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 agentmods add skills/googlecloudplatform/professional-services/agent-evalnpx skills add GoogleCloudPlatform/professional-services --skill agent-evalgit clone --depth 1 https://github.com/GoogleCloudPlatform/professional-servicesWhat 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 | $0.00136 | $0.01505 |
| Opus 5 | $0.00068 | $0.00753 |
| Sonnet 5 | $0.00027 | $0.00301 |
| Haiku 4.5 | $0.00014 | $0.00151 |
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 2d 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 — 109 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.
- 2d 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 (3,065 stars, last pushed 11d ago), licensed Apache-2.0. It adds 136 tokens to every session and 1,505 once invoked, about $0.0007 per session on Opus 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
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
stale-sweep
Sweep the googleapis/mcp-toolbox repo for issues and PRs with no real activity in N days (default 60), sort each by whose silence it is (the author's, ours, or nobody's), and draft the nudge or close comment. Use whenever a maintainer asks for a stale sweep, backlog cleanup, or an SLO check, e.g. "stale sweep", "find…
triage-issues
Triage GitHub issues in the googleapis/mcp-toolbox repo: propose the correct labels (type / priority / product / status), check for duplicates, verify a bug has enough info to act on, and draft a triage comment. Use whenever a maintainer asks you to triage, label, categorize, prioritize, or "look at" an issue (or a…
data-parity
Validate that two tables or query results are identical — or diagnose exactly how they differ. Discover schema, identify keys, profile cheaply, then diff. Use for migration validation, ETL regression, and query refactor verification.
dbt-develop
REQUIRED before writing or modifying ANY dbt model. Invoke this skill FIRST whenever a task says "create", "build", "add", "modify", "update", "fix", or "refactor" a dbt model, staging file, mart, incremental, or snapshot. Skipping this skill is the leading cause of silent-correctness bugs — models that compile and…