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/queryWrote 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/query)<a href="https://agentmods.dev/skills/qxbyte/pluginhub/query"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/query/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/query"><img src="https://agentmods.dev/badge/skills/qxbyte/pluginhub/query.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 | $0.00071 | $0.00631 |
| Opus 5.5 | $0.00028 | $0.00252 |
| Sonnet 5.5 | $0.00014 | $0.00126 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
query 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
RagKit Query
对项目 knowledge-base/ 做多路召回（向量+词汇+元数据，RRF 融合），返回定位卡片。
执行
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 \
query '<检索词>' --kb <项目根>/knowledge-base
- 检索词由你根据用户问题/需求自行提炼：优先用页面名、字段名、接口路径、功能域词。
- 允许多轮、多角度：一轮不够就换角度再查（按页面查 / 按字段查 / 按调用链查），直到定位充分或确认无相关知识。
- 需要程序化消费时加
--json；--top N调数量。
结果使用纪律（硬约束）
- 卡片是定位指针，非事实来源——命中后用「路径」
Read原文，再跳到真实代码验证；禁止仅凭卡片摘要下结论。 - tag/词面命中 ≠ 语义相关：逐条判断该知识点的改动类型/语义是否真适用，不适用就丢弃。
- 独立 RAG 用法（用户直接提问系统历史逻辑）：召回 → Read 命中文档 → 综合整理回答，并注明来源文档路径。
降级与错误（stderr 信号，固定文案）
无可用向量后端：结果仍有效（词汇+元数据路）；stderr 会带一个 ╭─ RagKit ─╮ 提示块，原样转述给用户（不改写、不省略）。向量后端调用失败（vector_error）：云端密钥失效/网络异常等，query 不崩溃，已自动降级词汇+元数据路，结果仍有效；stderr 带失败详情，转述并提示用户检查密钥（环境变量是否设对、是否用了完整 key）或网络后重试。索引不存在：提示用户先跑/ragkit:embed。model_mismatch/index_stale：提示用户重跑/ragkit:embed（必要时--rebuild）。
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 · 35 lines · 71 tokens per session scan A 9cf564b92a12
query is a skill published in the GitHub repository qxbyte/pluginhub (3 stars, last pushed 25d ago), licensed MIT. It adds 71 tokens to every session and 631 once invoked, about $0.0003 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.
Other skills, from other repositories
index
Use when the user wants to index their vault for semantic search, rebuild the RAG index, or says "index", "reindex", or "rebuild index".
mcp-builder
DEPRECATED: This skill has been replaced by mcp-app-builder. Check if mcp-app-builder is available in the skills folder. If not, install it: npx skills install mcp-use/mcp-use --skill mcp-app-builder Use mcp-app-builder instead of this skill. Build Model Context Protocol (MCP) servers with mcp-use framework. Use when…
thinking-out-loud
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's…
xberg-chunking
Use when splitting extracted text into chunks for LLM context windows or RAG ingestion. Covers chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone chunk command.
xberg-extracting-keywords
Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings for RAG and search. Covers the keyword config (and its feature gating), --detect-language, and the standalone embed command with real flags.
xberg-picking-a-format
Use when choosing an output format for extracted documents — plain text, markdown, djot, HTML, JSON, or DocTags. Maps consumer (LLM, parser, archive) to the right --format / --content-format pair.