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 qxbyte/pluginhub --skill using-ragkitgit clone --depth 1 https://github.com/qxbyte/pluginhubWrote 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/qxbyte/pluginhub/using-ragkit)<a href="https://agentmods.dev/skills/qxbyte/pluginhub/using-ragkit"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/using-ragkit/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/qxbyte/pluginhub/using-ragkit"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/using-ragkit.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 | $0.00062 | $0.00453 |
| Opus 5.5 | $0.00025 | $0.00181 |
| Sonnet 5.5 | $0.00012 | $0.00091 |
| Haiku 4.5 | $0.00006 | $0.00045 |
Grade A, and why
using-ragkit 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.
What it actually says
using-ragkit — ragkit 可用性说明（会话启动注入）
本技能是 ragkit 的会话启动 advisory。在 Kimi Code 由清单的
sessionStart.skill于会话开始时注入；在有 SessionStart hook 的宿主（Claude Code / CodeBuddy）由 hook 注入等价文案，无需本技能。
RagKit 可用：对项目 knowledge-base/ 做多路检索（向量 + 词汇 + 元数据，RRF 融合）。四个技能：
ragkit:query— 检索，返回定位卡片（非事实来源，仅用于快速定位真实代码）。ragkit:embed— 构建 / 更新向量索引。ragkit:status— 索引健康 / drift。ragkit:eval— 检索精度评估。
用户要检索 / 建索引 / 查健康时，用 Skill 工具（或宿主等价的 skill 调用机制）按名调用对应技能（/ragkit:* 或直接按名，无需去找命令文件）。
硬约束（防脱轨）：① 只按名调技能，绝不在文件系统里搜 skill / 脚本文件——插件文件在插件缓存目录、不在你的项目里，技能内部会自己定位脚本；② 检索只能调 RagKit 的脚本，严禁自己读 .ragkit 向量文件、装 numpy、或用 embedding API 手搓相似度（结果会错且不可复现）；③ 脚本定位 / 运行失败 → 停下报确切错误，不要绕过、不要自己实现。
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 · 20 lines · 62 tokens per session scan A 15b65bdf508f
using-ragkit is a skill published in the GitHub repository qxbyte/pluginhub (3 stars, last pushed 25d ago), licensed MIT. It adds 62 tokens to every session and 453 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 2026-08-31.
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