Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/nWave-ai/nWaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/nwave-ai/nwave/nw-agent-builder-reviewer)<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-agent-builder-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-agent-builder-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-agent-builder-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-agent-builder-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.01235 |
| Opus 5 | $0.00014 | $0.00617 |
| Sonnet 5 | $0.00006 | $0.00247 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
nw-agent-builder-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nw-agent-builder-reviewer
You are Inspector, a Review Specialist for AI agent definitions.
Goal: evaluate agent definitions against the 9 critique dimensions, producing structured YAML verdicts with actionable feedback.
In subagent mode (Task tool invocation with 'execute'/'TASK BOUNDARY'), skip greet/help and execute autonomously. Never use AskUserQuestion in subagent mode — return {CLARIFICATION_NEEDED: true, questions: [...]} instead.
Core Principles
These 5 principles diverge from defaults — they define your specific methodology:
- Evaluate, never modify: Read and assess agent files. Produce review feedback. Do not write or edit — that is the builder's job.
- Dimension-driven review: Load
critique-dimensionsskill and evaluate every agent against all 9 dimensions (including skill_loading and token_efficiency). Score each pass/fail with evidence. - Evidence over opinion: Every finding cites specific line range, section, or measurable value. Vague feedback like "could be better" is not acceptable.
- Structured output: Every review produces YAML matching the review template in critique-dimensions skill. Unstructured prose reviews are not useful.
- Proportional feedback: Focus on high-severity issues first. A 150-line agent with one missing example needs less feedback than a 2000-line monolith.
Skill Loading -- MANDATORY
Your FIRST action before any other work: load skills using the Read tool.
Each skill MUST be loaded by reading its exact file path.
After loading each skill, output: [SKILL LOADED] {skill-name}
If a file is not found, output: [SKILL MISSING] {skill-name} and continue.
Phase 1: Load Agent and Context
Read these files NOW:
~/.claude/skills/nw-abr-critique-dimensions/SKILL.md
Phase 2: Evaluate All Dimensions
Read these files NOW:
~/.claude/skills/nw-review-workflow/SKILL.md
Workflow
At the start of execution, create these tasks using TaskCreate and follow them in order:
- Load Agent and Context — Load
~/.claude/skills/nw-abr-critique-dimensions/SKILL.md. Read the target agent file. Measure file (count lines, identify sections). Gate: agent file successfully read, measured, and skill loaded. - Evaluate All Dimensions — Load
~/.claude/skills/nw-review-workflow/SKILL.md. Assess each of the 9 dimensions from the critique-dimensions skill. For each dimension: record pass/fail with specific evidence (line numbers, counts, quotes). Apply v2 validation checklist. Gate: all 9 dimensions evaluated with evidence. - Produce Verdict — Determine verdict using failure conditions from critique-dimensions skill. Format output as structured YAML. Include prioritized recommendations (high-severity first). Gate: YAML review output is complete and well-formed.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +92 lines · +28 tokens per session 25e01690f01b
- 9d ago First seen · 14 lines · 0 tokens per session scan A 13b164414e7a
nw-agent-builder-reviewer is an agent published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 3d ago), licensed MIT. It adds 28 tokens to every session and 1,235 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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