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openclaw-marketing-osOpenClaw marketing OS 搜索

Agent Skill

openclaw-marketing-os 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。

总安装

4,986

周安装

212

GitHub Stars

公开资料未说明

下载量

1,747
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:openclaw-marketing-os(OpenClaw marketing OS 搜索)
来源仓库:https://github.com/x-rayluan/openclaw-marketing-os
安装命令:
openclaw skills install openclaw-marketing-os
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 openclaw-marketing-os

简介

openclaw-marketing-os 用于运行或优化基于 OpenClaw 的多代理营销操作系统。

  • 适合构建端到端的自动化营销工作流和团队协作引擎。
  • 支持多代理协同执行广告投放、内容分发和客户跟进等任务。
  • 安装命令为 openclaw skills install openclaw-marketing-os,需配置代理角色和任务分配。
  • 注意任务调度冲突和资源竞争问题,确保各模块权限隔离。

SKILL.md

name
openclaw-marketing-os
description
Run, audit, or improve an OpenClaw/ClawLite-style multi-agent marketing operating system. Use when the task is to operate or package a full marketing engine across Hunter research intel, marketing-assets synchronization, JK content packaging, Elon social publishing, Tony blog publishing, Jenny lifecycle/activation, Peter blog QA, Karen truth-gate QA, Mission Control state management, and same-day delivery/receipt discipline. Also use when open-sourcing or documenting the system as a reusable growth operating system for AI-agent teams.

OpenClaw Marketing OS

This skill packages the ClawLite/OpenClaw AI marketing team into a reusable operating system.

What this skill is for

Use this skill when the goal is not “write one post” or “draft one blog.” Use it when the real task is to run or improve a daily AI marketing machine with handoffs, receipts, truth states, and conversion accountability.

This system assumes a multi-lane team:

  • Hunter — community intel and X/Reddit learning
  • JK — same-day content packaging / writing handoff
  • Elon — social publishing
  • Tony — blog publishing
  • Jenny — lifecycle / activation email
  • Peter — blog QA / closeout
  • Karen — truth gate / QA
  • Mission Control — state mirror and receipts

Core rule

The team is not complete when content exists. The team is complete only when:

  • upstream research exists
  • durable marketing-assets are updated
  • downstream publishing is executed
  • URLs / visibility / QA receipts exist
  • Mission Control truth matches reality

System lanes

1. Hunter — upstream intelligence

Hunter owns:

  • Reddit pain scan
  • X pain scan
  • X viral-learning scan
  • Pain Map
  • selection layer
  • Intel Pack
  • Mission Control mirror
  • marketing-assets sync receipt

Hunter is incomplete if research stays trapped in notes.

2. JK — packaging layer

JK converts same-day inputs into cleaner content substrate for downstream lanes. JK should consume:

  • same-day Hunter intel
  • company positioning
  • marketing-assets hooks / angles / proof / CTA

3. Elon — social lane

Elon owns publication, not just drafting. For each assigned lane/platform, require:

  • same-day draft
  • ASSET_CHECK
  • post URL
  • visibility proof
  • acceptance receipt

X should preferentially run deep threads, not shallow one-liners.

4. Tony — blog lane

Tony is keyword-first and publish-first. Tony is not complete on “drafts.” Tony is complete only when the day’s blog target is actually published and verifiable.

Default rule in this system:

  • Tony daily target = 12 blog publishes
  • fewer than target must be called out as an explicit gap

5. Jenny — activation / lifecycle lane

Jenny owns:

  • cohort selection
  • send execution
  • accepted send proof
  • writeback/accounting
  • same-day ASSET_CHECK

Do not confuse “send attempted” with “delivery complete.”

6. Peter — blog QA lane

Peter closes the Tony lane by verifying live/public reality. Peter PASS requires:

  • correct live URL
  • real browser/public verification
  • clean QA receipt

7. Karen — truth gate

Karen does not create marketing. Karen verifies whether claimed completion is true.

8. Mission Control — truth mirror

Mission Control must reflect:

  • current same-day truth
  • partial completion vs real completion
  • blockers without optimism inflation

Required operating principles

Asset-layer rule

Hunter learnings must be normalized into durable marketing-assets before the rest of the system can scale cleanly.

Receipt rule

Every lane leaves a dated receipt. No receipt = no completion.

ASSET_CHECK rule

All ClawLite/OpenClaw-facing growth/content lanes should leave ASSET_CHECK-backed evidence. Missing ASSET_CHECK means incomplete truth.

Truth-state rule

Use explicit states such as:

  • DELIVERED
  • PASS
  • EXECUTED_BUT_BLOCKED
  • BLOCKED_BUT_COMPLIANT
  • AT_RISK
  • SAME_DAY_UNRESOLVED_GAP

Do not collapse partial progress into fake completion.

Demo workflow (layered operating flow)

1. Intelligence layer

Hunter runs:

  • Reddit + X research
  • Pain Map
  • selection layer
  • Intel Pack
  • X viral-learning loop
  • marketing-assets sync

2. Packaging layer

JK converts same-day research and durable assets into a cleaner writing substrate for execution lanes.

3. Execution layer

Parallel lanes operate from the same-day packaged inputs:

  • Elon → social publishing
  • Tony → blog publishing
  • Jenny → lifecycle / activation

4. QA / truth layer

  • Peter verifies live/public blog reality
  • Karen checks whether claimed completion matches evidence

5. State closure layer

Mission Control records the verified same-day state:

  • PASS / FAIL / BLOCKED
  • blockers
  • make-up work
  • anti-optimism truth mirror

Recommended execution order

  1. Hunter research + X viral-learning
  2. Pain Map + selection layer
  3. marketing-assets sync
  4. JK package if used
  5. Elon social publishing
  6. Tony blog publishing
  7. Jenny activation / lifecycle
  8. Peter closeout
  9. Karen truth gate
  10. Mission Control refresh

Open-source packaging guidance

When packaging this system as a reusable public skill:

  • preserve the operating model, not private data
  • keep role definitions, receipts, QA logic, and handoff discipline
  • remove private secrets, private campaign data, and local-only credentials
  • point users to configurable references rather than hard-coded company paths when needed

Read next when needed

  • references/system-map.md
  • references/role-contracts.md
  • references/daily-loop.md
  • references/open-source-packaging.md

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

74.48%
按下载量换算1,301

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。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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