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Agent Skill · 研究检索

email-smart-reply(电子邮件智能回复)

email-smart-reply 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。

email-smart-reply 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 31,159。

总安装 2,752周安装 117GitHub Stars 公开资料未说明下载 964核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:email-smart-reply(电子邮件智能回复)
来源仓库:https://github.com/cjboy007/email-smart-reply
安装命令:
openclaw skills install email-smart-reply
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 email-smart-reply

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

基于 B2B 场景分析邮件意图并生成针对性回复建议。适用于销售、技术支持等岗位快速响应客户咨询与投诉。可识别询问、交付追踪、投诉等多种意图类型并匹配话术。依赖模型对语义的理解准确性,复杂语境下可能需要人工复核。建议结合企业知识库训练以提高领域适应性。

email-smart-reply 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install email-smart-reply;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

email-smart-reply 怎么安装?

在终端执行 openclaw skills install email-smart-reply。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

email-smart-reply 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

email-smart-reply 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

email-smart-reply 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

email-smart-reply 用的人多吗?

公开数据是累计安装 2,752 次,近一周安装 117 次,下载 964 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

email-smart-reply 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
email-smart-reply

电子邮件智能回复 · 本页

2,752—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
email-smart-reply
description
AI-powered email reply generation for B2B sales. Analyzes incoming emails to detect intent (inquiry, delivery chase, complaint, technical question, partnership, spam), retrieves relevant knowledge base content, generates contextually appropriate draft replies, and routes low-confidence replies for human review via Discord. Use when you need to automate initial email response drafting for sales inquiries.

email-smart-reply

Category: Email Automation Status: Production-Ready Version: 1.0.0 Created: 2026-03-24 Maintainer: WILSON + IRON


Description

Intelligent email auto-reply pipeline for B2B sales. Automatically classifies incoming emails by intent, retrieves relevant knowledge from your knowledge base, generates personalized reply drafts, and routes them through a Discord-based human review workflow before sending.

Pipeline: IMAP fetch → Intent Recognition → KB Retrieval → Reply Generation → Discord Review → SMTP Send

This skill is designed for B2B electronics manufacturing email workflows. It understands product lines (HDMI/DP/USB/LAN cables), customer intent categories specific to electronics manufacturing, and integrates with CRM data.


Core Modules

FilePurpose
scripts/intent-recognition.jsClassifies email intent via LLM (OpenRouter) with keyword fallback
scripts/kb-retrieval.jsRetrieves relevant knowledge from LanceDB + Obsidian vault
scripts/reply-generation.jsGenerates personalized reply drafts using templates + KB context
scripts/discord-review.jsPushes drafts to Discord for human approval before sending
scripts/integration-test.jsEnd-to-end pipeline test with --dry-run mode
config/intent-schema.jsonDefines 6 intent categories with thresholds and behaviors
config/discord-config.jsonDiscord bot token and channel configuration

Intent Categories

Defined in config/intent-schema.json:

IDEnglishChinesePriorityAuto-DraftFallback
inquiryProduct Inquiry产品询价high✅manual_review
delivery-chaseDelivery Follow-up交期催促high✅manual_review
complaintCustomer Complaint客户投诉urgent❌escalate_to_human
technicalTechnical Support技术支持medium✅manual_review
partnershipPartnership/Collaboration合作意向high✅manual_review
spamSpam/Promotional垃圾邮件low❌ignore

Confidence threshold: 0.75 (below this → needs_manual = true, no auto-draft sent)


Usage

Run Full Pipeline (Dry Run)

cd $WORKSPACE/skills/email-smart-reply/scripts
node integration-test.js --dry-run --limit 5

Run Full Pipeline (Live - sends to Discord review)

node integration-test.js --limit 10

Intent Recognition Only

const { recognizeIntent } = require('./scripts/intent-recognition');
const result = await recognizeIntent(emailText);
// Returns: { intent, confidence, method: 'llm'|'keyword' }

KB Retrieval Only

const { retrieveKB } = require('./scripts/kb-retrieval');
const results = await retrieveKB({ intent, emailText });
// Returns: { found, results: [{source, content}], queries }

Generate Reply Draft

const { generateReply } = require('./scripts/reply-generation');
const draft = await generateReply({ email, intentResult, kbResults });
// Returns: { draft_id, subject, body, needs_manual, reason } or null
// Draft saved to: $WORKSPACE/skills/imap-smtp-email/drafts/

Push to Discord Review

const { pushToDiscordReview } = require('./scripts/discord-review');
await pushToDiscordReview({ draft, email, intentResult });
// Sends embed with Approve/Edit/Discard buttons to #email-review channel

Discord Review CLI (manual actions)

node scripts/discord-review.js test          # Send test embed
node scripts/discord-review.js approve <draft_id>
node scripts/discord-review.js discard <draft_id>

Draft ID Format

DRAFT-{timestamp}-{3-letter-prefix}

IntentPrefix
inquiryINQ
delivery-chaseDEL
complaintCOM
technicalTEC
partnershipPAR
spam(filtered, no draft)

Dependencies

External Services

  • IMAP/SMTP: Configured email account via enterprise mail provider
  • OpenRouter API: LLM intent classification (API key in .env)
  • Discord Bot: Token + channel (configured in config/discord-config.json)

Local Skills/Tools

  • $WORKSPACE/skills/imap-smtp-email/ — IMAP/SMTP transport layer
  • $WORKSPACE/vector_store/okki_vector_search_v3.py — LanceDB vector search
  • $KB_PATH — Product knowledge base (Obsidian vault)

Node.js Packages

  • imap / nodemailer — email transport (inherited from imap-smtp-email skill)
  • node-fetch — OpenRouter API calls
  • discord.js — Discord bot integration

Configuration

config/intent-schema.json

  • Intent definitions, keywords (EN + ZH), confidence thresholds
  • Fallback behaviors per intent type
  • Global settings (multi-intent handling, language detection)

config/discord-config.json

  • bot_token: Discord bot token
  • channel_id: Target channel for review embeds (<your-discord-channel-id>)
  • review_timeout_minutes: Auto-discard timeout (default: 30)

Safety Guarantees

  1. No blind sending: All drafts require human approval via Discord before SMTP send
  2. Low confidence → manual: Confidence < 0.75 sets needs_manual=true, skips Discord push, queues for manual review
  3. Complaint escalation: Complaint intent never auto-drafts; always escalates to human
  4. Spam filtering: Spam intent immediately discarded, no draft created
  5. Dry-run mode: --dry-run flag for safe testing without real sends or Discord posts
  6. Fallback degradation: LLM unavailable → keyword matching; IMAP unavailable → sample emails

Development History

Task: task-001 | Phase: 1 | Iterations: 5 | Duration: ~2.5 hours

IterationAgentWhat Was Built
1IRONInitial attempt (timed out at 300s — restructured to single-subtask iterations)
2IRONSteps 1-3: intent-schema.json, intent-recognition.js, kb-retrieval.js
3IRONStep 4: reply-generation.js (templates, escalation logic, draft file I/O)
4IRONStep 5: discord-review.js (Embed format, 3-button interaction, CLI fallback)
5IRONStep 6: integration-test.js (full pipeline, --dry-run, test-results/ output)

Key Design Decisions:

  • Single-subtask-per-iteration strategy after initial timeout failure
  • LLM → keyword cascade for intent recognition robustness
  • Discord embed review (not email approval) for fast human-in-the-loop UX
  • needs_manual flag as primary safety gate (not confidence threshold alone)
  • Reviews stored locally in reviews-pending/ as fallback if Discord is unavailable

Known Limitations (Phase 1):

  • Integration tests use sample emails (real IMAP auth was unavailable in test env)
  • LLM intent classification falls back to keyword matching (confidence ~0.4–0.6)
  • Discord live push not tested in dry-run (separately verified in Iteration 4)

Phase 2 Roadmap

  1. Real IMAP testing — Run pipeline against actual incoming emails, measure intent accuracy
  2. LLM availability — Ensure OpenRouter API accessible in production
  3. Discord Bot permissions — Confirm bot has send access to #email-review channel
  4. Cron job — Schedule integration-test.js every 30 minutes via cron
  5. Manual queue monitoring — Alert when needs_manual backlog exceeds threshold

File Structure

email-smart-reply/
├── SKILL.md                    ← This file
├── README.md                   ← Quick start guide
├── scripts/
│   ├── intent-recognition.js   ← LLM + keyword intent classifier
│   ├── kb-retrieval.js         ← LanceDB + Obsidian knowledge retrieval
│   ├── reply-generation.js     ← Template-based reply drafts
│   ├── discord-review.js       ← Discord embed review workflow
│   └── integration-test.js     ← End-to-end pipeline runner
├── config/
│   ├── intent-schema.json      ← Intent categories and thresholds
│   └── discord-config.json     ← Discord bot configuration
└── drafts/                     ← Generated draft replies (gitignored)

Environment Variables

Create a .env file in the skill root:

# LLM API
OPENROUTER_API_KEY=your-openrouter-api-key

# Knowledge Base Path (e.g. Obsidian vault)
KB_PATH=/path/to/your/knowledge-base

# Draft output directory
DRAFTS_DIR=./drafts

# Discord Review (optional)
DISCORD_BOT_TOKEN=your-discord-bot-token
DISCORD_REVIEW_CHANNEL_ID=your-channel-id

# IMAP (to read incoming emails)
IMAP_HOST=imap.your-provider.com
IMAP_PORT=993
IMAP_USER=your@email.com
IMAP_PASS=your-password

Source

This skill is a packaged, documented, reusable version of the email automation pipeline.

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

平台分布

OpenClaw

97.35%
按下载量换算938

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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