Search Integration

by kangarooking

Search Integration is an AI Engineering skill for Claude Code, published by kangarooking in system-prompt-skills.

182 stars40 forkson kangarooking/system-prompt-skillsAdded 2026/09/06+3% in starsRepository updated 2026/05/04

View source on GitHub Download repo ZIP

agent-designagent-skillsai-agentscangjie-skillcontext-managementprompt-engineeringprompt-injectionsystem-prompttool-use

Install in seconds

Install Search Integration

Copy Search Integration into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration ~/.claude/skills/search-integration

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/kangarooking/system-prompt-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: search-integration/SKILL.md in kangarooking/system-prompt-skills
Installs to: ~/.claude/skills/search-integration
Collection: One of 15 skills cataloged from this repository
Category: AI Engineering — 3278 skills

What Search Integration does

Search Integration defines when an AI should search, what sources to search, and how to handle results. Use it for real-time knowledge retrieval in chatbots, research assistants, and enterprise Q&A; it is not for offline or purely internal knowledge tasks.

Search Integration is cataloged under AI Engineering on DirSkills. Search Integration comes from a repository tagged agent-design, agent-skills, ai-agents, cangjie-skill and context-management.

Documentation

README

Excerpt

搜索与知识检索集成

R — 原文 (Reading)

跨供应商系统提示词中浮现的搜索策略模式：Claude Web 的"自信不是跳过搜索的理由"、Perplexity 的"追问必须重新搜索"、Gemini 的多查询策略（至少一个问题式+一个关键词式）、Notion AI 的"搜索很便宜，默认每次首次交互都搜"。Le Chat 对所有当代公众人物强制搜索，NotebookLM 则完全不搜索——纯源文档引用。核心分歧点在于搜索的门槛：从"能不搜就不搜"到"默认每次都搜"。

I — 方法论骨架 (Interpretation)

搜索优先策略 (search_first) — 对时效性信息、事实性声明、当代人物/事件，搜索是默认动作而非可选动作
多查询组合 — 单次搜索至少发出两种不同形态的查询：自然语言问题式 + 关键词式，覆盖不同索引模式
源优先级层级 — 企业数据 > 授权语料库 > 公共网页搜索 > 社交媒体，按场景定义层级
追问重新搜索原则 — 不假设前次结果在追问时仍然有效，每次实质性问题都重新检索
领域专用搜索规则 — 金融=单实体聚焦、本地=地理编码、旅行=交通+酒店、体育=完整上下文
搜索成本感知 — "搜索很便宜、安全且快速，用户愿意等待"（Notion AI），降低搜索门槛
无搜索例外 — 纯源文档场景（NotebookLM）用逐句引用替代搜索，保证忠实度

A1 — 案例分析 (Past Application)

案例: Gemini 多查询强制策略

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Search Integration

What else does kangarooking publish alongside Search Integration?
Search Integration is one of 15 skills that DirSkills catalogs from kangarooking/system-prompt-skills, the repository it ships in. Its siblings there include Agent Delegation, Citation System and Code Engineering. Each one is a separate skill with its own page in this directory, installs the same way Search Integration does, and is maintained by kangarooking in that same repository. The rest of the collection is listed on the kangarooking/system-prompt-skills page.
How does Search Integration compare to other AI Engineering skills?
Search Integration ranks #3055 by stars among the 3278 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Search Integration against them. Open each page to compare what they document and how they install.