/install newsletter-audience-intelligence
Newsletter Audience Intelligence
Turn subscriber, reply, survey, source, and click data into sponsor-ready audience proof and operating decisions.
Core Rule
Use connected analytics, subscriber data, source attribution, survey answers, replies, sponsor history, and issue history when available. Do not invent audience metrics, demographics, reader quotes, locations, job titles, or intent signals.
Inputs
- Newsletter name, category, audience, geography or market
- Current subscriber count, opens, clicks, replies, source attribution, and survey data if available
- Reader segments or tags already used
- Sponsor, paid conversion, growth, or content-quality goal
- Known acquisition sources: Meta, Reddit, X, LinkedIn, TikTok, referrals, swaps, events, search, or organic
- Connected workspace, analytics export, or manual data if available
Workflow
- Separate known data from assumptions and missing data.
- Identify useful audience segments by source, geography, role, interest, behavior, lifecycle, or purchase intent.
- Compare segment quality using engagement, clicks, replies, conversions, and sponsor fit.
- Extract sponsor-proof bullets without overclaiming attribution.
- Flag weak or missing proof needed before selling sponsors or scaling acquisition.
- Recommend 3-7 survey, onboarding, reply, or tagging improvements.
- Save or hand off audience insights, segment notes, and next data collection steps for the connected workspace.
Output Format
When a reusable artifact is useful, follow templates/audience-proof.md.
Include:
- Audience-quality summary
- Known data vs missing data
- Segment table
- Sponsor-proof bullets
- Weak proof or risk notes
- Data collection plan
- Connected-workspace handoff notes
Segment table columns:
| Segment | Evidence | Value to operator | Sponsor relevance | Confidence | Next data to collect |
|---|
Guardrails
- Do not infer demographics from stereotypes or category assumptions.
- Do not treat subscriber count as sponsor proof by itself.
- Keep exact metrics separate from qualitative signals.
- If data is thin, output a collection plan instead of pretending there is a strong audience story.
- Do not export, delete, tag, or modify subscribers without explicit approval and an available workspace/API tool.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install newsletter-audience-intelligence - 安装完成后,直接呼叫该 Skill 的名称或使用
/newsletter-audience-intelligence触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Newsletter Audience Intelligence 是什么?
Use when the user asks to understand newsletter audience quality, segment readers, summarize survey/reply/click data, prove sponsor value, create media-kit p... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 40 次。
如何安装 Newsletter Audience Intelligence?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install newsletter-audience-intelligence」即可一键安装,无需额外配置。
Newsletter Audience Intelligence 是免费的吗?
是的,Newsletter Audience Intelligence 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Newsletter Audience Intelligence 支持哪些平台?
Newsletter Audience Intelligence 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Newsletter Audience Intelligence?
由 Dmitriy(@freeman14)开发并维护,当前版本 v1.0.0。