AI Talent Recruiter

A comprehensive skill for AI/ML talent discovery, profiling, and personalized outreach.

Overview

This skill enables the complete talent recruitment pipeline:

1. Search: Generate optimized queries and discover candidates via web search
2. Extract: Scrape profile pages and extract structured candidate information
3. Identify: Recognize Chinese candidates using surname and institution matching
4. Classify: Categorize candidates by type (PhD/PostDoc/Professor/Industry)
5. Deduplicate: Remove duplicate candidates using 7-level fingerprinting
6. Standardize: Map research fields to 22 standardized categories
7. Generate: Create personalized outreach emails using field-specific templates

Scraping Approach Selection

This skill supports two scraping approaches:

Selection Guidelines

• Use Python scraping for academic websites (.edu, .github.io) - FREE
• Use BrightData MCP for LinkedIn and sites requiring login - PAID
• See references/python-scraping-guide.md for detailed implementation

Prerequisites

Option A: BrightData MCP (Paid)

This approach requires the BrightData MCP server to be configured in Claude Code.

1. Get your API token from BrightData
2. Add the MCP server using Claude CLI:

claude mcp add --transport sse --scope user brightdata "https://mcp.brightdata.com/sse?token=<your-api-token>"

Replace <your-api-token> with your actual BrightData API key.

3. Verify the MCP server is connected by checking available tools:
   • mcp__brightdata__search_engine - For web searches
   • mcp__brightdata__scrape_as_markdown - For page scraping
   • mcp__brightdata__web_data_linkedin_person_profile - For LinkedIn profiles

Option B: Python Scraping (Free)

Requires Python dependencies:

pip install requests beautifulsoup4 httpx python-dotenv

See reference scripts in scripts/ directory for implementation templates.

Reference Files

Scripts (Reference Implementations)

Complete Workflow

Step 1: Generate Search Queries

Based on the user's research direction, generate 2-4 search queries using templates from references/search-templates.md.

Query Generation Strategy:

• Include both English and Chinese keywords
• Target high-quality domains: github.io, university sites, LinkedIn
• Combine research direction with role indicators

Example queries for "Reinforcement Learning":

"reinforcement learning" PhD student site:github.io OR site:stanford.edu
"RLHF" "PPO" PhD researcher personal homepage
强化学习 博士生 清华 OR 北大 个人主页

Step 2: Execute Searches

Option A: BrightData MCP (Paid)

Use mcp__brightdata__search_engine with parallel execution:

Tool: mcp__brightdata__search_engine
Parameters:
  engine: "google"
  query: "<generated_query>"

Option B: Python + Serper API (Free)

Use scripts/serper_search.py template:

from serper_search import serper_search

results = await serper_search(
    query='"reinforcement learning" PhD student site:*.edu',
    api_key="YOUR_SERPER_API_KEY"
)
urls = [item["link"] for item in results.get("organic", [])]

URL Filtering Priority (see references/url-priority-rules.md):

1. Personal pages (*.github.io, sites.google.com)
2. University domains (see references/top-ai-labs.md)
3. LinkedIn profiles (linkedin.com/in/)
4. Google Scholar profiles

Step 3: Scrape Candidate Profiles

Option A: BrightData MCP (Paid)

Use mcp__brightdata__scrape_as_markdown for general pages:

Tool: mcp__brightdata__scrape_as_markdown
Parameters:
  url: "<candidate_url>"

For LinkedIn profiles, use the specialized tool:

Tool: mcp__brightdata__web_data_linkedin_person_profile
Parameters:
  url: "<linkedin_url>"

Option B: Python Scraping (Free)

Use scripts/httpx_scraper.py or scripts/lab_member_scraper.py:

# Simple static pages
from httpx_scraper import batch_scrape

results = await batch_scrape(urls, max_concurrent=5)

# Lab member pages
from lab_member_scraper import LabMemberScraper
scraper = LabMemberScraper()
members = scraper.scrape_lab("https://ai.stanford.edu/people/")

Cloudflare Email Decryption (see scripts/cloudflare_email_decoder.py):

from cloudflare_email_decoder import decode_cloudflare_email
email = decode_cloudflare_email("f493919e85c6c5...")

Scrape in parallel: Process 2-5 URLs simultaneously for efficiency.

Anti-scraping solutions: See references/anti-scraping-solutions.md for handling:

• Cloudflare protection
• User-Agent detection
• Rate limiting
• IP blocking

Step 4: Extract Profile Data

From scraped content, extract fields defined in references/profile-schema.md:

Required fields:

• name: English name
• name_cn: Chinese name (if available)
• title: Position
• affiliation: University/Company
• email: Contact email

Recommended fields:

• advisor: PhD advisor
• research_interests: Research areas
• homepage, google_scholar, github, linkedin
• education, experience
• publications, citation_count, h_index

Step 5: Identify Chinese Candidates (Optional)

Use rules from references/chinese-surnames.md:

Multi-dimensional scoring:

• Surname match (40%): Check against 100+ Chinese surnames
• Institution match (35%): Check against Chinese universities/labs
• Name structure (15%): Pinyin pattern analysis
• ID pattern (10%): OpenReview ID analysis

Decision threshold: Confidence >= 0.5

# Pseudo-code for Chinese detection
is_chinese = (
    surname_score * 0.4 +
    institution_score * 0.35 +
    structure_score * 0.15 +
    id_score * 0.1
) >= 0.5

Step 6: Classify Candidate Type

Use rules from references/candidate-classifier.md:

Priority order: PhD > PostDoc > Professor > Industry > Master > Unknown

Classification keywords:

• PhD: "phd student", "doctoral student", "博士生"
• PostDoc: "postdoc", "post-doctoral", "博士后"
• Professor: "professor", "associate professor", "教授"
• Industry: "engineer", "research scientist at [company]", "算法工程师"

Step 7: Deduplicate Candidates

Use 7-level fingerprinting from references/deduplication-rules.md:

Priority (highest to lowest):

1. Email (most reliable)
2. Google Scholar URL
3. LinkedIn URL
4. GitHub URL
5. Personal website
6. Composite hash (name + school + field)
7. Source URL hash (last resort)

# Standardization before comparison
email = email.lower().strip()
url = url.lower().replace("https://", "").replace("http://", "").replace("www.", "").rstrip("/")

Step 8: Standardize Research Field

Use references/field-mappings.md to map research directions to 22 standardized fields:

Standard fields: RL, NLP, Multimodal, MOE, Pre-training, post-train, Alignment, Reasoning, Agent&RAG, MLSys, LLM4CODE, Computer Vision, Embodiment, Audio, EVAL, data, AI4S, Interpretable AI, Recommendation System, Federated Learning, Trustworthy AI, Pre/Post-train×RL

def get_standardized_field(research_field: str) -> str:
    for standard_field, aliases in FIELD_MAPPING.items():
        for alias in aliases:
            if alias.lower() in research_field.lower():
                return standard_field
    return "default"

Step 9: Generate Personalized Email

Use templates from references/email-templates.md and talk tracks from references/talk-tracks.md:

Template structure:

Hi, {{researcher_name}},

[Field-specific opening paragraph referencing their work]

{{technical_hook}}  # Connect their specific work to our interests

{{talk_track_paragraph}}  # 3-4 sentences showing domain depth

[Closing with call to action]

Signature

Key placeholders to fill:

• {{technical_hook}}: Connect candidate's specific work to team interests
• {{talk_track_paragraph}}: Domain expertise demonstration

Step 10: Present Results

Format results as a structured table:

## Candidate Profile: [Name]

| Field | Value |
|-------|-------|
| Name | [English Name] ([Chinese Name]) |
| Type | [PhD/PostDoc/Professor/Industry] |
| Affiliation | [University/Lab] |
| Research Field | [Standardized Field] |
| Chinese | [Yes/No (confidence)] |
| Email | [Email] |
| Scholar | [URL] |
| GitHub | [URL] |

**Generated Email:**
[Personalized email based on their field]

Usage Examples

Example 1: Complete PhD Search with Email Generation

User Request: "Search for RL PhD students at top universities and generate personalized emails"

Execution:

1. Generate queries for "reinforcement learning PhD student"
2. Execute searches, collect URLs
3. Scrape profiles in parallel
4. Extract structured data
5. Identify Chinese candidates (optional)
6. Classify as PhD
7. Deduplicate using email/Scholar URLs
8. Standardize to "RL" field
9. Generate emails using RL template and talk tracks
10. Present results with emails

Example 2: Lab-Targeted Search (直接爬取实验室页面)

User Request: "Find all PhD students at NJU LAMDA lab"

Execution (两阶段爬取):

1. Fetch lab people page: https://www.lamda.nju.edu.cn/CH.PhD_student.ashx
2. Extract member entries from list page (filter by Chinese name length 2-5 chars)
3. Exclude navigation noise (MainPage, Pub.ashx, etc. via URL keyword filter)
4. For each member detail page (/Name/):
   • Extract email with multi-method support: mailto: → Cloudflare XOR → [at] regex → plain regex
   • Extract English name from URL path as fallback (e.g., /zhangzn/ → zhangzn)
   • Extract publications from "Publications" header + following ul/ol
   • Handle SSL errors gracefully (skip www.nju.edu.cn etc.)
5. Classify, deduplicate, present results
6. See scripts/lab_member_scraper.py for complete implementation

Practical tips:

• Chinese lab websites often use .ashx (ASP.NET) pages
• Email obfuscation: [at] / (at) is more common than Cloudflare on Chinese .edu.cn sites
• SSL handshake failures are common on .edu.cn domains - catch and skip
• Use time.sleep(1.0) for Chinese university websites (more conservative than global sites)

Example 3: Conference Paper Author Discovery (OpenReview API)

User Request: "Find Chinese researchers who published at ICML 2025"

Execution (OpenReview API 方案 — 推荐):

1. Login to OpenReview API (api2.openreview.net)
2. Fetch all papers: client.get_all_notes(content={'venueid': 'ICML.cc/2025/Conference'})
3. For each paper, extract authors and author IDs
4. Identify Chinese authors using surname matching (references/chinese-surnames.md)
5. For Profile IDs (~Name1): call client.get_profile() to get Email, Homepage, Scholar, DBLP, ORCID, GitHub
6. For Email IDs (user@email.com): use directly as contact email
7. Apply three-level email fallback: preferredEmail → emails[0] → 'Hidden'
8. Handle Google Scholar field name variants (gscholar or google_scholar)
9. Construct ORCID URLs from bare IDs if needed
10. Save to CSV with utf-8-sig encoding
11. See references/conference-paper-scraping.md for complete guide and scripts/openreview_scraper.py for code

Performance benchmark (ICML 2025):

• 3,257 papers → 8,221 Chinese author records in ~12 min (4.73 it/s)
• Homepage: 73%, Google Scholar: 72%

Execution (Web 搜索方案 — 备选):

1. Search: ICML 2025 "alignment" authors site:openreview.net
2. Extract author names and affiliations from search results
3. For each author, search for their personal page
4. Scrape profiles and identify Chinese candidates
5. Standardize research field, generate personalized emails

Example 4: GitHub Social Network Discovery

User Request: "Find researchers in AmandaXu97's GitHub Following list"

Execution (GitHub API 三层数据拼装):

1. Get Following list via GitHub API: GET /users/AmandaXu97/following?per_page=100 (paginated)
2. For each user, assemble profile from three layers:
   • Layer 1: GET /users/{login} → name, bio, email, company, blog, twitter_username
   • Layer 2: GET /users/{login}/social_accounts → dedicated social links
   • Layer 3: raw.githubusercontent.com/{login}/{login}/main/README.md → extract Scholar/LinkedIn/知乎 via regex
3. Merge all text sources and extract links using regex patterns
4. Classify candidates, deduplicate, present results
5. See scripts/github_network_scraper.py for complete implementation

Performance benchmark (AmandaXu97):

• 926 following users processed successfully
• Rate limit: 5,000 requests/hour with Token (vs 60/hour without)
• Each user ≈ 3 API calls → ~2,778 total calls within limits

Key insight: Profile README is a hidden goldmine — many researchers put Scholar, personal homepage, and social links there that aren't in the API profile.

Example 5: Hugo Academic Card Parsing with Cloudflare Decryption

User Request: "Find all members of PKU.AI lab and extract their emails"

Execution (单页卡片提取 — Hugo Academic 模板):

1. Detect Hugo Academic template: look for .people-person or .media.stream-item CSS classes
2. Fetch single page: https://pku.ai/people/ (only 1 HTTP request needed)
3. For each .people-person card:
   • Extract name from .portrait-title h2
   • Extract role/affiliation from .portrait-title h3 and .portrait-subtitle
   • Extract social links from .network-icon a
4. For Cloudflare-protected emails in .network-icon a:
   • Detect /cdn-cgi/l/email-protection# in href
   • Extract hex string after #, XOR decrypt with first byte as key
   • continue to skip further link classification
5. For relative URLs (/path): prepend base URL (https://pku.ai + /path)
6. Classify remaining links: GitHub, Scholar, LinkedIn, Zhihu, Bilibili
7. See scripts/lab_member_scraper.py (scrape_card_page()) for implementation

Performance benchmark (PKU.AI):

• 65 members extracted from single page in <1 second
• 30+ Cloudflare emails successfully decrypted (~95% success rate on encrypted emails)
• Only 1 HTTP request (vs 66+ for two-phase approach)

Key insight: Hugo Academic is the most popular academic website template. Recognizing .people-person + .network-icon CSS classes lets you extract all data from one page without visiting individual profile pages. Always check for Cloudflare email protection (email-protection in href) before converting relative URLs to absolute.

Example 6: Email Anchor Reverse Lookup (防御性爬虫策略)

User Request: "Find all members of Tsinghua MediaLab (custom HTML, no fixed CSS classes)"

Execution (邮箱反向定位法 — 适用于自定义 HTML):

1. Recognize the challenge: page has no .people-person or other fixed CSS classes
2. Use email anchor strategy: search for all text nodes containing @
3. For each email node, traverse up the DOM tree (max 4 levels) to find the person card container
4. Container recognition heuristics:
   • Must be div or li tag
   • Text length between 20-3000 characters (single person card range)
   • Deduplicate: process each container only once
5. Extract from container:
   • Name: first line (filter out "All Faculty", "Team" etc.)
   • Split Chinese/English: re.findall(r'[\u4e00-\u9fa5]+') for Chinese, re.sub to remove Chinese for English
   • Email: regex match [\w\.-]+@[\w\.-]+\.[a-zA-Z]{2,}
   • Title: lines containing "Professor", "PhD", "Master", "Student"
   • Detail page link: relative or same-domain links (exclude mailto:)
6. Optional: visit detail page for more info (bio, publications, social links)
7. Filter out footer contact info (containing "Address", "Mailbox", "Contact")
8. See scripts/lab_member_scraper.py (scrape_by_email_anchor()) for implementation

Performance benchmark (Tsinghua MediaLab):

• 39 members extracted from 39 email nodes
• 100% extraction success rate (all email nodes correctly mapped to person cards)
• Handles mixed Chinese/English names: "Lu Fang 方路" → cn_name="方路", en_name="Lu Fang"

Key insight: Email anchor reverse lookup is a defensive scraping strategy for when all else fails. It doesn't rely on CSS classes or page structure — only on the universal presence of email addresses on academic websites. The DOM traversal heuristic (20-3000 char text length) effectively filters out empty tags and full-page containers.

When to use each method:

• Hugo Academic template detected, card has emails → scrape_card_page() (Example 5)
• Hugo Academic template detected, card only has names → two-phase with individual page visit (Example 8)
• Clear person link list exists → scrape_lab() (Example 2)
• No fixed structure, but emails present → scrape_by_email_anchor() (Example 6)

Example 5: Hugo Academic Card Parsing with Cloudflare Decryption

User Request: "Find all members of PKU.AI lab and extract their emails"

Execution (单页卡片提取 — Hugo Academic 模板):

1. Detect Hugo Academic template: look for .people-person or .media.stream-item CSS classes
2. Fetch single page: https://pku.ai/people/ (only 1 HTTP request needed)
3. For each .people-person card:
   • Extract name from .portrait-title h2
   • Extract role/affiliation from .portrait-title h3 and .portrait-subtitle
   • Extract social links from .network-icon a
4. For Cloudflare-protected emails in .network-icon a:
   • Detect /cdn-cgi/l/email-protection# in href
   • Extract hex string after #, XOR decrypt with first byte as key
   • continue to skip further link classification
5. For relative URLs (/path): prepend base URL (https://pku.ai + /path)
6. Classify remaining links: GitHub, Scholar, LinkedIn, Zhihu, Bilibili
7. See scripts/lab_member_scraper.py (scrape_card_page()) for implementation

Performance benchmark (PKU.AI):

• 65 members extracted from single page in <1 second
• 30+ Cloudflare emails successfully decrypted (~95% success rate on encrypted emails)
• Only 1 HTTP request (vs 66+ for two-phase approach)

Key insight: Hugo Academic is the most popular academic website template. Recognizing .people-person + .network-icon CSS classes lets you extract all data from one page without visiting individual profile pages. Always check for Cloudflare email protection (email-protection in href) before converting relative URLs to absolute.

Example 7: CVF Paper Scraping with PDF Email Extraction

User Request: "Scrape all CVPR 2025 papers and extract author emails from PDFs"

Execution (CVF HTML 爬虫 + PDF 邮箱提取):

1. Fetch CVF listing page: https://openaccess.thecvf.com/CVPR2025?day=all
2. Parse HTML using sibling traversal: dt.ptitle → find_next_sibling('dd') for authors → next dd for PDF link
3. For each paper PDF:
   • Download to memory stream (io.BytesIO, no disk I/O)
   • Extract first page text via PyMuPDF (fitz): doc[0].get_text("text").replace('\n', ' ')
   • Apply dual email extraction strategy:
     • Strategy 1: Standard regex [a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+
     • Strategy 2: Bracket notation parser for LaTeX-style {user1@sub., user2}domain.edu
   • Infer institutions from email domains (.edu / .ac)
4. Save to CSV with utf-8-sig encoding
5. See scripts/cvf_paper_scraper.py for complete implementation

Performance benchmark (CVPR 2025):

• 2,871 papers processed in ~85 minutes (1.78 s/paper, bottleneck is PDF download)
• MuPDF non-fatal warnings (Screen annotations) safely ignored
• Bracket email parsing successfully handles {bguler@ece., amitrc@ece.}ucr.edu → 2 valid emails

Key insight: CVF Open Access is distinct from OpenReview — no API, no Profile system. Author contact info must be extracted from PDF first pages. The bracket email pattern {users}@?domain is extremely common in CS papers (LaTeX authblk package). Always replace('\n', ' ') on PDF text before regex matching, as PDFs break lines at visual boundaries, not logical ones.

When to use CVF vs OpenReview:

• OpenReview (ICML/NeurIPS/ICLR): API returns structured Profile with Homepage, Scholar, GitHub links
• CVF (CVPR/ICCV/WACV): No API, but PDF emails are often more accurate than OpenReview's preferredEmail
• For maximum coverage: use both platforms and merge results

Environment pitfall: pip install fitz installs a WRONG abandoned package. Always use pip install PyMuPDF. In Colab, must restart runtime after fixing.

Example 8: Hugo Academic Two-Phase Scraping (列表页 → 个人页)

User Request: "Find all members of TongClass (清华-北大通用人工智能实验班) and extract their info"

Execution (两阶段 Hugo Academic — 列表页+个人页):

1. Fetch list page: https://tongclass.ac.cn/people/
2. Parse list page: alternating h2 (grade headers like "清华 23 级") and a[href*="/author/"] (member links)
3. Deduplicate: filter Avatar text nodes and duplicate URLs
4. For each member's individual profile page:
   • Extract email: Cloudflare XOR decrypt (/cdn-cgi/l/email-protection#...)
   • Extract social links: GitHub (exclude wowchemy), Scholar, LinkedIn, Zhihu, Bilibili
   • Filter placeholder links: URL-encoded "无" (%e6%97%a0)
   • Extract interests via regex: Interests\s*\n([\s\S]*?)(?=Education|$)
   • Extract education via regex: Education\s*\n([\s\S]*?)(?=©|$)
   • Detect university from .edu domain links
5. Save to CSV with utf-8-sig
6. See scripts/lab_member_scraper.py for base implementation patterns

Performance benchmark (TongClass):

• 154 members from 6 grades (清华/北大 21-23 级)
• Email: 145/154 (94%), GitHub: 66/154 (43%), Interests: 144/154 (94%)
• Total time: ~30 seconds (0.2s delay × 154 pages)

Key insight: This pattern bridges Example 2 (LAMDA two-phase) and Example 5 (Hugo Academic card-mode). When a Hugo Academic site's list page only shows names (no emails), you must visit individual profile pages. The grade/cohort headers in h2 tags provide valuable metadata for grouping. The Interests\s*\n([\s\S]*?)(?=Education|$) regex pattern works on ANY Hugo Academic profile page.

Debugging journey (valuable decision framework):

1. requests + BS4 → emails empty (Cloudflare protection)
2. Selenium → WebDriver errors on Windows
3. Chrome DevTools MCP → works but too slow for 154 pages
4. BrightData MCP → emails still encrypted in HTML
5. XOR decrypt → breakthrough! Cloudflare only does client-side XOR, the cipher text is in the HTML

When to use two-phase vs card-mode Hugo Academic:

• Card page has .network-icon with emails → card-mode (Example 5)
• Card page only has names/photos, details on individual pages → two-phase (Example 8)

Best Practices

1. Parallel Processing: Execute independent searches and scrapes in parallel
2. Domain Prioritization: Prioritize academic domains over general sites
3. Progressive Filtering: Filter aggressively at each step to reduce processing
4. Error Resilience: Continue processing if individual scrapes fail
5. Deduplication Early: Apply deduplication after extraction, not just at the end
6. Email Quality: Always customize {{technical_hook}} based on actual candidate work
7. Field Mapping: Use standardized fields for consistent categorization
8. Rate Limiting: Space out requests if encountering rate limits

Output Format

Summary Table

Detailed Profile (for each candidate)

## Wei Zhang (张伟)

**Identity:** PhD Student at Tsinghua University
**Field:** Reinforcement Learning (RL)
**Chinese:** Yes - surname match + institution match (confidence: 0.92)

**Contact:**
- Email: wei.zhang@tsinghua.edu.cn
- Homepage: weizhang.github.io
- Scholar: [Google Scholar link]
- GitHub: [GitHub link]

**Research:** RLHF, reward modeling, policy optimization

**Publications:**
1. "Efficient RLHF for LLMs" (NeurIPS 2024)
2. "Reward Hacking in Practice" (ICML 2024)

**Generated Email:**
[Personalized email using RL template]