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badgebotbadgebot 搜索

Agent Skill

badgebot 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。

总安装

2,645

周安装

107

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下载量

830
OpenClaw

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

MIT-0

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:badgebot(badgebot 搜索)
来源仓库:https://github.com/mchensf/badgebot
安装命令:
openclaw skills install badgebot
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。

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openclaw skills install badgebot

简介

badgebot 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。

  • 适用于 Slack 渠道潜在客户名片信息整理场景。
  • 可自动提取图像/文本线索并通过 Apollo.io 丰富数据后同步至 HubSpot。
  • 安装命令:openclaw skills install badgebot,需确认 Slack API 与第三方 CRM 接入权限。
  • 使用前建议核对数据抓取频率是否符合平台速率限制。

SKILL.md

name
slack-lead-scanner
description
Monitor a Slack channel for images/text of leads (badges, business cards), extract data, enrich with Apollo.io API, let Apollo auto-sync to HubSpot, search HubSpot for the new contact link, and reply in-thread with summary and link. Also monitors Slack DM replies to update HubSpot fields on existing leads. Use when setting up or managing Slack-based lead scanning, including polling configuration via cron.

Slack Lead Scanner

Monitor #leadscanner for new images/text, process as leads via Apollo + HubSpot, reply in-thread. Also check Slack DM replies to update HubSpot contacts.

Setup

  • SLACK_TOKEN in ~/.openclaw/credentials/slack-bot-token (xoxb- token)
  • Apollo key in ~/.openclaw/credentials/apollo-api-key
  • HubSpot token in ~/.openclaw/credentials/hubspot-api-key
  • State file: ~/clawd/memory/slack-lead-scanner-state.json
  • Portal ID: 43856876
  • Channel: #leadscanner (C0AQAJ8VD8A)

State File Schema

{
  "last_ts": 1234567890,
  "processed_ids": ["msg_ts_1", "msg_ts_2"],
  "channel_id": "C0AQAJ8VD8A",
  "pending_dm_replies": [
    {
      "dm_ts": "1234567890.123456",
      "hubspot_contact_id": "12345",
      "contact_name": "John Doe"
    }
  ]
}

Polling Workflow

Part 1: Process New #leadscanner Messages

  1. Load state from ~/clawd/memory/slack-lead-scanner-state.json
  2. Fetch messages: curl -H "Authorization: Bearer $SLACK_TOKEN" "https://slack.com/api/conversations.history?channel=C0AQAJ8VD8A&oldest=[last_ts]&limit=50"
  3. Filter: ts > last_ts, not in processed_ids, has files or non-empty text
  4. For each new message:

a. Download image: curl -H "Authorization: Bearer $SLACK_TOKEN" "[file.url_private_download]" -o /tmp/lead-[ts].jpg b. Analyze with image tool: "Extract structured data from this badge or business card. Return JSON: {first_name, last_name, company, title, email, phone, notes}" c. Append message.text to notes d. Dedup: Search HubSpot first by firstname+lastname+company — if exists, skip enrich, go to step (g) e. Enrich via Apollo: curl -X POST -H "Content-Type: application/json" -d '{"api_key":"[KEY]","first_name":"[f]","last_name":"[l]","organization_name":"[co]","title":"[t]","reveal_personal_emails":true,"reveal_phone_number":true}' https://api.apollo.io/api/v1/people/match f. Sleep 10s, then search HubSpot for new contact (sort by createdate DESC, filter name+company) g. Get contact id, build link: https://app.hubspot.com/contacts/43856876/contact/[id] h. Send DM to Mark (user:U03H7C6HW5B) with formatted message (see Reply Format below) i. Track DM timestamp from response, add to pending_dm_replies in state j. React ✅ on original #leadscanner message k. Add msg ts to processed_ids, update last_ts l. Cleanup: rm /tmp/lead-[ts].jpg

Part 2: Check DM Replies for HubSpot Updates

  1. For each entry in pending_dm_replies:

a. Fetch thread replies: curl -H "Authorization: Bearer $SLACK_TOKEN" "https://slack.com/api/conversations.replies?channel=[DM_CHANNEL_ID]&ts=[dm_ts]" b. Filter replies that are from the user (not the bot itself) and not yet processed c. Parse reply text as a natural-language HubSpot update instruction, e.g.: - "update title to VP of Sales" → jobtitle - "wrong email, it's john@acme.com" → email - "company is Acme Corp" → company - "phone is 415-555-1234" → phone - "add note: met at SaaStr" → note (create HubSpot note via /crm/v3/objects/notes) d. Apply update via HubSpot PATCH: curl -X PATCH -H "Authorization: Bearer $HUBSPOT_TOKEN" -H "Content-Type: application/json" -d '{"properties":{"[field]":"[value]"}}' "https://api.hubapi.com/crm/v3/objects/contacts/[hubspot_contact_id]" e. Reply in thread: "✅ Updated [field] to [value] for [contact_name]." f. If unrecognized instruction, reply: "❓ I didn't understand that. Try: 'update title to [value]' or 'email is [value]'"

  1. Write updated state back to ~/clawd/memory/slack-lead-scanner-state.json
  2. Log to memory/YYYY-MM-DD.md

Reply Format (DM to Mark)

Use Slack line breaks (\ ) between fields:

*[Full Name]*
[Title]
[Company]
📧 [Email]
📞 [Phone]
🔗 <https://app.hubspot.com/contacts/43856876/contact/[id]|View in HubSpot>

_Reply to this message to update any field in HubSpot._

HubSpot Field Mappings

Natural languageHubSpot property
name / first name / last namefirstname / lastname
title / job title / rolejobtitle
company / orgcompany
emailemail
phonephone
note / notescreate note object

Error Handling

  • No Apollo match: send DM anyway with extracted data, note "No enrichment match"
  • No HubSpot match after sleep: send DM with "Sync pending - check HubSpot later", omit link
  • API errors: Log and DM "Error processing: [msg]" with ❌ react
  • Unknown DM reply: Reply with ❓ and guidance

Notes

  • Use haiku model for cost efficiency
  • Rate limits: Slack ~1 call/sec, Apollo per plan, HubSpot 100 req/10s
  • Never log or output tokens
  • Get DM channel ID for direct messages: it's a conversations.open response for user U03H7C6HW5B

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

94.63%
按下载量换算785

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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