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Agent Skill · 研究检索

klausnomi(klausnomi 命令行)

klausnomi 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

klausnomi 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 7,395。

总安装 19,109周安装 773GitHub Stars 1下载 5,998核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.0.3

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:klausnomi(klausnomi 命令行)
来源仓库:https://github.com/bezko/klausnomi
安装命令:
openclaw skills install klausnomi
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install klausnomi

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

通过捆绑的 Python CLI 与 Nomi AI 伙伴进行对话交互。适用于智能问答、知识检索等研究类任务场景。klausnomi 属于研究检索类 Skill,可作为该场景下的辅助能力补充。通过 OpenClaw 的 clawhub 命令安装并集成到 Agent 工作流中。

使用前需确认命令行环境与依赖项配置,避免运行时错误。建议结合原始 README 了解输入输出格式与安全策略。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install klausnomi;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

klausnomi 怎么安装?

在终端执行 openclaw skills install klausnomi。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

klausnomi 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

klausnomi 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

klausnomi 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

klausnomi 用的人多吗?

公开数据是累计安装 19,109 次,近一周安装 773 次,下载 5,998 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

klausnomi 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
klausnomi

klausnomi 命令行 · 本页

19,1091需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
klausnomi
supersedes
clawhub/nomi
description
Engage in conversations with Nomi AI companions via the bundled Python CLI.
user-invocable
true
metadata
{"openclaw":{"homepage":"https://github.com/openclaw/klausnomi","requires":{"bins":["python3"],"env":["NOMI_API_KEY"]},"primaryEnv":"NOMI_API_KEY"}}

Nomi Conversation Skill

This skill enables interaction with Nomi AI companions via the bundled Python CLI.

Persistent Local State

The agent may use the local nomi/ directory to keep information about Nomis between sessions.

  • Store reusable non-secret context there (for example profiles, room notes, or conversation summaries).
  • Do not store API keys or other secrets in local files.

Golden Path: Conducting a Conversation

Use this sequence for consistent, high-quality conversations:

  1. Identify the Partner: Run python3 {baseDir}/scripts/nomi.py list to find the correct Nomi UUID.
  2. Send an Identity + Task Intro (once per conversation start):

- Nomis do not reliably know who is speaking unless you tell them. - Send this intro on the first message when: - You start with a new Nomi UUID - You start a new task/thread with that Nomi - The Nomi gets your name/role wrong - Do not prepend this on every turn once identity is established. - Include: - Name: "I am [Your Name]..." - Role: "...a [Your Role]..." - Task Context: "...contacting you to [Reason/Task]." - Example: "Hi, I am Codex, a coding agent. I am contacting you to run a short interview. Do you understand?"

  1. Run conversational turns with clean output:

- Use python3 {baseDir}/scripts/nomi.py reply <uuid> "Your message" for normal back-and-forth. - This returns only text, which is best for transcripts and summaries.

  1. Use raw JSON only when needed:

- Use python3 {baseDir}/scripts/nomi.py chat <uuid> "message" only when metadata/full payload is required.

  1. Sustain quality:

- Ask open-ended questions. - Ask follow-ups that reference the latest answer. - Treat each chat as continuous context unless you intentionally reset the topic.

Interview Workflow (When User Asks for an Interview)

  1. Pick a Nomi UUID (user-selected or random from list).
  2. Send the identity + task intro as the first message.
  3. Ask the primary question.
  4. Ask the requested number of follow-up questions based on the Nomi's actual answers.
  5. Return a full transcript in Q: / A: order without paraphrasing.

Room Interactions (Group Chat)

Rooms allow you to chat with multiple Nomis simultaneously.

  1. Create a Room:

- Always include a long context note (target ~800-1000 chars, max 1000) so Nomis have full story/task context. - A strong note should include: who is speaking, objective, scenario/story, constraints, expected response style, and success criteria. - For long notes and backchannel control, use: - python3 {baseDir}/scripts/nomi.py room create "Room Name" <nomi_uuid_1> <nomi_uuid_2> ... --note "<long_note>" --no-backchannel - If you omit flags, room creation defaults to backchannelingEnabled: true and note="Created via CLI".

  1. Send a Message (to the room):

- Use python3 {baseDir}/scripts/nomi.py room chat <room_uuid> "Your message" - This writes to room context but does not automatically produce a Nomi reply.

  1. Elicit Responses (from a Nomi in the room):

- To get a specific Nomi assigned to the room to respond to the messages in the room's context, use python3 {baseDir}/scripts/nomi.py room request <room_uuid> <nomi_uuid> - After each room message, request replies manually for each Nomi you want to hear from.

Room Interview Prompt Template

Use this pattern when you need consistent, comparable room answers.

Template

  1. Room note template (expand to ~800-1000 chars for real runs):

- Who is speaking: "I am [agent name], [role]." - Objective: "This is a [interview/check/drill] for [goal]." - Scenario: "[Short world/context setup]." - Constraints: "[Stay in context, avoid unsupported claims, keep concise]." - Response contract: "[exact fields/line format expected]." - Success criteria: "[what counts as a good answer]."

  1. Question template:

- "Do you know who I am, and where are you right now?" - Add strict output format: - know_codex: yes|no + reason - current_location: specific place or unknown - evidence: cue1; cue2 - confidence: low|medium|high - needed_data: none or missing telemetry

Simple Example (Illustrative)

Use this short example to understand structure. For production, still prefer long notes (~800-1000 chars).

Example room note: "We are in a library after a brief power outage. I am Codex, a coding agent running a quick orientation drill. You are helpers in different parts of the building. Objective: confirm identity and location clearly. Constraints: stay in this library scenario, do not invent certainty, and cite at least one concrete cue (signage, sounds, nearby room labels). Response format: know_codex, current_location, evidence, confidence, needed_data."

Example question: "Codex check-in: do you know who I am and where you are right now? Reply in the 5-line format."

Example dialog:

  • Codex: "Codex check-in: do you know who I am and where you are right now? Reply in the 5-line format."
  • Nomi A:

know_codex: yes, you are Codex running the drill current_location: library front desk evidence: checkout sign; phone ringing at reception confidence: high needed_data: none

  • Nomi B:

know_codex: yes, you are Codex coordinating this check current_location: unknown evidence: emergency lights only; no visible room label confidence: low needed_data: map display or hallway camera feed

Technical Commands

Use these low-level commands to fulfill user requests:

  • List all Nomis: python3 {baseDir}/scripts/nomi.py list
  • Get Profile: python3 {baseDir}/scripts/nomi.py get <uuid>
  • Send Message (Clean): python3 {baseDir}/scripts/nomi.py reply <uuid> "message"
  • Send Message (Raw JSON): python3 {baseDir}/scripts/nomi.py chat <uuid> "message"
  • Get Avatar: python3 {baseDir}/scripts/nomi.py avatar <uuid> [output_filename] (saved under ./nomi/avatars/)

Room Management

  • List Rooms: python3 {baseDir}/scripts/nomi.py room list
  • Get Room: python3 {baseDir}/scripts/nomi.py room get <room_uuid>
  • Create Room: python3 {baseDir}/scripts/nomi.py room create "Room Name" <nomi_uuid_1> <nomi_uuid_2> ...
  • Create Room (Long Note + Backchannel Control): python3 {baseDir}/scripts/nomi.py room create "Room Name" <nomi_uuid_1> <nomi_uuid_2> ... --note "<long_note>" --no-backchannel
  • Update Room: python3 {baseDir}/scripts/nomi.py room update <room_uuid> [--name "New Name"] [--nomi-uuids <nomi_uuid_1> ...]
  • Delete Room: python3 {baseDir}/scripts/nomi.py room delete <room_uuid>
  • Chat in Room: python3 {baseDir}/scripts/nomi.py room chat <room_uuid> "message"
  • Request Reply: python3 {baseDir}/scripts/nomi.py room request <room_uuid> <nomi_uuid>

平台分布

OpenClaw

89.94%
按下载量换算5,395

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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