# Nw Abr Critique Dimensions

> Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation

- Skill: `nwave-ai/nw-abr-critique-dimensions-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add nwave-ai/nw-abr-critique-dimensions-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nwave-ai/nw-abr-critique-dimensions-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: nWave-ai (https://skillmd.com/u/nwave-ai)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/nwave-ai/nw-abr-critique-dimensions-2

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# 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