Agent skill

Afrexai Lead Hunter

by LeoYeAI in LeoYeAI/openclaw-master-skills

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents.

MITAuto-check passedMarketing & SEO

Install Afrexai Lead Hunter

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill afrexai-lead-hunter -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills afrexai-lead-hunter --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/afrexai-lead-hunter .claude/skills/afrexai-lead-hunter && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
afrexai-lead-hunter
GitHub stars
2.2k
Token cost
~4.4k tokens
SKILL.md length
958 words
Files
3
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents.

  • Works in 8 steps: Define Your Ideal Customer Profile (ICP) → Multi-Source Discovery → Enrichment Engine → …
  • Tasks that involve Lead generation
  • SKILL.md covers Architecture, Phase 1: Define Your Ideal…, Phase 2: Multi-Source Discovery and Phase 3: Enrichment Engine, plus 4 more sections
  • Reaches acme.com and linkedin.com

What it does

Afrexai Lead Hunter is an agent skill from LeoYeAI/openclaw-master-skills. 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.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `README.md` and `_meta.json`).

It sits in Marketing & SEO, covering Lead generation and Cold outreach. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve Lead generation
  • Tasks that involve Cold outreach

Example prompts

  • “/afrexai-lead-hunter”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Define Your Ideal Customer Profile (ICP)
  2. Multi-Source Discovery
  3. Enrichment Engine
  4. Lead Scoring Algorithm
  5. Segmentation & Campaign Assignment
  6. Outreach Sequence Templates
  7. CRM & Pipeline Management
  8. Automation & Scheduling

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, json, csv and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • acme.com
    • linkedin.com

    Also links to:

    • afrexai-cto.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Afrexai Lead Hunter loads about 4.4k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 958 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 958 words, ~4,432 tokens.

Download SKILL.mdSave it as .claude/skills/afrexai-lead-hunter/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
afrexai-lead-hunter
description
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.
tags
leads, sales, b2b, prospecting, enrichment, outreach, pipeline, crm, cold-email, icp
author
AfrexAI
version
1.0.0
license
MIT

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
yaml
# 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
yaml
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 < 10 employees (no budget)
    - already has AI vendor (check for competitors in their stack)
Scoring Weights
yaml
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
SourceBest ForHow To SearchData QualityCost
Web SearchAny industry"[industry] companies" site:linkedin.com/companyHighFree
GitHubDev tools, tech companiesSearch repos, org pages, contributor profilesHighFree
Product HuntStartups, SaaSBrowse launches, upvoters (they're buyers too)MediumFree
Industry ListsTargeted verticals"Top 50 [industry] companies 2026", Clutch, G2HighFree
Job BoardsHiring = growing = buying"AI" OR "automation" site:lever.co OR site:greenhouse.ioHighFree
CrunchbaseFunded startupsRecently funded companies in target verticalsHighFreemium
Conference SpeakersActive industry leadersSpeaker lists from industry eventsVery HighFree
Podcast GuestsThought leaders with budgetSearch "[industry] podcast" transcriptsHighFree
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 <meta> 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. first.last@company.com     (most common, ~40%)
2. first@company.com          (startups, ~25%)
3. firstlast@company.com      (~15%)
4. flast@company.com           (~10%)
5. first_last@company.com     (~5%)
6. last.first@company.com     (~3%)
7. first.l@company.com        (~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)
SignalPointsHow to Check
Industry matches ICP exactly+10Compare to ICP config
Employee count in sweet spot+5LinkedIn/website
Revenue in target range+5Crunchbase/estimate
Located in target geography+3Website/LinkedIn
Uses compatible tech stack+4Job posts, BuiltWith
No competitor currently+3Research, case studies
Persona Score (0-20 points)
SignalPointsHow to Check
Title matches buyer persona+8LinkedIn
C-Suite or VP level+5LinkedIn
Has decision authority+4Title + company size
Active on LinkedIn (posts monthly)+3LinkedIn activity
Intent Score (0-25 points)
SignalPointsHow to Check
Recently posted about relevant pain+8LinkedIn/Twitter
Company hiring for roles you'd replace+7Job boards
Attended relevant industry event+5Conference lists
Downloaded competitor content+3Hard to verify, skip if unknown
Searched for solution keywords+2Hard to verify, skip if unknown
Timing Score (0-15 points)
SignalPointsHow to Check
New in role (< 90 days)+5LinkedIn start date
Company just raised funding+4Crunchbase/news
End of quarter (budget flush)+3Calendar
Company growing fast (hiring surge)+3Job postings count
Show full SKILL.md (396 more words)Show less
Engagement Score (0-10 points)
SignalPointsHow to Check
Opened previous email+4Email tracking
Visited your website+3Analytics
Connected on LinkedIn+2LinkedIn
Referred by someone+1CRM 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
json
{
  "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": "jane.smith@acme.com",
    "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
csv
company,contact,title,email,linkedin,score,tier,industry,employees,pain_signal
Acme Corp,Jane Smith,VP Ops,jane@acme.com,linkedin.com/in/jane,66,B,SaaS,150,hiring analysts
Weekly Report Template
markdown
# 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.

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/afrexai-lead-hunter of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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Categories

Questions about Afrexai Lead Hunter

What does Afrexai Lead Hunter do?

Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Afrexai Lead Hunter is an agent skill from LeoYeAI/openclaw-master-skills. Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents.

When should I use Afrexai Lead Hunter?

Afrexai Lead Hunter fits situations like: tasks that involve Lead generation; tasks that involve Cold outreach.

How do I install Afrexai Lead Hunter in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill afrexai-lead-hunter -a claude-code`. Or copy the skill folder (skills/afrexai-lead-hunter in LeoYeAI/openclaw-master-skills) into .claude/skills/afrexai-lead-hunter in your project. Claude Code loads it when a task matches its description.

How do I install Afrexai Lead Hunter in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill afrexai-lead-hunter -a codex`. Or copy the skill folder (skills/afrexai-lead-hunter in LeoYeAI/openclaw-master-skills) into .agents/skills/afrexai-lead-hunter in your project. Codex loads it when a task matches its description.

Can I use Afrexai Lead Hunter in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill afrexai-lead-hunter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/afrexai-lead-hunter, .gemini/skills/afrexai-lead-hunter, .github/skills/afrexai-lead-hunter and .opencode/skills/afrexai-lead-hunter in your project.

What does Afrexai Lead Hunter need to run?

SKILL.md names no scripts, command-line tools or credentials: Afrexai Lead Hunter is instructions for the agent only.

Does Afrexai Lead Hunter access the network?

SKILL.md names 3 domains. In commands or code: acme.com and linkedin.com; the agent is likely to contact these when it follows the instructions. As links in the text: afrexai-cto.github.io. This is read from the text; nothing was executed.

Is Afrexai Lead Hunter safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Afrexai Lead Hunter use?

Afrexai Lead Hunter is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Afrexai Lead Hunter use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Afrexai Lead Hunter?

Skills that share tags, products or a category with Afrexai Lead Hunter: 100m Leads (getagentseal/founder-playbook, 729 stars), Google Maps API Skill (browser-act/skills, 6.1k stars), List Builder (explorium-ai/gtm-skills, 185 stars) and Lead Researcher (borghei/Claude-Skills, 891 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Afrexai Lead Hunter?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.