Atlas / Skills / nwave-ai / Nw Abr Critique Dimensions

Nw Abr Critique DimensionsSAFE

skills/nwave-ai/nw-abr-critique-dimensions

AI agents that guide you from idea to working code, with you in control at every step.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
1 documented
License
MIT
Stars
617
01

Overview

AI agents that guide you from idea to working code, with you in control at every step.

Read from source at commit 1a864ec81cdeOBSERVED · 2026-10-14
02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
03

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: nw-abr-critique-dimensions
agent: nw-agent-builder-reviewer
description: Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
user-invocable: false
disable-model-invocation: true
---

# 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

**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.

## 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) 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.

## 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 Output Format

```yaml
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}"
```

## Failure Conditions

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
04

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-14 · audit v0.4.1 · source sha 1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-abr-critique-dimensions.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-141a864ec81cdeSAFEB89first audit
06

Questions

What does the Nw Abr Critique Dimensions skill do?

AI agents that guide you from idea to working code, with you in control at every step.

Is Nw Abr Critique Dimensions safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Nw Abr Critique Dimensions access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Nw Abr Critique Dimensions work with?

Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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