Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/qxbyte/pluginhubnpx agentmods add skills/qxbyte/pluginhub/embedWrote 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/embed)<a href="https://agentmods.dev/skills/qxbyte/pluginhub/embed"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/embed/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/embed"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/embed.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 | $0.00054 | $0.00444 |
| Opus 5.5 | $0.00022 | $0.00178 |
| Sonnet 5.5 | $0.00011 | $0.00089 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
embed 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 24d 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
RagKit Embed
脚本在本插件的 scripts/ 目录（本 skill 目录的上两级）；用本 skill 的 base directory 把下面的相对路径拼成绝对路径执行。
sh ../../scripts/run.sh ../../scripts/ragkit.py \
embed --kb <项目根>/knowledge-base
- 增量：默认只重嵌变更 chunk；模型/后端变更或索引损坏时加
--rebuild。 - 退出码 3 = 无向量后端：stdout 的 ╭─ RagKit ─╮ 提示块必须原样转述给用户（含第三方 API 配置方法与本地模型安装命令，不要改写）；此时词汇/元数据索引已建好，query 可降级使用。
- 默认走云端（推荐，免下载）：
sh ../../scripts/run.sh ../../scripts/ragkit.py backend set --provider qwen --kb <知识库路径>+ 设置对应密钥环境变量；preset 见提示块（openai / qwen / zhipu / voyage / azure）。不要主动引导用户下载本地模型。 - 本地模型是可选手动项（仅离线/零费用场景，下载约 1.2GB，不会自动下载）：仅在用户明确要求离线时才执行
uv run ../../scripts/ragkit_local_embed.py install（国内先export HF_ENDPOINT=https://hf-mirror.com）。 - 双后端并存时固定用云端（脚本内置优先级，无需选择）；确要离线可在
<知识库路径>/.ragkit/config.json显式写"backend": "local"。
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.
- 24d ago Changed · +1 lines 5abc0c3c39f4
- 1mo ago First seen · 19 lines · 54 tokens per session scan A d0bf5c64dc50
embed is a skill published in the GitHub repository qxbyte/pluginhub (3 stars, last pushed 25d ago), licensed MIT. It adds 54 tokens to every session and 444 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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