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/google/agents-cli/google-agents-cli-evalnpx skills add google/agents-cli --skill google-agents-cli-evalgit clone --depth 1 https://github.com/google/agents-cliWhat 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 | $0.00125 | $0.05115 |
| Opus 5 | $0.00063 | $0.02558 |
| Sonnet 5 | $0.00025 | $0.01023 |
| Haiku 4.5 | $0.00013 | $0.00511 |
Grade A, and why
google-agents-cli-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 yesterday.
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 — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation Guide
Requires:
agents-cli(uv tool install google-agents-cli) — install uv first if needed.
Scaffolded project? If you used
/google-agents-cli-scaffold, you already haveagents-cli eval run(chainsgenerate+grade),tests/eval/datasets/, andtests/eval/eval_config.yaml. Start with executingeval runand iterate from there.
Reference Files
| File | Contents |
|---|---|
references/dataset_schema.md |
Canonical EvaluationDataset schema — all field types, JSON examples for single-turn / multi-turn / multi-agent, common mistakes |
references/metrics-guide.md |
Complete metrics reference — all built-in metrics, match types, custom metrics, judge model config |
references/user-simulation.md |
Dynamic conversation testing — eval dataset synthesize flags, what scenarios are, compatible metrics |
references/builtin-tools-eval.md |
google_search and model-internal tools — trajectory behavior, metric compatibility |
references/advanced-commands.md |
Opt-in commands: eval analyze, eval optimize, eval submit / eval results |
references/multimodal-eval.md |
Multimodal inputs — eval dataset schema, built-in metric limitations, custom evaluator pattern |
The Quality Flywheel
Improving agent quality is iterative. The 4 stages below describe the loop. Each stage has a Default path (you, the coding agent, do the work directly) and an Opt-in CLI command that delegates to the Agent Platform Eval Service for better quality and scale.
1. Prepare Data
Default: Use or edit the scaffolded tests/eval/datasets/basic-dataset.json to define single-turn eval inputs. Start with 1–2 cases.
Opt-in: agents-cli eval dataset synthesize: user-simulate multi-turn datasets when you lack data; its output already includes traces, so Stage 2 collapses to agents-cli eval grade alone. See Eval Commands and references/user-simulation.md.
What ships with it
6 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.
- yesterday First seen · 365 lines · 125 tokens per session scan A d38c9792ec73
google-agents-cli-eval is a skill published in the GitHub repository google/agents-cli (5,759 stars, last pushed 4d ago), licensed Apache-2.0. It adds 125 tokens to every session and 5,115 once invoked, about $0.0006 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.
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