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AfrexAI Lead Hunter Pro

作者 1kalin · GitHub ↗ · v1.0.0
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在 OpenClaw 中安装
/install afrexai-lead-hunter
功能描述
Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.
使用说明 (SKILL.md)

AfrexAI Lead Hunter Pro

Turn your AI agent into a full B2B sales development machine. Discovery → Enrichment → Scoring → Outreach → CRM. Zero manual work.


Architecture

DEFINE ICP ──▶ DISCOVER ──▶ ENRICH ──▶ SCORE ──▶ SEGMENT ──▶ OUTREACH ──▶ CRM
    │              │            │          │          │            │          │
    ▼              ▼            ▼          ▼          ▼            ▼          ▼
 Persona      Multi-source  Email+Phone  ICP fit   Tier A/B/C  Sequences  Pipeline
 Builder      Web Research  Company Data  Intent    Campaigns   Templates  Tracking

Phase 1: Define Your Ideal Customer Profile (ICP)

Before hunting, know WHO you're hunting. Answer these:

Company-Level ICP

# Copy and customize this ICP template
company:
  industries: [SaaS, fintech, legal-tech, prop-tech]
  employee_range: [50, 500]        # sweet spot for AI adoption
  revenue_range: [$5M, $100M]      # can afford $120K+ contracts
  funding_stage: [Series A, Series B, Series C]
  tech_signals:                     # tools that indicate AI readiness
    positive: [Salesforce, HubSpot, Snowflake, AWS, Python]
    negative: [no-website, wordpress-only]
  geography: [US, UK, Canada, Australia]
  pain_signals:                     # problems they're likely facing
    - "manual data entry"
    - "compliance overhead"
    - "scaling operations"
    - "document processing"

Buyer Persona

persona:
  titles: [CEO, CTO, COO, VP Operations, Head of Innovation, Director of IT]
  seniority: [C-Suite, VP, Director]
  decision_authority: true          # can sign $50K+ without board approval
  linkedin_activity:                # signals they're actively looking
    - posts about AI/automation
    - comments on digital transformation content
    - recently changed roles (first 90 days = buying window)
  anti-signals:                     # skip these
    - "consultant" in title (not buyers)
    - company \x3C 10 employees (no budget)
    - already has AI vendor (check for competitors in their stack)

Scoring Weights

scoring:
  icp_company_match: 30             # how well company matches
  icp_persona_match: 20             # right title + seniority
  intent_signals: 25                # actively looking for solutions
  engagement_recency: 15            # recent activity online
  timing_bonus: 10                  # new role, funding round, hiring
  
  thresholds:
    tier_a: 80                      # hot — outreach immediately
    tier_b: 60                      # warm — nurture sequence
    tier_c: 40                      # cool — add to newsletter
    disqualify: below 40            # don't waste time

Phase 2: Multi-Source Discovery

Source Priority Matrix

Source Best For How To Search Data Quality Cost
Web Search Any industry "[industry] companies" site:linkedin.com/company High Free
GitHub Dev tools, tech companies Search repos, org pages, contributor profiles High Free
Product Hunt Startups, SaaS Browse launches, upvoters (they're buyers too) Medium Free
Industry Lists Targeted verticals "Top 50 [industry] companies 2026", Clutch, G2 High Free
Job Boards Hiring = growing = buying "AI" OR "automation" site:lever.co OR site:greenhouse.io High Free
Crunchbase Funded startups Recently funded companies in target verticals High Freemium
Conference Speakers Active industry leaders Speaker lists from industry events Very High Free
Podcast Guests Thought leaders with budget Search "[industry] podcast" transcripts High Free

Discovery Search Templates

Find companies by pain signal:

"[industry]" "manual process" OR "time-consuming" OR "looking for solutions" site:linkedin.com

Find companies by hiring signal (they're growing = they're buying):

"[company type]" "hiring" "AI" OR "automation" OR "data" site:linkedin.com/jobs

Find recently funded companies (flush with cash):

"[industry]" "raises" OR "Series A" OR "funding" OR "investment" 2026

Find companies using competitor tools (ripe for switching):

"[competitor tool]" "alternative" OR "switching from" OR "replaced"

Find decision makers directly:

"[title]" "[industry]" "[city/region]" site:linkedin.com/in

Discovery Workflow

FOR each search query:
  1. Run web_search with the query
  2. Extract company names + URLs from results
  3. Deduplicate against existing leads
  4. For each NEW company:
     a. Visit company website → extract: industry, size estimate, tech signals
     b. Search "[company name] CEO" OR "[company name] founder" → get decision maker
     c. Search "[company name] funding" → get financial signals
     d. Create lead record (see schema below)
  5. Rate limit: 2-3 second delay between searches

Phase 3: Enrichment Engine

For each discovered lead, enrich with verified data:

Company Enrichment Checklist

  • Website — Load homepage, extract value prop, tech stack (check \x3Cmeta> tags, JS frameworks)
  • Employee Count — LinkedIn company page, Crunchbase, or website "About" page
  • Revenue Estimate — Funding amount × 3-5x multiplier, or industry benchmarks
  • Tech Stack — Check BuiltWith, Wappalyzer data, or job postings for tech mentions
  • Recent News — Last 90 days: funding, launches, executive changes, partnerships
  • Pain Indicators — Job postings mentioning problems you solve, blog posts about challenges
  • Competitor Usage — Do they use a competitor? Which one? (Check G2 reviews, case studies)

Contact Enrichment Checklist

  • Full Name — First + Last from LinkedIn or company page
  • Title — Current role (verify it matches your buyer persona)
  • Email Pattern — Determine company pattern: first@, first.last@, firstlast@, f.last@
  • Email Verification — Test pattern with known format, check MX records
  • LinkedIn URL — Direct profile link
  • Recent Activity — What have they posted/shared in last 30 days?
  • Mutual Connections — Anyone in your network connected to them?
  • Content Interests — What topics do they engage with? (Use for personalization)

Email Pattern Detection

Common patterns (test in order of likelihood):
1. [email protected]     (most common, ~40%)
2. [email protected]          (startups, ~25%)
3. [email protected]      (~15%)
4. [email protected]           (~10%)
5. [email protected]     (~5%)
6. [email protected]     (~3%)
7. [email protected]        (~2%)

Verification approach:
- Check if company has public team page with email format
- Look for email in GitHub commits from company domain
- Check email format on Hunter.io or similar (if available)
- Search "[person name] email [company]" 
- Check their personal website/blog for contact

Phase 4: Lead Scoring Algorithm

Score each lead 0-100 using this rubric:

Company Score (0-30 points)

Signal Points How to Check
Industry matches ICP exactly +10 Compare to ICP config
Employee count in sweet spot +5 LinkedIn/website
Revenue in target range +5 Crunchbase/estimate
Located in target geography +3 Website/LinkedIn
Uses compatible tech stack +4 Job posts, BuiltWith
No competitor currently +3 Research, case studies

Persona Score (0-20 points)

Signal Points How to Check
Title matches buyer persona +8 LinkedIn
C-Suite or VP level +5 LinkedIn
Has decision authority +4 Title + company size
Active on LinkedIn (posts monthly) +3 LinkedIn activity

Intent Score (0-25 points)

Signal Points How to Check
Recently posted about relevant pain +8 LinkedIn/Twitter
Company hiring for roles you'd replace +7 Job boards
Attended relevant industry event +5 Conference lists
Downloaded competitor content +3 Hard to verify, skip if unknown
Searched for solution keywords +2 Hard to verify, skip if unknown

Timing Score (0-15 points)

Signal Points How to Check
New in role (\x3C 90 days) +5 LinkedIn start date
Company just raised funding +4 Crunchbase/news
End of quarter (budget flush) +3 Calendar
Company growing fast (hiring surge) +3 Job postings count

Engagement Score (0-10 points)

Signal Points How to Check
Opened previous email +4 Email tracking
Visited your website +3 Analytics
Connected on LinkedIn +2 LinkedIn
Referred by someone +1 CRM notes

Phase 5: Segmentation & Campaign Assignment

Tier A (Score 80-100) — HOT LEADS

Action: Immediate personalized outreach
Sequence: 5-touch hyper-personalized campaign
Timeline: Contact within 24 hours
Channel: Email → LinkedIn → Phone (if available)
Template: "CEO-to-CEO" or "Specific Pain" (see below)

Tier B (Score 60-79) — WARM LEADS

Action: Nurture sequence
Sequence: 7-touch value-first campaign  
Timeline: Start within 48 hours
Channel: Email → LinkedIn
Template: "Value Insight" or "Case Study" (see below)

Tier C (Score 40-59) — COOL LEADS

Action: Add to newsletter + long-term nurture
Sequence: Monthly value content
Timeline: Bi-weekly touchpoints
Channel: Email only
Template: "Industry Report" or "Educational" (see below)

Phase 6: Outreach Sequence Templates

Template 1: The Specific Pain (Tier A)

Email 1 — Day 0 (The Hook)

Subject: [specific pain point] at [Company]?

Hi [First Name],

Noticed [Company] is [specific observation — hiring for X role / posted about Y challenge / using Z tool].

That usually means [pain point they're likely feeling].

We built [solution] that [specific result with number]. [Client name] cut their [metric] by [X%] in [timeframe].

Worth a 15-min call to see if it fits [Company]?

[Your name]

Email 2 — Day 3 (The Proof)

Subject: Re: [original subject]

[First Name] — quick follow-up.

Here's exactly what we did for [similar company]: [1-sentence case study with specific numbers].

[Link to case study or calculator]

Happy to walk through how this maps to [Company].

[Your name]

Email 3 — Day 7 (The Angle)

Subject: [industry trend] + [Company]

[First Name],

[Industry trend or stat that's relevant]. Companies like [Company] are [what smart companies are doing about it].

We help [type of company] [specific outcome]. Takes about [timeframe] to see results.

Open to a quick chat this week?

[Your name]

Email 4 — Day 14 (The Breakup)

Subject: Should I close your file?

[First Name],

I've reached out a few times — totally understand if the timing isn't right.

If [pain point] becomes a priority, here's a [free resource] that might help: [link]

Either way, I'll stop filling your inbox. Just reply "yes" if you'd like to chat sometime.

[Your name]

Template 2: The Value-First (Tier B)

Email 1 — Lead with insight, not a pitch

Subject: [number] [industry] companies are doing [thing] wrong

Hi [First Name],

We analyzed [X] companies in [industry] and found that [surprising insight].

The ones getting it right are [what top performers do differently].

Put together a quick breakdown: [link to free resource/calculator]

Thought it'd be useful given what [Company] is building.

[Your name]

Template 3: The LinkedIn Warm-Up

Step 1: View their profile (creates notification) Step 2 (Day 2): Like/comment on their recent post (genuine, not generic) Step 3 (Day 4): Send connection request with note:

Hi [Name] — been following [Company]'s work in [space]. 
Particularly liked your take on [specific post topic]. 
Would love to connect.

Step 4 (Day 7, after accepted): Send value message (NOT a pitch):

[Name] — saw you mentioned [challenge] in your recent post. 
We put together [free resource] that addresses exactly that. 
Thought you might find it useful: [link]

Phase 7: CRM & Pipeline Management

Lead Record Schema

{
  "id": "lead-001",
  "created": "2026-02-13",
  "source": "web-search",
  
  "company": {
    "name": "Acme Corp",
    "website": "https://acme.com",
    "industry": "SaaS",
    "employees": 150,
    "revenue_est": "$20M",
    "funding": "Series B — $15M (2025)",
    "tech_stack": ["Salesforce", "AWS", "React"],
    "location": "San Francisco, CA"
  },
  
  "contact": {
    "first_name": "Jane",
    "last_name": "Smith",
    "title": "VP of Operations",
    "email": "[email protected]",
    "email_verified": false,
    "linkedin": "https://linkedin.com/in/janesmith",
    "phone": null
  },
  
  "scoring": {
    "company_score": 25,
    "persona_score": 18,
    "intent_score": 15,
    "timing_score": 8,
    "engagement_score": 0,
    "total": 66,
    "tier": "B"
  },
  
  "enrichment": {
    "pain_signals": ["hiring 3 data analysts", "blog about manual reporting"],
    "recent_news": ["Raised Series B in Jan 2026"],
    "competitor_usage": "None detected",
    "content_interests": ["data automation", "operational efficiency"]
  },
  
  "outreach": {
    "status": "not_started",
    "sequence": "value-first",
    "emails_sent": 0,
    "last_contacted": null,
    "next_action": "2026-02-14",
    "replies": [],
    "notes": ""
  },
  
  "pipeline": {
    "stage": "prospect",
    "deal_value": null,
    "probability": 0,
    "next_step": "Initial outreach"
  }
}

Pipeline Stages

PROSPECT → CONTACTED → REPLIED → MEETING_BOOKED → QUALIFIED → PROPOSAL → NEGOTIATION → CLOSED_WON / CLOSED_LOST

Tracking Metrics

Track these weekly to optimize your machine:

  • Discovery rate: leads found per search session
  • Enrichment completeness: % of fields filled per lead
  • Score distribution: what % are Tier A vs B vs C?
  • Response rate: replies / emails sent (target: 5-15%)
  • Meeting rate: meetings / replies (target: 30-50%)
  • Conversion rate: deals / meetings (target: 20-30%)
  • Pipeline velocity: days from discovery → closed deal

Phase 8: Automation & Scheduling

Daily Autopilot Routine

MORNING (agent runs autonomously):
  1. Run 3-5 discovery searches (rotate queries)
  2. Enrich any un-enriched leads from yesterday
  3. Score new leads
  4. Send Day-N emails for active sequences
  5. Check for replies → flag for human review
  6. Update pipeline stages
  7. Report: "Found X leads, sent Y emails, Z replies"

WEEKLY:
  1. Review Tier C leads — any moved to B/A?
  2. Clean dead leads (no response after full sequence)
  3. Analyze response rates by template — A/B test
  4. Refresh ICP based on closed deals
  5. Add new search queries based on wins

Agent Integration

# In your agent's heartbeat or cron:
1. Load ICP config
2. Run discovery for 1 search query
3. Enrich top 5 new leads
4. Score all unscored leads
5. Queue outreach for Tier A leads
6. Log results to daily brief

Output Formats

CSV Export

company,contact,title,email,linkedin,score,tier,industry,employees,pain_signal
Acme Corp,Jane Smith,VP Ops,[email protected],linkedin.com/in/jane,66,B,SaaS,150,hiring analysts

Weekly Report Template

# Lead Hunter Weekly Report — Week of [DATE]

## Pipeline Summary
- Total leads in system: [N]
- New leads this week: [N]  
- Tier A: [N] | Tier B: [N] | Tier C: [N]

## Outreach Performance
- Emails sent: [N]
- Reply rate: [X%]
- Meetings booked: [N]
- Pipeline value added: $[X]

## Top Leads This Week
1. [Company] — [Contact] — Score: [X] — [Why they're hot]
2. [Company] — [Contact] — Score: [X] — [Why they're hot]
3. [Company] — [Contact] — Score: [X] — [Why they're hot]

## Insights
- Best performing search query: [query]
- Best performing email template: [template]
- Recommendation: [action to take]

Pro Tips

  1. The 90-Day Window: New executives are 10x more likely to buy in their first 90 days. Prioritize "new role" signals.
  2. Hiring = Buying: If a company is hiring for the role your product replaces, they have budget AND pain. These are your hottest leads.
  3. Competitor's Customers: Search for reviews/complaints about competitors. Unhappy customers switch fastest.
  4. Conference Lists: Speaker and attendee lists from industry events are gold. These people are actively engaged in the space.
  5. The "Reply to Anything" Rule: Any reply (even "not interested") is valuable. It confirms the email works and the person exists. Log it.
  6. Personalization > Volume: 20 hyper-personalized emails outperform 200 generic ones. Always reference something specific about the prospect.
  7. Multi-Thread: Don't rely on one contact per company. Find 2-3 decision-makers and approach from different angles.
  8. Timing Matters: Tuesday-Thursday, 8-10 AM local time gets the best open rates. Avoid Mondays and Fridays.

Built by AfrexAI — AI agents that actually sell.

安全使用建议
This skill mostly contains templates and workflows for public web discovery and scoring, which is coherent, but it advertises "verified" enrichment and autonomous outreach without listing which services or credentials it will use. Before installing or enabling autonomous runs, ask the publisher: (1) which external APIs or services are required (email verifiers, CRM APIs, SMTP providers, LinkedIn automation), and provide the exact env vars the skill will need; (2) where does enriched data get sent/stored and who can access it; (3) whether outreach messages will be sent automatically and via which account (use a sandbox/test account first); (4) for paid context packs, confirm the external payment/hosted URL and vendor legitimacy. If you must proceed, supply least-privilege, single-purpose credentials (test CRM account, dedicated outbound email) and monitor initial runs. If the publisher cannot specify required connectors or still expects you to provide broad account tokens, decline or treat as untrusted.
功能分析
Type: OpenClaw Skill Name: afrexai-lead-hunter Version: 1.0.0 The OpenClaw AgentSkills bundle 'afrexai-lead-hunter' is classified as benign. The `SKILL.md` and `README.md` files provide detailed instructions for an AI agent to perform B2B lead generation, enrichment, scoring, and outreach. While this process involves capabilities like web scraping, email sending, and LinkedIn interactions (which could be misused if the underlying agent platform is vulnerable), the instructions themselves are entirely aligned with the stated purpose. There is no evidence of intentional malicious behavior such as data exfiltration, unauthorized command execution, persistence mechanisms, or prompt injection attempts designed to subvert the agent's core directives. External links provided are for marketing and additional resources, not for downloading and executing arbitrary code.
能力评估
Purpose & Capability
The stated purpose (B2B discovery → enrichment → scoring → outreach → CRM) aligns with the SKILL.md and README content: search templates, enrichment checklist, scoring rubric, and outreach templates are present. However, several advertised capabilities ("verified" email/phone enrichment, sending outreach sequences, CRM pipeline management, and paid context packs) normally require external services or credentials (email verification providers, SMTP/SendGrid, CRM API tokens). The skill declares no required env vars or connectors, which is unexpected.
Instruction Scope
The runtime instructions focus on web search, visiting company sites, extracting public signals, deduplication, scoring, and creating lead records — all within the described lead-gen scope. The SKILL.md does not instruct reading local files or unrelated system state. A concern is vagueness around how outreach is executed (how emails/LinkedIn messages are sent or tracked) and how "verified" data is obtained; that vagueness grants broad discretion to the agent at runtime.
Install Mechanism
No install spec or code is included (instruction-only), so there is no download/execution risk from an installer. This is low-risk from an install perspective.
Credentials
Given features like email/phone verification, CRM updates, and automated outreach, the absence of declared required environment variables or primary credentials is disproportionate. Real-world operation typically needs API keys or SMTP/CRM credentials; the skill's omission is an incoherence that could hide runtime prompts for credentials or rely on the agent to use arbitrary connectors, increasing risk of accidental exposure or misconfiguration.
Persistence & Privilege
The skill does not request always:true and does not declare persistent system changes. It can be invoked autonomously (platform default), which is expected for an automation skill. Note: autonomous outreach combined with unspecified outbound channels increases procedural risk if given broad permissions.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install afrexai-lead-hunter
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /afrexai-lead-hunter 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
AfrexAI Lead Hunter Pro v1.0.0 – Initial Release - Launches enterprise-grade B2B lead generation, enrichment, scoring, and outreach automation for AI agents. - Features ICP builder, multi-source lead discovery, verified data enrichment, customizable scoring, and outreach sequencing. - Enables autonomous workflow: define ideal customer, find prospects, enrich leads, score and segment, generate personalized outreach, and sync with CRM. - Includes detailed checklists and search templates for lead discovery and enrichment. - Provides scoring rubrics and thresholds to prioritize hot, warm, and cool leads.
元数据
Slug afrexai-lead-hunter
版本 1.0.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

AfrexAI Lead Hunter Pro 是什么?

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 703 次。

如何安装 AfrexAI Lead Hunter Pro?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install afrexai-lead-hunter」即可一键安装,无需额外配置。

AfrexAI Lead Hunter Pro 是免费的吗?

是的,AfrexAI Lead Hunter Pro 完全免费(开源免费),可自由下载、安装和使用。

AfrexAI Lead Hunter Pro 支持哪些平台?

AfrexAI Lead Hunter Pro 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 AfrexAI Lead Hunter Pro?

由 1kalin(@1kalin)开发并维护,当前版本 v1.0.0。

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