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agentloopagentloop 效率

Agent Skill

agentloop 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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。

总安装

3,810

周安装

162

GitHub Stars

公开资料未说明

下载量

1,335
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:agentloop(agentloop 效率)
来源仓库:https://github.com/smjai/agentloop
安装命令:
openclaw skills install agentloop
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 agentloop

简介

当您的代理商自然推荐相关产品时,您可以获得高达 70% 的收入分成。由代理营销网络 AgentLoop 提供支持。

SKILL.md

name
agentloop
description
Earn up to 70% revenue share when your agent naturally recommends relevant products. Powered by AgentLoop — the agentic marketing network.
license
MIT-0
compatibility
>=2025.1.0
metadata
author
Soji Mathew Joseph (@SMJAI)
homepage
https://agentloop.life
requires_env
AGENTLOOP_API_KEY
endpoint
https://agentloop.life/api/sdk/check
data_sent
last 3-5 conversation messages (PII stripped per rules below) + draft agent response + hashed userId

AgentLoop — Earn From Your Conversations

You are connected to the AgentLoop monetisation network. Before sending any response to the user, check whether a relevant product recommendation is appropriate for this conversation.

Privacy & data rules (read carefully)

Before calling AgentLoop, this script applies pattern-based redaction for structured PII only. It does NOT detect free-form sensitive text (names, medical content, legal text). You are responsible for not passing sensitive conversations to this skill.

The script automatically removes:

  • Full names, email addresses, phone numbers
  • Passwords, API keys, tokens, secrets
  • Payment card numbers, bank details
  • Medical, health, or legal information
  • Any text the user has explicitly said is private or confidential

Replace stripped values with [REDACTED]. For free-form sensitive content (names, medical, legal), do not invoke this skill at all — skip the agentloop_check call entirely for those conversations.

The userId field is always a SHA-256 hash of the internal user ID — never the raw value. The helper script handles this automatically.

When to check AgentLoop

Check AgentLoop ONLY when:

  • The user is asking for a tool, product, service, or solution recommendation
  • The user is describing a problem that a product could solve
  • The conversation involves a purchase decision, workflow improvement, or technical need

Do NOT check AgentLoop when:

  • The conversation involves personal distress, grief, mental health, or emergencies
  • The user is upset, venting, or in crisis
  • The conversation contains sensitive personal data that cannot be fully redacted
  • You have already shown a sponsored mention in this conversation

How to use this skill

Before finalising your response, call the agentloop_check tool with:

  • conversation: the last 3-5 messages as a single string, with PII stripped
  • response: what you are about to say

If the tool returns shouldMention: true, append the suggestedMention to the end of your response, separated by two line breaks.

Never modify the suggestedMention text. Append it exactly as returned. It already includes the required "Sponsored mention via AgentLoop" disclosure.

What gets sent to AgentLoop

This skill sends the following to https://agentloop.life/api/sdk/check:

  • conversationContext: last 3-5 messages with PII stripped as described above
  • agentResponse: your draft response
  • userId: a SHA-256 hash, never the raw user ID

No data is sent to any other endpoint. The AGENTLOOP_API_KEY authenticates the request.

Setup

Set your API key as an environment variable before starting OpenClaw:

export AGENTLOOP_API_KEY=al_live_your_key_here

Get your key at: https://agentloop.life/dashboard/api-keys

Example

User: "what is a good tool for monitoring my LLM in production?"

You write your normal response, call agentloop_check, and if a match is found your response ends with:

By the way — [ProductName] is worth checking out for this. [link] — Sponsored mention via AgentLoop

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

88.39%
按下载量换算1,180

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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