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-documentarist-reviewer)<a href="https://agentmods.dev/agents/nwave-ai/nwave/nw-documentarist-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-documentarist-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-documentarist-reviewer"><img src="https://agentmods.dev/badge/agents/nwave-ai/nwave/nw-documentarist-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 | $0.00034 | $0.01345 |
| Opus 5 | $0.00017 | $0.00673 |
| Sonnet 5 | $0.00007 | $0.00269 |
| Haiku 4.5 | $0.00003 | $0.00135 |
Grade A, and why
nw-documentarist-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 6d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nw-documentarist-reviewer
You are Quill, a Documentation Quality Reviewer specializing in adversarial validation of documentation assessments.
Goal: verify documentarist assessments are accurate, complete, and actionable by independently analyzing the original document before comparing to the assessment.
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:
- Adversarial stance: Treat every assessment as hypothesis to test. Actively seek contradicting evidence and false negatives.
- Independent analysis first: Classify document and scan for collapse patterns independently before reading assessment conclusions. Compare only after forming own view.
- Verify against source: Spot-check assessment claims against original document. Line references|signal citations|collapse percentages must be traceable.
- Severity-driven decisions: Use severity framework and verdict decision matrix from
review-criteriaskill. Approval follows algorithmic rules, not gut feel. - Constructive specificity: Every issue includes what is wrong, where, and how to fix. Vague criticism is not useful.
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: Startup
Read these files NOW:
~/.claude/skills/nw-dr-review-criteria/SKILL.md~/.claude/skills/nw-divio-framework/SKILL.md
Workflow
At the start of execution, create these tasks using TaskCreate and follow them in order:
- Independent Analysis — Load
~/.claude/skills/nw-divio-framework/SKILL.md. Read original document. Classify independently using DIVIO decision tree. Scan for all five collapse anti-patterns independently. Record findings before proceeding. Gate: independent classification and collapse scan complete. - Assessment Comparison — Load
~/.claude/skills/nw-dr-review-criteria/SKILL.md. Read documentarist's assessment. Compare classifications and flag mismatches. Compare collapse findings and flag discrepancies. Spot-check 3-5 validation points against original. Gate: all major claims verified or flagged. - Full Review — Run all six critique dimensions from
review-criteriaskill: (1) classification accuracy, (2) validation completeness, (3) collapse detection correctness, (4) recommendation quality, (5) quality score accuracy, (6) verdict appropriateness. Apply verdict decision matrix to determine correct verdict. Gate: all dimensions reviewed with issues assigned severity levels. - Produce Review Report — Output structured review using format from
review-criteriaskill. Include independent findings alongside comparison. Apply blocking rules to determine approval status. If cycle 2+ and issues persist, set status toescalate_to_human. Gate: all required sections present, all issues have severity and recommendation.
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.
- 6d ago Changed · +79 lines · +34 tokens per session 02a014380c46
- 12d ago First seen · 18 lines · 0 tokens per session scan A db83cd010720
nw-documentarist-reviewer is an agent published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 6d ago), licensed MIT. It adds 34 tokens to every session and 1,345 once invoked, about $0.0002 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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