rag-query

rag-query is a skill for Claude Code from zhaixin244-wq/fnw. It costs 17 tokens per session (667 once invoked), scanned A, original, MIT.

A knowledge-retrieval tool that looks up protocol, architecture, and hardware-design information from a structured wiki. RAG means retrieving relevant reference material before answering a question.

In plain words
What is it for?
Use it to look up interfaces such as AXI4, SPI, or PCIe, compare design options, and read integration guides.
Why use it?
It gives the agent a consistent way to find technical information without searching through every source document from scratch.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

Good fit Use it to look up interfaces such as AXI4, SPI, or PCIe, compare design options, and read integration guides.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zhaixin244-wq/fnw/rag-query
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.

Any agent
npx skills add zhaixin244-wq/fnw --skill rag-query
Clone the repo
git clone --depth 1 https://github.com/zhaixin244-wq/fnw

Made for: Claude Code.

Wrote 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.

agentmods badge for rag-query

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-query/github.svg)](https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-query)
Your own site
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-query"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-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.

agentmods 80×15 button for rag-query

Your own site · 80×15
<a href="https://agentmods.dev/skills/zhaixin244-wq/fnw/rag-query"><img src="https://agentmods.dev/badge/skills/zhaixin244-wq/fnw/rag-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 667 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00017 $0.00667
Opus 5 $0.00009 $0.00333
Sonnet 5 $0.00003 $0.00133
Haiku 4.5 $0.00002 $0.00067

Measured 9d ago against content hash 5b04a51084e2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

rag-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 9d 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.

.claude/skills/rag-query/SKILL.md · 81 lines

What it actually says

Wiki Query — 结构化知识检索

基于 LLM Wiki 系统的结构化知识检索。Wiki 是预编译的知识页面集合,比传统 RAG 更高效。

检索流程

Step 1:读取索引

Read .claude/wiki/index.md

在索引中定位相关条目(实体/概念/对比/指南)。

Step 2:读取 Wiki 页面

Read .claude/wiki/{category}/{name}.md

Wiki 页面包含预编译的结构化知识:核心特性、接口信号、关键参数、应用场景、设计注意事项。

Step 3:深入原始文档(按需)

仅当 wiki 信息不足时:

Read .claude/knowledge/{domain}/{name}.md

查询模式

模式 命令示例 说明
实体查询 /rag-query AXI4 读取 entities/axi4.md
选型对比 /rag-query 仲裁器选型 读取 comparisons/arbiter-selection.md
概念查询 /rag-query CDC 策略 读取 concepts/cdc-strategy.md
指南查询 /rag-query AXI4 集成 读取 guides/axi4-integration-guide.md

快速定位表

关键词 Wiki 页面路径
AXI4 / AXI / 总线 entities/axi4.md
APB / 外设总线 entities/apb.md
SPI / I2C / UART entities/spi.md / i2c.md / uart.md
PCIe / USB / MIPI entities/pcie.md / usb.md / mipi.md
FIFO / 同步FIFO entities/sync_fifo.md
异步FIFO / CDC entities/async_fifo.md
仲裁器 / arbiter entities/arbiter.md
交叉开关 / crossbar entities/crossbar.md
选型 / 对比 comparisons/*.md
设计模式 / 概念 concepts/*.md
集成指南 guides/*.md

响应格式

  • 摘要而非原始内容
  • 标注信息来源(wiki 页面名或原始文档路径)
  • 过时信息标记(wiki 页面 >30 天未更新)
  • 无结果时明确说明,不编造

Wiki 维护

当查询产生有价值的结果时,考虑回写为新 wiki 页面:

  • 新的对比分析 → comparisons/
  • 新的设计概念 → concepts/
  • 新的集成经验 → guides/
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. 9d ago First seen · 81 lines · 17 tokens per session scan A 5b04a51084e2

Subscribe to this mod's changes

rag-query is a skill published in the GitHub repository zhaixin244-wq/fnw (29 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 667 once invoked, about $0.0001 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 2026-09-03.

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