Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
npx skills add https://github.com/nWave-ai/nWave --skill nw-abr-critique-dimensions
Use these dimensions when reviewing or validating agent definitions.
Does the agent follow official Claude Code format?
Check: YAML frontmatter with name and description (required) | Markdown body as system prompt | No embedded YAML config blocks | No activation-instructions or IDE-FILE-RESOLUTION sections | Skills referenced in frontmatter, not inline
Severity: High -- non-compliant agents may not load correctly.
Check: Core definition under 400 lines | Domain knowledge in Skills | Single clear responsibility | No monolithic sections (>50 lines without structure) | No redundant Claude default behaviors
Measurement: wc -l {agent-file}. Target: 200-400 lines.
Severity: High -- oversized agents suffer context rot.
Does the agent specify only what diverges from Claude defaults?
Check: No file operation instructions | No generic quality principles ("be thorough") | No tool usage guidelines | Core principles are domain-specific and non-obvious | Each instruction justifies why Claude wouldn't do this naturally
Severity: Medium -- redundant instructions waste tokens, cause overtriggering.
Check: Tools restricted via frontmatter tools field | maxTurns set | No prose-based security layers (use hooks) | No embedded enterprise safety frameworks | permissionMode set for risky actions
Severity: High -- prose safety is ineffective and token-wasteful.
Check: No "CRITICAL:", "MANDATORY:", "ABSOLUTE" language | Direct statements ("Do X" not "You MUST X") | Affirmative phrasing ("Do Y" not "Don't do X") | Consistent terminology | No repetitive emphasis
Severity: Medium -- aggressive language causes overtriggering on Opus 4.6.
Check: 3-5 canonical examples present | Cover critical/subtle decisions (not obvious cases) | Good/bad paired where useful | Concise (not full implementations)
Severity: Medium -- missing examples cause edge case failures.
Does the agent ensure skills are actually loaded during execution?
Check: Skill Loading Strategy table present for agents with 3+ skills | Every frontmatter skill has matching Load: directive in workflow | Skills path documented (~/.claude/skills/nw-{skill-name}/SKILL.md) | Phase-gated loading (not "load everything at start")
Severity: High — orphan skills (declared but never loaded) mean sub-agents operate without domain knowledge. The skills: frontmatter field is declarative only; Claude Code does not auto-load skill files.
Gold standard: nw-product-owner.md — Skill Loading Strategy table mapping phases to skills with triggers + explicit Load: directives in each workflow phase.
Is the agent definition compressed without losing semantic content?
Check: No verbose prose where pipe-delimited lists suffice | Imperative voice throughout | No filler words ("in order to", "it is important to") | ### Example N: headers preserved verbatim (not inlined) | AskUserQuestion options preserved with numbered descriptions | Code blocks preserved verbatim | No duplicate content already in skills
Severity: Medium — bloated definitions waste context window and degrade performance via context rot.
Compression safe: prose descriptions, bullet lists, related items -> pipe-delimited
Compression unsafe: example headers, code blocks, decision tree options, YAML frontmatter
Questions: 1. Is this the largest bottleneck? (Evidence required) | 2. Simpler alternatives considered? | 3. Constraint prioritization correct? | 4. Architecture data-justified?
Severity: High if agent addresses secondary concern while larger problem exists.
review:
agent: "{agent-name}"
dimensions:
template_compliance: {pass|fail}
size_and_focus: {pass|fail}
divergence_quality: {pass|fail}
safety_implementation: {pass|fail}
language_and_tone: {pass|fail}
examples_quality: {pass|fail}
skill_loading: {pass|fail|n/a}
token_efficiency: {pass|fail}
priority_validation: {pass|fail}
issues:
- dimension: "{dimension}"
severity: "{high|medium|low}"
finding: "{description}"
recommendation: "{fix}"
verdict: "{approved|revisions_needed}"
Review blocked (verdict: revisions_needed) if: any high-severity dimension fails | 3+ medium-severity fail | Agent exceeds 400 lines without Skills extraction | Zero examples provided | Agent with 3+ skills missing Skill Loading Strategy table
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This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or mentions LLM-as-judge, multi-dimensional evaluation, agent testing, or quality gates for agent pipelines.
Take nwave-ai/nw-abr-critique-dimensions from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.