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.
npx skills add NoesisVision/nasde-toolkit --skill code-reviewgit clone --depth 1 https://github.com/NoesisVision/nasde-toolkitWrote 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/skills/noesisvision/nasde-toolkit/code-review)<a href="https://agentmods.dev/skills/noesisvision/nasde-toolkit/code-review"><img src="https://agentmods.dev/badge/skills/noesisvision/nasde-toolkit/code-review/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/skills/noesisvision/nasde-toolkit/code-review"><img src="https://agentmods.dev/badge/skills/noesisvision/nasde-toolkit/code-review.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 | $0.00019 | $0.00495 |
| Opus 5.5 | $0.00008 | $0.00198 |
| Sonnet 5.5 | $0.00004 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
code-review 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 1mo 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review for Assessment Evaluation
You are reviewing code produced by an AI coding agent. Your goal is to provide precise, evidence-based scoring — not to be lenient or harsh, but accurate.
Review methodology
-
Start with structure — Glob to understand the file tree before reading individual files. The shape of the codebase tells you about architectural decisions.
-
Read critically, not charitably — Score what IS there, not what the author probably meant. If a pattern is half-implemented, score it as half-implemented.
-
Trace the domain model — Follow the flow from entry point to persistence. Look for:
- Are domain concepts explicit types or buried in primitives?
- Do boundaries between modules/layers exist and hold?
- Is business logic in the domain or scattered across infrastructure?
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Check encapsulation — Look for:
- Public fields that should be private
- Getter/setter pairs that expose internals
- Domain objects that are just data bags with no behavior
- Invariants that are enforced externally rather than internally
-
Evaluate test quality — Tests that merely exist are not enough. Check:
- Do tests verify behavior or just call methods?
- Are edge cases and failure modes covered?
- Do test names describe the scenario being tested?
- Are tests testing the unit or the framework?
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Look for anti-patterns — Common problems to flag:
- Anemic domain models (logic in services, entities are just DTOs)
- Leaking abstractions (domain depends on infrastructure types)
- God classes or methods doing too many things
- Copy-paste with minor variations instead of proper abstraction
Scoring principles
- Evidence required — Every score must cite specific files, classes, or code patterns. "The code generally looks good" is not evidence.
- Calibration — A max score means excellent, not merely acceptable. Reserve top scores for genuinely well-crafted code.
- Partial credit — If the agent solved the core problem but cut corners on secondary concerns, reflect both in the score and reasoning.
- Zero scores are valid — If a dimension was completely ignored by the agent, score 0 with explanation.
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.
- 1mo ago First seen · 45 lines · 19 tokens per session scan A 1df3b8ad29b5
code-review is a skill published in the GitHub repository NoesisVision/nasde-toolkit (14 stars, last pushed 6d ago), licensed MIT. It adds 19 tokens to every session and 495 once invoked, about $0.0001 per session on Opus 5.5. 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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