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 kumaran-is/claude-code-onboarding --skill adk-eval-guidegit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/kumaran-is/claude-code-onboarding/adk-eval-guide)<a href="https://agentmods.dev/skills/kumaran-is/claude-code-onboarding/adk-eval-guide"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/adk-eval-guide/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/kumaran-is/claude-code-onboarding/adk-eval-guide"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/adk-eval-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00110 | $0.01616 |
| Opus 5 | $0.00055 | $0.00808 |
| Sonnet 5 | $0.00022 | $0.00323 |
| Haiku 4.5 | $0.00011 | $0.00162 |
Grade A, and why
adk-eval-guide 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 6d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADK Evaluation Guide
Iron Law
NEVER evaluate agents against the live Gemini API in unit tests. Use adk eval with evalsets.
Hitting the real API in eval makes results non-deterministic, incurs cost, and breaks CI. Always run evaluations through the ADK eval CLI with a defined evalset and config file.
Reference Files
| File | Contents |
|---|---|
reference/criteria-guide.md |
Complete metrics reference — all 8 criteria, match types, custom metrics, judge model config |
reference/user-simulation.md |
Dynamic conversation testing — ConversationScenario, user simulator config, compatible criteria |
reference/builtin-tools-eval.md |
google_search and model-internal tools — trajectory behavior, metric compatibility |
reference/multimodal-eval.md |
Multimodal inputs — evalset schema, built-in metric limitations, custom evaluator pattern |
Process
- Define criteria — choose metrics based on your goal (see Choosing the Right Criteria below); read
reference/criteria-guide.mdfor all 8 options - Write evalset — create
evalset.jsonwith eval cases; includeintermediate_data.tool_usesfor every turn that expects tool calls - Write eval config — create
eval_config.jsonwith criteria thresholds; setmatch_typetoIN_ORDERunless you need strictEXACTregression tests - Run eval —
adk eval ./app <path_to_evalset.json> --config_file_path=<path_to_config.json> --print_detailed_results - Interpret results — identify which criteria failed; match failure symptom to cause (see Common Eval Failure Causes below)
- Fix the agent — adjust prompts, tool descriptions, or agent instructions; do NOT widen thresholds to hide failures
- Fix the evalset if needed — if expected trajectory doesn't match real agent behavior due to model variance, switch match type or use rubric-based criteria
- Rerun eval — verify the fix resolves the specific failure without regressing other cases
- Expand coverage — once current cases pass, add more eval cases incrementally
- Repeat — expect 5–10 iterations per feature; each iteration makes the agent more reliable
What ships with it
4 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.
- 6d ago First seen · 122 lines · 110 tokens per session scan A 434ded9ff1d4
adk-eval-guide is a skill published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 110 tokens to every session and 1,616 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 2026-09-03.
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