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name: evaluation-debugging description: "Design and run ADK evaluation, test, and debugging workflows for eval sets, JSON fixtures, event traces, sessions, and safe native verification." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Evaluation Debugging

Use this sub-skill when the user needs to create or run ADK evaluation cases, replay JSON tests, inspect session events, summarize traces, or choose safe native verification targets for an ADK agent.

Route Common Requests

• Convert a manual interaction into an adk test fixture or replay existing tests/*.json event files with deterministic mocks: use Evaluation Workflows.
• Run metric-based EvalSet checks with adk eval, adk eval_set, or google.adk.evaluation.AgentEvaluator: use Evaluation Workflows.
• Debug missing tool calls, unexpected output, callback order, branch/node routing, or event/session state: use Debugging.
• Diagnose schema validation, credentials, flaky LLM metrics, missing trace attributes, and server session lookup failures: use Troubleshooting.
• Summarize raw ADK session/event JSON without exposing long values: run summarize_adk_events.py.

Boundaries

• Stay in this sub-skill for adk test, adk eval, adk eval_set, JSON test/eval fixtures, AgentEvaluator, event printing, session/trace inspection, and selecting safe native eval/debug verification candidates.
• Route general CLI command discovery, agent app layout, server startup, deployment, and YAML configuration mechanics to cli-configuration-deployment.
• Route code changes to agent constructors, tools, callbacks, instructions, schemas, or model binding to agent-construction or tools-and-integrations.
• Route repository maintainer style, broad pytest policy, docs/sample update policy, and PR review gates to repo-development.

Safe Defaults

• Prefer read-only checks first: adk test --help, adk eval --help, adk eval_set --help, fixture validation, event summaries, and focused native candidates.
• Do not start a long-running adk web or api_server process unless the user asks or confirms; if one is already running, inspect it before starting another.
• Treat model credentials, cloud storage, Vertex AI scenario generation, and LLM-as-judge metrics as environment-dependent; separate these from deterministic local replay.
• Never print full tool arguments, session state, trace payloads, or model requests when they may contain secrets; truncate and redact before sharing.

Quick Decision Guide

Goal	Best path	Why
Regression-test exact event flow	adk test AGENTS_DIR with tests/*.json	Replays stored event JSON, mocks model outputs/randomness, and normalizes volatile IDs.
Score behavior against references/tools	adk eval AGENT_INIT EVAL_SET or AgentEvaluator.evaluate(...)	Uses EvalSet metrics such as tool_trajectory_avg_score and response_match_score.
Inspect why a web session failed	Fetch session JSON, summarize events, then inspect /dev/apps/{app}/debug/trace/session/{session}	Correlates events, node paths, tool calls, and LLM/tool spans.
Choose repo-native verification	Start with focused evaluation unit coverage, selected eval fixture integration coverage, and CLI help	Avoids broad, credential-heavy, or server-dependent runs unless needed.

Bundled Reference Map

• Evaluation Workflows — EvalSet schemas, legacy JSON, CLI/API routes, fixture design, assertions, and native candidates.
• Debugging — adk run/web debugging, session and trace inspection, event fields, callback order, and print_event usage.
• Troubleshooting — Concrete failure signatures for schema mismatches, credentials, flaky metrics, missing tools, sessions, and trace attributes.
• summarize_adk_events.py — Safe stdin/file event JSON summarizer for sessions, event arrays, and trace-like dumps.