Conciseness

The body is mostly efficient code without much explanation of known concepts, but contains clear tightening opportunities: the Overview's 'Key Principles (validated by AWS December 2025)' section duplicates thresholds already encoded in code, and the three full custom-evaluator examples (brand tone, technical accuracy, compliance) plus the duplicate single-vs-loop evaluator creation in Operation 1 pad the file. The time-sensitive 'December 2025' date also sits outside any old-patterns section.

Actionability

Nearly all guidance is concrete, executable boto3 code (create_evaluator calls with real parameter shapes, CloudWatch alarms, dashboard JSON). Minor gaps: the baselines example calls an undefined 'run_evaluation()' helper, and variables like 'agent_arn' and 'helpfulness_evaluator_id' are used without prior definition, keeping it just below copy-paste-ready anchor 5.

Workflow Clarity

The five operations are clearly sequenced (create evaluators → custom evaluators → on-demand evaluation → continuous monitoring → dashboard) with per-operation purpose, time, and automation estimates, and the on-demand flow includes pass/fail reporting. However, the batch evaluation flow lacks an explicit verify-before-deploy checkpoint and the adversarial section asserts without a recovery loop, leaving minor validation gaps versus anchor 5.

Progressive Disclosure

The References section cleanly signals three files ('references/evaluator-reference.md', 'references/test-scenarios.md', 'references/alerting-patterns.md'), but none of these files exist in the bundle, and the content they would hold (alarm patterns, test scenario templates, full evaluator API details) is instead inlined in the ~670-line SKILL.md. This matches anchor 3: content that should be separate is inline and the reference structure is not actually functional.

Specificity

Names the concrete offering ('13 built-in evaluators plus custom LLM-as-Judge patterns') and two core actions (testing and monitoring AI agent quality). It lists several specific capabilities but omits coverage of a few functions shown in the body (dashboards, A/B testing), matching the 'several specific actions; minor gaps' anchor rather than the fully comprehensive anchor 5.

Completeness

Explicitly answers what ('Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns') and when ('Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior') with concrete trigger phrases, exactly matching the anchor-5 example pattern.

Trigger Term Quality

The 'Use when' clause provides natural phrases users would say: 'testing agents', 'monitoring production quality', 'setting up alerts', 'validating agent behavior'. Coverage is good but misses common variations like 'evaluating an agent' or 'agent evaluation metrics', fitting anchor 4 (a few natural terms missing) rather than anchor 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

The 'AgentCore Evaluations' niche is clearly distinct from the sibling skills it must not trigger for (policy enforcement, deployment). However, generic triggers like 'testing agents' and 'validating agent behavior' could fire for non-Bedrock agent testing contexts, giving minor overlap risk — anchor 4 rather than anchor 5's minimal conflict risk.