards.
What ships with it
5 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.
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 Changed · +7 lines 9561023061df
- 1mo ago First seen · 109 lines · 136 tokens per session scan A f7c90a219c3e
agent-eval is a skill published in the GitHub repository GoogleCloudPlatform/professional-services (3,082 stars, last pushed yesterday), licensed Apache-2.0. It adds 136 tokens to every session and 1,593 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.
Other skills, from other repositories
quality-flywheel
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testing-assistant
Manages testing lifecycle including unit tests, integration tests, validation, and quality assurance.
testing-dbt-projects
Add data quality tests to a dbt project — generic tests (unique, notnull, acceptedvalues, relationships), singular tests, unit tests, dbt-utils and dbt-expectations packages, and source freshness. Use when adding tests to dbt models, catching data quality regressions, validating assumptions, or setting up source…
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".