embed

An index builder for RagKit, which searches the files in a project's knowledge-base folder using an index.

In plain words
What is it for?
Use it to create or refresh the knowledge-base index, optionally rebuilding it completely when required.
Why use it?
It keeps search data up to date after knowledge changes, without rebuilding unchanged parts by default. It also allows a full rebuild when the search model, backend, or index has changed or broken.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/qxbyte/pluginhub/embed
Any agent
npx skills add qxbyte/pluginhub --skill embed
Clone the repo
git clone --depth 1 https://github.com/qxbyte/pluginhub

Made for: Claude Code, Codex.

Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 306 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00054 $0.00306
Opus 5 $0.00027 $0.00153
Sonnet 5 $0.00011 $0.00061
Haiku 4.5 $0.00005 $0.00031

Measured yesterday against content hash d0bf5c64dc50, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 yesterday.

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.

plugins/ragkit/skills/embed/SKILL.md · 19 lines

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(含安装命令与第三方配置方法,不要改写);此时词汇/元数据索引已建好,query 可降级使用。
  • 首次装本地模型:uv run ../../scripts/ragkit_local_embed.py install(约 1.2GB;国内先 export HF_ENDPOINT=https://hf-mirror.com)。
  • 双后端并存时固定用本地(脚本内置优先级,无需选择)。
Changes

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.

  1. yesterday First seen · 19 lines · 54 tokens per session scan A d0bf5c64dc50

Subscribe to this mod's changes

embed is a skill published in the GitHub repository qxbyte/pluginhub (3 stars, last pushed 27d ago), licensed MIT. It adds 54 tokens to every session and 306 once invoked, about $0.0003 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 2026-08-31.

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