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rag-architect

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

2 次下载 4 次浏览 更新于 Oct 31, 2026 v1.0.0
rag-architect skill cover

下方技能配置(提示词、变量、安装步骤)保留英文原文。.aiskill 文件直接面向 AI 智能体,统一使用英文以确保跨平台行为一致。

概述

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

Source: https://github.com/alirezarezvani/claude-skills · MIT · © 2025 Alireza Rezvani

系统提示词

预览已脱敏

定义智能体角色、规则与输出格式的指令块。

system-prompt.txt 
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用户提示词模板

填充变量后发送,即可运行该技能: {{variables}}

user-template.txt 
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变量

名称 必填 说明 示例

完整技能配置

脱敏预览

标准 .aiskill 文件,提供 JSON 与 YAML 两种格式。

rag-architect.aiskill (JSON) 
{
  "format": "aiskill/v1",
  "skill": {
    "name": "rag-architect",
    "slug": "rag-architect",
    "version": "1.0.0",
    "description": "Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).",
    "category": "Development",
    "tags": [
      "ai-agents",
      "sql",
      "llm",
      "rag",
      "agents"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "████████████████████████████████████████████████████████████████████████████████████████████████",
    "user_template": "██████████"
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The rag-architect/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/rag-architect",
    "exported_at": "2026-11-03T07:44:19.851Z"
  }
}
rag-architect.aiskill (YAML) 
format: aiskill/v1
skill:
  name: rag-architect
  slug: rag-architect
  version: 1.0.0
  description: >-
    Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model,
    pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples:
    'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my
    retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or
    agent loops over retrieval (use agenthub).
  category: Development
  tags:
    - ai-agents
    - sql
    - llm
    - rag
    - agents
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: ████████████████████████████████████████████████████████████████████████████████████████████████
  user_template: ██████████
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The rag-architect/ folder contains
        SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/rag-architect
  exported_at: "2026-11-03T07:44:19.851Z"

安装指南

  1. 1

    Download & unzip

    Download the .zip package from SkillVault and unzip it. The rag-architect/ folder contains SKILL.md plus any companion scripts, references and assets.

  2. 2

    Copy into your agent skills directory

    Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session.

  3. 3

    Use it

    Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description.

使用方法

平台兼容性

ChatGPT GPTs

OpenAI

将系统提示词粘贴到 GPT 的「Instructions」字段,并把用户模板作为起始消息使用。

官方文档

Claude Code

Anthropic

将导出的 .aiskill JSON 保存为项目中的技能文件,或将系统提示词粘贴进 CLAUDE.md / 斜杠命令 Markdown 文件。

官方文档

Cursor

Anysphere

将系统提示词复制到 .cursor/rules/*.mdc(或设置中的 Project Rules),并在智能体提示词中引用用户模板。

官方文档

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