nWave-ai Agent Quality Critique Dimensions
Use these dimensions when reviewing or validating agent definitions.
Dimension 1: Template Compliance
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.
Dimension 2: Size and Focus
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.
Dimension 3: Divergence Quality
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.
Dimension 4: Safety Implementation by Construction (GDP-0)
Which producer could make the unsafe action unrepresentable — did the spec construct it away, or reach for a check?
Check: Tools restricted via frontmatter tools field | maxTurns set | permissionMode set for risky actions | provider-enforced structured outcome used only for ephemeral semantic IPC | terminal prose never parsed as control input | durable handovers/documents produced by existing CLI/software from authority and observable effects | any hook present only as last resort with a recorded reason | No embedded enterprise safety frameworks
Severity: High -- prose safety is ineffective and token-wasteful.
Dimension 5: Language and Tone
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.
Dimension 6: Examples Quality
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.
Dimension 7: Skill Loading Effectiveness
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 ON-TRIGGER) mean sub-agents either waste context on unused eager preload or operate without domain knowledge. The skills: frontmatter field eagerly preloads full skill content into context; omit it and load ON-TRIGGER via Skill invocation instead.
Gold standard: nw-product-owner.md — Skill Loading Strategy table mapping phases to skills with triggers + explicit Load: directives in each workflow phase.
Dimension 8: Token Efficiency
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
Dimension 9: Priority Validation
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 Result Boundary
Evaluate every dimension and cite concrete findings in diagnostic prose. In a managed invocation, the caller may request a provider-enforced ephemeral semantic outcome; it must not ask the model to print YAML, JSON, headings or a verdict grammar. Findings remain semantic review evidence. Any durable review projection is materialized by existing CLI/software, never by the reviewer.
Failure Conditions
Review is not acceptable 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