NagaAgent is a desktop AI assistant built around conversation, memory, tool use, and a virtual Live2D character. It is intended for personal interaction, knowledge exploration, game assistance, and automated tasks through components such as MCP and OpenClaw. The catalogue skills relate to its assistant and tool-integration workflows.
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 RTGS2017/NagaAgent --skill code-reviewgit clone --depth 1 https://github.com/RTGS2017/NagaAgentWrote 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/rtgs2017/nagaagent/code-review)<a href="https://agentmods.dev/skills/rtgs2017/nagaagent/code-review"><img src="https://agentmods.dev/badge/skills/rtgs2017/nagaagent/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/rtgs2017/nagaagent/code-review"><img src="https://agentmods.dev/badge/skills/rtgs2017/nagaagent/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.00513 |
| Opus 5.5 | $0.00017 | $0.00205 |
| Sonnet 5.5 | $0.00008 | $0.00103 |
| Haiku 4.5 | $0.00004 | $0.00051 |
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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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 · 73 lines · 42 tokens per session scan A 962e48fbd2ca
code-review is a skill published in the GitHub repository RTGS2017/NagaAgent (1,539 stars, last pushed 2mo ago), licensed AGPL-3.0. It adds 42 tokens to every session and 513 once invoked, about $0.0002 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.
Other skills, from other repositories
code-review
Review code for quality, maintainability, and correctness.
code-review
Comprehensive code review for security, performance, and best practices.
sage-review
Deep, platform-neutral code review for PRs and CRs in ONE thorough single pass — design reasoning (Problem Worth Solving & Solution Fit) as one dimension alongside the 9 code-level dimensions, with chain-of-consequences, self-critique, and draft-only comments. The single app-owned source of truth.
code-review
Reviews diffs by severity to produce actionable feedback.
code-review
Automated code review with checklist and quality gates.
review-loop
Iterative code review — fix P1+P2 until clean, report P3 as an offer.