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newsletter-signal-scanner时事通讯信号扫描仪

Agent Skill

newsletter-signal-scanner 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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

225

周安装

9

GitHub Stars

607

下载量

73
CodexClaudeCursorGemini CLI

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

unknown

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:newsletter-signal-scanner(时事通讯信号扫描仪)
来源仓库:https://github.com/athina-ai/goose-skills
仓库路径:skills/newsletter-signal-scanner
安装命令:
npx skills add https://github.com/athina-ai/goose-skills --skill newsletter-signal-scanner
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/athina-ai/goose-skills --skill newsletter-signal-scanner

简介

将订阅的新闻通讯转化为结构化情报流,提取竞争对手提及与市场趋势。

  • 支持按关键词活动分组监控,每周汇总一次高价值片段。
  • 帮助团队提前感知行业风向,优化内容规划与外展时机选择。
  • 需配置 AgentMail 账户与至少一个关键词活动才能启动扫描任务。
  • newsletter-signal-scanner 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Newsletter Signal Scanner

Turn your newsletter subscriptions into a structured intelligence feed. Monitors an AgentMail inbox for incoming newsletters, extracts signal-relevant content by keyword campaign, and delivers a weekly digest of what matters — competitor mentions, ICP pain language, market shifts, and emerging topics.

When to Use

  • "Monitor industry newsletters for competitor mentions"
  • "Alert me when newsletters mention [topic] or [company]"
  • "What are newsletters writing about this week in our space?"
  • "Set up newsletter monitoring for [client]"

Phase 0: Intake

Newsletters to Monitor

  1. Which newsletters should be subscribed to and monitored? (List names or URLs)

- If unknown, ask: "What 3-5 newsletters does your ICP read?" — then use sponsored-newsletter-finder to discover others.

  1. Which AgentMail inbox should receive them? (Or should we create a new one?)

Keyword Campaigns

  1. Competitor names to track (e.g., "Clay", "Apollo", "Outreach")
  2. ICP pain-language terms to track (e.g., "outbound struggling", "pipeline dried up", "SDR ramp")
  3. Market shift terms (e.g., "AI SDR", "agent-led growth", "GTM engineer")
  4. Your brand name (to catch mentions)

Output

  1. Digest delivery: Slack channel, email, or markdown file? (default: markdown file)
  2. Frequency: daily or weekly? (default: weekly)

Save campaign config to clients/<client-name>/configs/newsletter-signals.json.

{
  "inbox_id": "<agentmail_inbox_id>",
  "keyword_campaigns": {
    "competitors": ["Clay", "Apollo", "Outreach", "Salesloft"],
    "pain_language": ["pipeline is down", "outbound isn't working", "SDR ramp"],
    "market_shifts": ["AI SDR", "GTM engineer", "agent-led"],
    "brand_mentions": ["YourCompany", "yourcompany.com"]
  },
  "newsletters": [
    {"name": "Exit Five", "from_domain": "exitfive.com"},
    {"name": "The GTM Newsletter", "from_domain": "gtmnewsletter.com"}
  ],
  "output": {
    "format": "markdown",
    "path": "clients/<client-name>/intelligence/newsletter-signals-[DATE].md"
  }
}

Phase 1: Scan Inbox

Use the agentmail capability to fetch new emails from the monitored inbox:

Fetch emails from inbox <inbox_id> since <last_scan_date>
Filter to: known newsletter senders (match against newsletters config)

For each email:

  • Extract subject, sender, date, full body text
  • Strip HTML → plain text for analysis

Phase 2: Apply Keyword Campaigns

For each newsletter email, scan for keyword matches:

for email in emails:
    matches = {}
    for campaign, keywords in keyword_campaigns.items():
        found = []
        for keyword in keywords:
            if keyword.lower() in email.body.lower():
                # Extract context: 50 chars before + keyword + 50 chars after
                context = extract_context(email.body, keyword)
                found.append({"keyword": keyword, "context": context})
        if found:
            matches[campaign] = found
    email.signal_matches = matches

Only include emails with at least one keyword match in the digest.

Phase 3: Extract Signal Snippets

For each matched email, extract clean signal snippets:

Competitor mention example:

Newsletter: The GTM Newsletter | Date: 2026-03-05 Campaign: competitors Keyword: "Clay" Context: "...teams that use Clay for enrichment are seeing 3x better personalization rates compared to..."

Pain language example:

Newsletter: Exit Five | Date: 2026-03-04 Campaign: pain_language Keyword: "outbound isn't working" Context: "...a lot of founders telling me outbound isn't working the way it used to. The reply rates I'm seeing..."

Phase 4: Output Format

# Newsletter Signal Digest — Week of [DATE]

## Summary
- Newsletters scanned: [N]
- Emails with signals: [N]
- Top trending topic: [topic]

---

## Competitor Mentions

### Clay
- **[Newsletter Name]** — [Date]
  > "[Context snippet]"
  Source: [email subject] | [URL if available]

### [Other Competitor]
...

---

## ICP Pain Language

Signals suggesting your ICP is feeling pain your product solves:

- **[Newsletter Name]** — [Date]
  > "[Context snippet]"
  — Relevance: [why this matters]

---

## Market Shift Signals

Emerging topics gaining newsletter coverage:

- **"[Topic]"** — mentioned in [N] newsletters this week
  > "[Context snippet]"

---

## Your Brand Mentions
[Any mentions of your company or product]

---

## Recommended Actions
1. [Specific action based on signals — e.g., "Exit Five is covering AI SDR fatigue — good moment to publish our take"]
2. [Competitive response if needed]

Save to clients/<client-name>/intelligence/newsletter-signals-[YYYY-MM-DD].md.

Phase 5: Setup — Subscribe to Newsletters

For first-time setup, subscribe the AgentMail address to target newsletters:

  1. Get the AgentMail inbox address (via agentmail capability)
  2. For each newsletter, visit subscription page and submit the AgentMail address
  3. Confirm subscriptions (check inbox for confirmation emails)
  4. Allow 1-2 weeks of accumulation before first full digest

Scheduling

Run weekly (Monday morning recommended):

# Every Monday at 7am — before the team's standup
0 7 * * 1 python3 run_skill.py newsletter-signal-scanner --client <client-name>

Cost

ComponentCost
AgentMail inboxDepends on AgentMail pricing
Email parsing + keyword matchingFree (local logic)
TotalNear-zero ongoing cost

Tools Required

  • AgentMail API — for inbox access
  • Upstream skill: agentmail capability

Trigger Phrases

  • "Scan newsletters for this week's signals"
  • "What are industry newsletters saying about [topic]?"
  • "Run newsletter signal scanner for [client]"
  • "Set up newsletter monitoring"

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Claude

33.58%
按下载量换算25

Codex

32.66%
按下载量换算24

Cursor

18.46%
按下载量换算13

Gemini CLI

9.27%
按下载量换算7

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Snyk

可疑

权限和风险

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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。

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