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SKILL研究检索

原名 ironclaw-lead-enrichment

研究检索 联网访问 许可证明确 OpenClaw
安装这个技能

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。

累计安装 19,560 本周 799
GitHub 星标 未标注 仓库 ironclaw-lead-enrichment
下载量 6,328 技能市场下载数
权限级别 联网访问 安装前请确认风险范围
许可证 许可证明确 MIT-0

安装

ClawHub 市场
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
openclaw skills install ironclaw-lead-enrichment

技能档案

研究检索
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aspenas仓库 ironclaw-lead-enrichment · 版本 1.0.0
宿主客户端OpenClaw技能可被这些客户端加载
安装方式ClawHub 市场安装包 aspenas/ironclaw-lead-enrichment
权限级别联网访问安装前请确认风险范围
许可证MIT-0许可证明确,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2026-05-01上游最后核验时间 · 数据更新 未标注
正文体量5.1 KBSKILL.md 原文大小,越大说明说明书写得越细

能做什么

5 项能力
上游给出的能力概览与适用场景
  • 按任务关键词查找相关 Skills
  • 展示可复制的安装命令
  • 保留来源站点、仓库和原始说明,方便继续核验
  • 补充不同宿主或平台的使用分布数据
  • 展示第三方安全扫描或审计结果
OpenClaw 用户查找和安装 Skill 时用户想查找某类 Agent Skill 时需要根据任务场景推荐可安装能力包时需要对比不同来源的安装命令和来源信息时

权限与风险

联网访问
装之前先读完这段

安装与使用指南

安装步骤

本站只展示命令,不托管安装包,也不自动执行。

怎么用

安装后应在对应宿主中按原始 README 的触发条件使用;当前草稿只做目录说明,具体调用方式由规则抽取,异常条目再复核。

SKILL.md 全文

5.1 KB
模型实际读到的说明书原文,来自技能仓库

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
  3. Optionally verify with web search: "email" "{name}" site:{domain}
  4. 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
  5. 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 Data DuckDB Field Type
Full name Name text
Email address Email email
LinkedIn URL LinkedIn URL url
Job title Title text
Company name Company text / relation
Location Location text
Education Education text
Phone Phone phone
Company size Company Size text
Industry Industry text
Enrichment date Enriched At date
Confidence Enrichment Confidence enum (high/medium/low)

安全信息

审计结论
未标注不代表已通过审计
安全审计VirusTotal、ClawScan、Static analysis第三方安全审计平台给出的结论

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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 2026-05-01。