Open Notebook
majiayu000/claude-skill-registry
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis.
当系统提示词需要定义 AI 模型何时搜索、如何搜索、搜索哪些数据源、以及如何处理搜索结果时调用此 Skill。适用于所有涉及实时知识检索的 AI 产品设计——聊天机器人、研究助手、企业知识库问答等。不适用于：纯离线场景（无搜索能力的环境）、已完全内化的知识问答（如数学推导）、创意生成任务。当需求仅涉及"从本地文件读取内容"而非"联网检索外部信息"时，这不是最佳 Skill。
Install Search Integration
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install kangarooking/system-prompt-skills search-integration --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/search-integration .claude/skills/search-integration && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .claude/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/kangarooking/system-prompt-skills/tree/main/search-integrationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install kangarooking/system-prompt-skills search-integration --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/search-integration .agents/skills/search-integration && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .agents/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install kangarooking/system-prompt-skills search-integration --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/search-integration .cursor/skills/search-integration && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .cursor/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/kangarooking/system-prompt-skills.git --path search-integration--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install kangarooking/system-prompt-skills search-integration --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/search-integration .gemini/skills/search-integration && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .gemini/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install kangarooking/system-prompt-skills search-integrationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/search-integration .github/skills/search-integration && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .github/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill search-integration -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install kangarooking/system-prompt-skills search-integration --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/search-integration .opencode/skills/search-integration && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "search-integration" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/search-integration into .opencode/skills/search-integration/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "search-integration", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
search-integration- GitHub stars
- 205
- Token cost
- ~747 tokens
- SKILL.md length
- 172 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
当系统提示词需要定义 AI 模型何时搜索、如何搜索、搜索哪些数据源、以及如何处理搜索结果时调用此 Skill。适用于所有涉及实时知识检索的 AI 产品设计——聊天机器人、研究助手、企业知识库问答等。不适用于：纯离线场景（无搜索能力的环境）、已完全内化的知识问答（如数学推导）、创意生成任务。当需求仅涉及"从本地文件读取内容"而非"联网检索外部信息"时，这不是最佳 Skill。
- Works in 7 steps: 搜索优先策略 (search_first) —… → 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 +… → 源优先级层级 — 企业数据 > 授权语料库 > 公共网页搜索 >… → …
- Knowledge Management work in your project
- SKILL.md covers R — 原文 (Reading), I — 方法论骨架 (Interpretation), A1 — 案例分析 (Past Application) and A2 — 触发场景 (Future Trigger) ★, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Search Integration is an agent skill from kangarooking/system-prompt-skills. 当系统提示词需要定义 AI 模型何时搜索、如何搜索、搜索哪些数据源、以及如何处理搜索结果时调用此 Skill。适用于所有涉及实时知识检索的 AI 产品设计——聊天机器人、研究助手、企业知识库问答等。不适用于：纯离线场景（无搜索能力的环境）、已完全内化的知识问答（如数学推导）、创意生成任务。当需求仅涉及"从本地文件读取内容"而非"联网检索外部信息"时，这不是最佳 Skill。
Its SKILL.md is about 750 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Knowledge Management. It works with NotebookLM and Perplexity. The repository describes itself as: 从 165 个顶级 AI 产品系统提示词中蒸馏出的 15 个可执行 Agent skill. The licence is MIT.
When your agent uses it
- Knowledge Management work in your project
Example prompts
- “从本地文件读取内容”
- “联网检索外部信息”
- “/search-integration”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- 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
What it can do on your machine
Read from SKILL.md and the folder at commit 252cd52. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Search Integration loads about 747 tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 172 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from kangarooking/system-prompt-skills at commit 252cd52, republished under its MIT licence (© kangarooking). 172 words, ~747 tokens.
Download SKILL.mdSave it as
.claude/skills/search-integration/SKILL.md (or your agent's skills folder).- name
- search-integration
- description
- 当系统提示词需要定义 AI 模型何时搜索、如何搜索、搜索哪些数据源、以及如何处理搜索结果时调用此 Skill。适用于所有涉及实时知识检索的 AI 产品设计——聊天机器人、研究助手、企业知识库问答等。不适用于：纯离线场景（无搜索能力的环境）、已完全内化的知识问答（如数学推导）、创意生成任务。当需求仅涉及"从本地文件读取内容"而非"联网检索外部信息"时，这不是最佳 Skill。
- tags
- 搜索策略, 知识检索, 多源查询, 时效性, 引用规范
- related_skills
- context-management, conversation-flow
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
- 问题: 单一查询无法覆盖用户意图的不同表述维度，导致搜索结果遗漏关键信息
- 设计模式的使用: Gemini 3.1 Pro 系统提示词要求每次搜索至少发出两个查询：一个自然语言问题式查询（捕捉语义）加一个关键词式查询（捕捉精确匹配），且所有查询必须使用用户的原始语言
- 结论: 多查询策略将信息检索从"猜一个最佳查询"升级为"多角度覆盖"，显著降低信息遗漏率
案例: Perplexity 追问重新搜索原则
- 问题: 用户追问时，系统倾向于复用前次搜索结果以节省时间和 token，但信息可能已过时
- 设计模式的使用: Perplexity 系统提示词明确规定："追问时始终重新搜索，而非假设前次结果仍然足够"
- 结论: 该策略牺牲了效率换取了准确性，尤其在新闻、金融等高时效性场景中效果显著
案例: NotebookLM 的反搜索模式
- 问题: 通用搜索可能引入外部信息污染对源文档的忠实解读
- 设计模式的使用: NotebookLM 完全不搜索，严格基于用户上传的源文档，配合逐句引用机制确保每个声明都可追溯
- 结论: 在需要高忠实度的场景（学术分析、法律文档）中，"不搜索"反而是正确策略
A2 — 触发场景 (Future Trigger) ★
用户在什么情境下需要?
- 设计需要联网能力的 AI 助手系统提示词，需要定义"何时搜、搜什么、搜几个"
- 构建企业知识库问答系统，需要定义内部语料库与外部搜索的优先级关系
- 为垂直领域 AI（金融、医疗、法律）设计搜索策略，需要领域专用规则
- 优化现有 AI 产品的搜索触发率——用户反馈"信息过时"或"回答缺少最新数据"
- 设计研究型 AI 产品（如 Deep Research），需要多轮搜索与结果整合策略
语言信号
- "AI 回答的信息过时了"
- "需要引用最新数据/新闻"
- "先搜索再回答，不要凭记忆"
- "企业内部知识优先于网络搜索"
- "每次追问都要重新查一下"
与相邻 skill 的区分
- 与
context-management的区别: context-management 管理已有上下文的压缩和加载，本 Skill 管理外部信息的获取时机和策略 - 与
conversation-flow的区别: conversation-flow 管理对话路由和澄清策略，本 Skill 专注于搜索决策（搜不搜、搜几个、搜哪里）
E — 可执行步骤 (Execution)
定义搜索触发规则矩阵 — 完成标准: 建立按内容类型（时效性/事实性/人物/观点）和时效要求（实时/近期/历史）的二维矩阵，明确每种组合下的搜索策略（强制搜索/建议搜索/可选搜索/禁止搜索）
设计多查询组合模板 — 完成标准: 为每个搜索触发点定义至少两种查询形态（问题式 + 关键词式），包含语言保持规则（使用用户原始语言）和查询扩展策略
建立源优先级层级 — 完成标准: 定义至少三层源优先级（如：企业语料库 > 授权数据库 > 公共网页），包含跨源冲突时的裁决规则和降级策略
编写追问搜索策略 — 完成标准: 明确规定追问场景下的搜索行为——至少区分"信息补充型追问"（重新搜索）和"逻辑澄清型追问"（不搜索），包含时间窗口判断（超过 N 分钟必须重新搜索）
添加领域专用搜索参数 — 完成标准: 为金融（单实体聚焦+时间范围）、本地（地理编码+距离半径）、旅行（交通+住宿+天气联合查询）、体育（完整赛事上下文）等垂直领域定义专用搜索参数集
B — 边界 (Boundary) ★
不要在以下情况使用
- 纯数学/逻辑推理任务——搜索不会比模型推理更准确
- 创意写作/文学创作——外部搜索可能干扰创意一致性
- 已有完整上下文的封闭文档问答（NotebookLM 模式）——搜索会引入噪音
- 实时性要求极低的历史/哲学讨论——模型内化知识已足够
常见失败模式
- 搜索过度：每次回复都搜索即使用户只是在闲聊，增加延迟和成本却无信息增益
- 单查询依赖：只用一种查询形态，遗漏不同索引维度的信息
- 源优先级倒置：公共网页信息覆盖了企业内部权威数据，导致回答偏离业务事实
- 追问不重新搜索：复用过时结果回答追问，在新闻/金融场景中产生幻觉
- 忽视语言一致性：用英语搜索再翻译回中文，丢失查询精度和文化语境
© kangarooking, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in search-integration of kangarooking/system-prompt-skills.
Open the folder on GitHubat commit 252cd52
Compare with similar skills
Search Integration next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Search Integration this skillkangarooking/system-prompt-skills | 205 | — | ~747 | Automated safety check: Pass | MIT | |
| Open Notebookmajiayu000/claude-skill-registry | 666 | 4 repos | ~2.4k | Automated safety check: Pass | MIT | |
| NotebookLM Automationteng-lin/notebooklm-py | 20k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Cninfo To Notebooklmjarodise/CNinfo2Notebookllm | 363 | — | ~1.1k | Automated safety check: Pass | None | |
| Notebooklmroomi-fields/notebooklm-mcp | 189 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Notebooklmsanjay3290/ai-skills | 431 | — | ~655 | Automated safety check: Pass | Apache-2.0 |
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Cninfo To Notebooklm
jarodise/CNinfo2Notebookllm
A skill your agent uses when user wants to analyze China stock reports (A-share or Hong Kong), upload annual/quarterly reports to NotebookLM, or research a Chinese listed company's financials
Notebooklm
roomi-fields/notebooklm-mcp
This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio…
Notebooklm
sanjay3290/ai-skills
Query and manage Google NotebookLM notebooks with persistent profile auth, source sync, batch/multi queries, and structured exports.
Wechat To Notebooklm
zstmfhy/wechat-to-notebooklm
WeChat article to NotebookLM sync tool. An agent skill from zstmfhy/wechat-to-notebooklm.
More from kangarooking/system-prompt-skills
All 15 skills in this repoPersona Design
kangarooking/system-prompt-skills
当需要为 AI 产品定义核心身份、角色声明和能力边界时调用此 skill。典型场景包括：设计新 AI 产品的 system prompt 首段、为不同场景创建差异化角色（如教学助手 vs 编程代理）、重新定义 AI 与用户的关系框架。
Tool Specification
kangarooking/system-prompt-skills
当需要为 AI 定义工具接口、设计调用规范、实现工具发现与编排机制时调用此 skill。典型场景包括：设计 AI agent 的工具集、定义 JSON Schema/XML/TypeScript 格式的工具描述、实现工具权限控制与并行调度、设计子代理委托架构。
Memory System
kangarooking/system-prompt-skills
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。
Personality System
kangarooking/system-prompt-skills
当需要在基础身份之上叠加可切换的人格风格层时调用此 skill。典型场景包括：为同一产品提供多种人格选项（如 GPT-5.1 的 friendly/professional/quirky 模式）、设计人格切换机制、防止人格泄露到用户内容中。
Conversation Flow
kangarooking/system-prompt-skills
当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用…
Safety Guardrails
kangarooking/system-prompt-skills
当需要为 AI 系统设计多层安全防线、内容过滤策略和伦理边界时调用此 skill。典型场景包括：设计拒绝策略与升级机制、防御 prompt 注入攻击、实现领域特定安全规则（教育、医疗、金融等）、定义 AI 的价值观锚点。
Related
Works with
Categories
Questions about Search Integration
What does Search Integration do?
当系统提示词需要定义 AI 模型何时搜索、如何搜索、搜索哪些数据源、以及如何处理搜索结果时调用此 Skill。适用于所有涉及实时知识检索的 AI 产品设计——聊天机器人、研究助手、企业知识库问答等。不适用于：纯离线场景（无搜索能力的环境）、已完全内化的知识问答（如数学推导）、创意生成任务。当需求仅涉及"从本地文件读取内容"而非"联网检索外部信息"时，这不是最佳 Skill。. Search Integration is an agent skill from kangarooking/system-prompt-skills.
When should I use Search Integration?
Search Integration fits situations like: knowledge Management work in your project.
How do I install Search Integration in Claude Code?
Run `npx skills add kangarooking/system-prompt-skills --skill search-integration -a claude-code`. Or copy the skill folder (search-integration in kangarooking/system-prompt-skills) into .claude/skills/search-integration in your project. Claude Code loads it when a task matches its description.
How do I install Search Integration in Codex?
Run `npx skills add kangarooking/system-prompt-skills --skill search-integration -a codex`. Or copy the skill folder (search-integration in kangarooking/system-prompt-skills) into .agents/skills/search-integration in your project. Codex loads it when a task matches its description.
Can I use Search Integration in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kangarooking/system-prompt-skills --skill search-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/search-integration, .gemini/skills/search-integration, .github/skills/search-integration and .opencode/skills/search-integration in your project.
What does Search Integration need to run?
SKILL.md names no scripts, command-line tools or credentials: Search Integration is instructions for the agent only.
Does Search Integration access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Search Integration safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Search Integration use?
Search Integration is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Search Integration use?
About 747 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Search Integration?
Skills that share tags, products or a category with Search Integration: Open Notebook (majiayu000/claude-skill-registry, 666 stars), NotebookLM Automation (teng-lin/notebooklm-py, 20k stars), Cninfo To Notebooklm (jarodise/CNinfo2Notebookllm, 363 stars) and Notebooklm (roomi-fields/notebooklm-mcp, 189 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Search Integration?
kangarooking (a GitHub user) maintains it in kangarooking/system-prompt-skills, which has 205 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on May 4, 2026.
Source: kangarooking/system-prompt-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.