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Agent Skill · 研究检索

ironclaw-lead-enrichment(铁爪铅浓缩)

ironclaw-lead-enrichment 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。

ironclaw-lead-enrichment 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 7,242。

总安装 19,560周安装 799GitHub Stars 公开资料未说明下载 6,328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:ironclaw-lead-enrichment(铁爪铅浓缩)
来源仓库:https://github.com/aspenas/ironclaw-lead-enrichment
安装命令:
openclaw skills install ironclaw-lead-enrichment
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 ironclaw-lead-enrichment

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。

用于丰富联系人和潜在客户的基础信息。整合 LinkedIn 资料、邮箱和公司教育数据。提升 CRM 系统中客户画像完整性。需用户提供待处理联系人列表。ironclaw-lead-enrichment 属于研究检索类 Skill,可作为该场景下的辅助能力补充。涉及隐私数据时应遵守 GDPR 等法规。

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 ironclaw-lead-enrichment;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

    它需要需要联网,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

ironclaw-lead-enrichment 怎么安装?

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

ironclaw-lead-enrichment 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。

ironclaw-lead-enrichment 需要什么权限?

权限级别是需要联网。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。

ironclaw-lead-enrichment 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。

ironclaw-lead-enrichment 用的人多吗?

公开数据是累计安装 19,560 次,近一周安装 799 次,下载 6,328 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。

ironclaw-lead-enrichment 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
ironclaw-lead-enrichment

铁爪铅浓缩 · 本页

19,560—需要联网MIT-0通过

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
lead-enrichment
description
Enrich contact and lead records with LinkedIn profiles, email addresses, company data, and education info. Use when asked to "enrich contacts", "fill in missing data", "find emails for leads", "complete lead profiles", "look up company info", or any bulk data completion task for CRM records.
metadata
{ "openclaw": { "emoji": "✨" } }

Lead Enrichment — Multi-Source Data Completion

Enrich CRM contact records by filling missing fields from multiple sources. Works with DuckDB workspace entries or standalone JSON data.

Sources (Priority Order)

  1. LinkedIn (via linkedin-scraper skill) — name, title, company, education, connections
  2. Web Search (via web_search tool) — email patterns, company info, social profiles
  3. Company Website (via web_fetch) — team pages, about pages, contact info
  4. Email Pattern Discovery — derive email from name + company domain

Enrichment Pipeline

Step 1: Assess What's Missing

-- Query the target object to find gaps
SELECT "Name", "Email", "LinkedIn URL", "Company", "Title", "Location"
FROM v_leads
WHERE "Email" IS NULL OR "LinkedIn URL" IS NULL OR "Title" IS NULL;

Step 2: Prioritize by Value

  • High priority: Missing email (needed for outreach)
  • Medium priority: Missing title/company (needed for personalization)
  • Low priority: Missing education, connections count, about text

Step 3: Enrich Per Record

For each record with gaps:

If LinkedIn URL is known but other fields missing:
  1. Use linkedin-scraper to visit profile
  2. Extract: title, company, location, education, about
  3. Update DuckDB record
If LinkedIn URL is missing:
  1. Search LinkedIn: {name} {company} or {name} {title}
  2. Verify match (name + company alignment)
  3. Store LinkedIn URL, then scrape full profile
If Email is missing:
  1. Find company domain (web search or LinkedIn company page)
  2. Try common patterns:

- first@domain.com - first.last@domain.com - flast@domain.com - firstl@domain.com

  1. Optionally verify with web search: "email" "{name}" site:{domain}
  2. Check company team/about page for email format clues
If Company info is missing:
  1. Web search: "{name}" "{title}" or check LinkedIn
  2. Fetch company website for: industry, size, description, funding

Step 4: Update Records

-- Update via DuckDB pivot view
UPDATE v_leads SET
  "Email" = ?,
  "LinkedIn URL" = ?,
  "Title" = ?,
  "Company" = ?,
  "Location" = ?
WHERE id = ?;

Bulk Enrichment Mode

For enriching many records at once:

  1. Query all incomplete records from DuckDB
  2. Group by company (scrape company once, apply to all employees)
  3. Process in batches of 10-20 records
  4. Report progress after each batch:
   Enrichment Progress: 45/120 leads (38%)
   ├── Emails found: 32/45 (71%)
   ├── LinkedIn matched: 41/45 (91%)
   ├── Titles updated: 38/45 (84%)
   └── ETA: ~15 min remaining
  1. Save checkpoint after each batch (in case of interruption)

Enrichment Quality Rules

  • Confidence scoring: Mark each enriched field with confidence (high/medium/low)

- High: Direct match from LinkedIn profile or company website - Medium: Inferred from patterns (email format) or partial match - Low: Best guess from web search results

  • Never overwrite existing data unless explicitly asked
  • Flag conflicts: If enriched data contradicts existing data, flag for review
  • Dedup check: Before inserting LinkedIn URL, check it's not already assigned to another contact

Email Pattern Discovery

Common corporate email formats by frequency:

  1. first.last@domain.com (most common, ~45%)
  2. first@domain.com (~20%)
  3. flast@domain.com (~15%)
  4. firstl@domain.com (~10%)
  5. first_last@domain.com (~5%)
  6. last.first@domain.com (~3%)
  7. first.l@domain.com (~2%)

Strategy:

  1. If you know one person's email at the company, derive the pattern
  2. Search web for "@{domain}" email format
  3. Check company team page source code for mailto: links
  4. Use the most common pattern as fallback

Output

After enrichment, provide a summary:

Enrichment Complete: 120 leads processed
├── Emails: 94 found (78%), 26 still missing
├── LinkedIn: 108 matched (90%), 12 not found
├── Titles: 115 updated (96%)
├── Companies: 118 confirmed (98%)
├── Locations: 89 found (74%)
└── Avg confidence: High (82%), Medium (14%), Low (4%)

Top gaps remaining:
- 26 leads missing email (mostly small/stealth companies)
- 12 leads missing LinkedIn (common names, ambiguous matches)

DuckDB Field Mapping

Standard field names for Ironclaw CRM objects:

Enrichment DataDuckDB FieldType
Full nameNametext
Email addressEmailemail
LinkedIn URLLinkedIn URLurl
Job titleTitletext
Company nameCompanytext / relation
LocationLocationtext
EducationEducationtext
PhonePhonephone
Company sizeCompany Sizetext
IndustryIndustrytext
Enrichment dateEnriched Atdate
ConfidenceEnrichment Confidenceenum (high/medium/low)

平台分布

OpenClaw

85.6%
按下载量换算5,417

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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。目前只有一个公开来源,建议同时核对源仓库。

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