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send-email-campaign发送电子邮件活动

Agent Skill

send-email-campaign 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

618

周安装

26

GitHub Stars

66

下载量

216
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:send-email-campaign(发送电子邮件活动)
来源仓库:https://github.com/indranilbanerjee/digital-marketing-pro
仓库路径:skills/send-email-campaign
安装命令:
npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill send-email-campaign
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/indranilbanerjee/digital-marketing-pro --skill send-email-campaign

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在需要围绕仓库状态、代码变更或协作事项进行整理时使用。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装方式:通过 npx skills add 命令从指定 GitHub 仓库添加。
  • 注意权限范围和维护状态,确认是否会触发联网、命令执行或文件读写。

SKILL.md

/dm:send-email-campaign

Purpose

Create and send a targeted email campaign through the brand's connected email platform with personalization, A/B subject lines, compliance checks, and deliverability monitoring. Handles the full lifecycle from content validation through send execution to post-send monitoring, with tiered risk controls based on recipient list size. Ensures every send passes spam, compliance, and brand voice gates before reaching any inbox.

Input Required

The user must provide (or will be prompted for):

  • Email content: Subject line, preview text (40-90 chars), body copy with HTML structure, and primary CTA — or a draft to refine
  • Target list or segment: The recipient list name, segment ID, or audience criteria for the send — with confirmation of list hygiene status (last cleaned date)
  • Email platform: Which email service to use — SendGrid, Klaviyo, Customer.io, Brevo, or Mailgun — must have the corresponding MCP server connected
  • Personalization fields: Dynamic fields to personalize — first name, company, product interest, last purchase, location, or custom merge tags with fallback defaults for missing data
  • A/B variants: Optional — 2-3 subject line or content variants for split testing with desired test percentage (10-50%), test duration, and winning metric (open rate or click rate)
  • Send time: Immediate send, scheduled date and time with timezone, or "optimal" to use send-time optimization based on historical engagement data per segment
  • Reply-to address: Reply-to email address if different from the default sender configured in the platform
  • Sender name and from address: Display name and from address — must match authenticated sending domain (SPF, DKIM, DMARC)
  • Unsubscribe handling: Confirm unsubscribe link placement, one-click unsubscribe header compliance (required for bulk senders per Gmail/Yahoo 2024 rules), and preference center link
  • UTM tracking: Google Analytics UTM parameters for all links in the email body (source, medium, campaign), or auto-generate based on brand naming conventions
  • Suppression list: Any additional email addresses or domains to exclude from this send beyond the platform's global suppression list
  • Email template: Optional — platform template ID to use, or build from scratch with the provided content and brand styling
  • Preheader text strategy: Whether the preview text should complement, tease, or extend the subject line — affects how the email appears in inbox list view
  • Fallback content: Plain-text version of the email for recipients whose clients do not render HTML, or auto-generate from the HTML body

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/dm:brand-setup)?" — or proceed with defaults.
  2. Verify email platform connection: Check which email MCP server is connected and confirm it matches the user's target platform. Verify the sending domain is authenticated (SPF, DKIM, DMARC records). If not connected or not authenticated, instruct the user on setup steps.
  3. Score email subject lines: Run email-subject-tester.py on all subject line variants to evaluate length (optimal 30-50 chars), power words, personalization token effectiveness, emoji usage, and predicted open rate. Recommend improvements if any variant scores below threshold.
  4. Check spam score: Run spam-score-checker.py to analyze subject lines and body content for spam trigger words, excessive capitalization, exclamation marks, link-to-text ratio, image-to-text ratio, and authentication alignment. Flag any deliverability risks with specific remediation steps.
  5. Optimize send time: If the user selected "optimal" timing, run send-time-optimizer.py with historical engagement data to determine the best send window by day of week and hour for the target segment. Factor in timezone distribution of the recipient list.
  6. Build platform-specific payload: Structure the email payload per the target platform's API requirements — consult platform-publishing-specs.md for field mappings, template rendering, merge tag syntax (e.g., {{first_name}} vs {first_name}), A/B test configuration parameters, and scheduling API format.
  7. Verify list size and consent compliance: Confirm recipient count and segment definition. Check that the list has proper opt-in consent flags for the applicable jurisdiction. Verify unsubscribe mechanism is functional, one-click unsubscribe header is present, physical mailing address is included, and compliance with CAN-SPAM (US), GDPR (EU), CASL (Canada), and any other regulations for the brand's target markets.
  8. Score brand voice: Run brand-voice-scorer.py on the email body content to verify alignment with brand tone and messaging guidelines. Flag any copy that deviates from brand standards.
  9. Create approval record: Run approval-manager.py with tiered risk levels — medium for fewer than 1,000 recipients, high for 1,000-10,000, critical for more than 10,000. Generate a send summary with all campaign details, scores, and compliance status.
  10. Present campaign summary: Display the complete summary for user review — subject lines with scores, preview text, recipient count and segment name, send time, personalization preview with sample recipient data, spam score, brand voice score, and compliance checklist. Wait for explicit confirmation.
  11. Send test email: On initial approval, send a test email to the user's address (and any additional test addresses) via the MCP server. Ask the user to confirm the test renders correctly across desktop and mobile, personalization tokens resolve, links work, and images load.
  12. Execute full send via MCP: After test confirmation, trigger the campaign send through the connected email platform MCP. Handle A/B test split configuration, scheduling, and any platform-specific send options (track opens, track clicks, Google Analytics UTM tagging).
  13. Monitor deliverability: After send, poll the platform API at 15-minute intervals for the first hour to track delivery metrics — bounce rate, delivery rate, soft bounces, hard bounces, and spam complaints. Alert the user if bounce rate exceeds 3% or spam complaint rate exceeds 0.1%.
  14. Capture early engagement signals: After 1 hour and again at 4 hours, pull open rate and click rate data. Compare against the brand's historical averages for the same segment. If A/B testing, report which variant is leading.
  15. Log execution: Run execution-tracker.py to log the send event with timestamp, platform, campaign ID, list size, subject lines, A/B configuration, send time, initial delivery metrics, and compliance verification status. Save an insight about subject line performance for future email strategy optimization.

Output

A structured send confirmation containing:

  • Send confirmation: Campaign ID, platform, send status (sent, scheduled, or A/B testing), and timestamp with timezone
  • List details: Recipient count, segment name, consent verification status, and list hygiene notes
  • Subject line scores: Score breakdown for each variant — length, power words, personalization effectiveness, predicted open rate, and spam risk indicators
  • Spam score report: Overall deliverability risk rating (low/medium/high) with specific flags for any triggered spam indicators and remediation steps
  • Brand voice score: Email content alignment score with notes on tone consistency and any copy adjustments recommended
  • Send time: Actual send time with rationale — user-specified, scheduled with timezone, or optimized with supporting engagement data
  • A/B test configuration: If applicable — variant descriptions, split percentage, test duration, winning metric, and auto-send winner settings
  • Deliverability report: Initial delivery rate, bounce rate (hard and soft), spam complaint rate, and comparison against industry benchmarks for the brand's sector
  • Compliance checklist: Pass/fail for CAN-SPAM, GDPR, CASL, unsubscribe mechanism, one-click unsubscribe header, physical address, authentication headers (SPF, DKIM, DMARC), and sender identity
  • Early engagement signals: 1-hour and 4-hour open rate and click rate snapshots with comparison to brand historical averages and industry benchmarks
  • Personalization preview: Sample rendering showing how the email appears for 2-3 representative recipients with different merge tag values and fallback defaults
  • UTM tracking summary: Complete UTM parameters applied to all email links for attribution tracking in the brand's analytics platform
  • Execution log entry: Timestamped record of the send action with all campaign metadata for audit trail and performance benchmarking

Agents Used

  • email-specialist — Subject line optimization, content personalization strategy, deliverability analysis, spam scoring, send time optimization, compliance verification, brand voice scoring, A/B test design with statistical significance thresholds, and preheader text strategy
  • execution-coordinator — Approval workflow with tiered risk controls based on list size, test send coordination across desktop and mobile, platform API execution, deliverability monitoring with real-time alerting, early engagement signal capture, and execution logging with insight archival

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

Codex

35.59%
按下载量换算77

Claude

30.81%
按下载量换算67

Cursor

16.32%
按下载量换算35

Gemini CLI

9.42%
按下载量换算20

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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