客户培育销售信活动创建与发送|简诗 AI

SKILL商业运营
商业运营 未标注权限 许可证不明确
安装这个技能

Send email campaigns. Use when: deploying via SendGrid, Klaviyo, Customer.io, Brevo, or Mailgun with A/B testing. 当用户需要处理相关业务场景时使用。

一句话选型结论

按资料字段整理

客户培育销售信活动创建与发送|简诗 AI是商业运营类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类商业运营 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_fccf428b/send-email-campaign
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

商业运营
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_fccf428b仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验2026-11-06上游最后核验时间 · 数据更新 2026-11-06
正文体量13.7 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
写运营方案

按目标产出活动方案与执行清单。

分析数据与指标

从报表里找问题、给结论。

整理客户与销售材料

把客户信息与话术整理成可直接用的材料。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在商业运营场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
客户培育销售信活动创建与发送|简诗 AI当前 未标注权限 未标注 未标注 未标注 有
MX 股票筛选器 联网访问 ClawHub 市场 79.9 万 OpenClaw 有
交易 未标注权限 ClawHub 市场 58.1 万 OpenClaw 有
营销技巧 只读 ClawHub 市场 54.0 万 OpenClaw 有
百灵鸟好 联网访问 GitHub 仓库 52.6 万 Codex、Claude、Cursor 有
股票信息浏览器 接触敏感数据 ClawHub 市场 40.5 万 OpenClaw 有

SKILL.md 全文

13.7 KB
模型实际读到的说明书原文,来自技能仓库

/digital-marketing-pro:send-email-campaign

使用范围与安全规则

本 Skill 只用于“客户培育销售信活动创建与发送”场景,围绕用户当前提供或明确授权处理的材料完成任务。功能目标:Send email campaigns. Use when: deploying via SendGrid, Klaviyo, Customer.io, Brevo, or Mailgun with A/B testing. 当用户需要处理相关业务场景时使用。

  • 默认先在回复中交付分析、方案、草稿、代码建议或检查清单,不主动读取任务范围外的文件、账号和数据。
  • 信息不足时先标记缺口并向用户确认,不用猜测内容填补事实、数字、姓名、结论或权限。
  • 需要联网检索或调用接口时,先说明访问目标、拟发送的数据和用途并取得用户明确同意;只访问用户指定或公开合法来源,不绕过登录、付费墙、验证码、访问控制或平台限制。
  • 涉及发送、发布、上传、删除、支付、投放、部署、同步或其他外部写入时,先展示目标、内容和影响,获得用户对该次动作的明确确认后再执行。
  • 不要求用户在对话里粘贴密码、Cookie、Token、API Key 或私钥;凭据仅用于用户指定的对应官方服务,不回显、不记录、不写入交付物,也不转发到无关地址。
  • 命令和脚本默认作为可审查的参考方案;只有用户明确要求执行、目标路径与影响范围清楚且完成预检后,才运行必要的最小步骤。
  • 营销与增长输出只做合规方案、内容草稿和测试建议,不承诺流量、排名、成交或收益;不刷量、不群发骚扰、不伪造背书,不设计规避平台规则的做法。
  • 涉及客户、员工、联系人或其他个人信息时,只使用完成任务所需的最少字段;默认脱敏,不抓取、拼接、买卖或外传未经授权的个人数据。
  • 交付前复核事实依据、隐私、版权、平台规则和可执行边界;发现高风险或越权请求时停止相关动作,并给出合规替代方案。

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.

Execution gate (MANDATORY — cannot be skipped)

  1. Present the full preview — recipients / spend / changes / compliance — as an Execution Summary before touching any live system.
  2. The user must type yes (or an equivalent explicit approval). ANY other input — ambiguous, implied, partial, or absent approval — cancels the run.
  3. Never proceed on ambiguous input. Never auto-retry a failed execution; a failure needs human review before any re-run.
  4. Record the approval with python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action create-approval --data '{"risk_level":"<tier>","summary":"..."}' before executing, then python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action mark-executed --id {approval_id} after the platform confirms success.

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 (/digital-marketing-pro: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 python "${CLAUDE_PLUGIN_ROOT}/scripts/send-time-optimizer.py" --industry {industry} --audience-type {b2b|b2c|mixed} to suggest a send window by day of week and hour. NOTE: this returns a static industry heuristic (2024-era best-times table) — it does NOT read your historical engagement data. Treat the output as a starting hypothesis and validate it against your own segment's open/click history before relying on it. Factor in the timezone distribution of the recipient list.
  6. Build platform-specific payload: Structure the email payload per the target platform's API requirements — consult skills/context-engine/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: Create the record via approval-manager.py --action create-approval with the tiered risk level inside the --data JSON — {"risk_level":"medium",...} for fewer than 1,000 recipients, "high" for 1,000-10,000, "critical" for more than 10,000. There is no --risk-level flag; see the Execution gate above for the exact command. 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

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常见问题

5 问
答案由本站依据条目字段整理
客户培育销售信活动创建与发送|简诗 AI怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_fccf428b/send-email-campaign。安装方式为未标注。装完刷新技能列表或重开客户端。

客户培育销售信活动创建与发送|简诗 AI支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

客户培育销售信活动创建与发送|简诗 AI权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

客户培育销售信活动创建与发送|简诗 AI收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

客户培育销售信活动创建与发送|简诗 AI用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 2026-11-06。