universal-search

What this skill does

# Universal Deep Search

Comprehensive intelligence gathering across ALL available data sources with cascading analysis.

## Core Principle: CASCADING SEARCH

Every search expands to related entities:
- **PERSON** → + their company + key colleagues + company news
- **COMPANY** → + founders + C-level team + investors + competitors mentions
- **TOPIC** → + key people mentioned + companies involved + YC startups in space

## Query Classification

### Type 1: PERSON
**Triggers:** Names, roles ("CEO of"), profile URLs, "who is", "find person"

**Context to extract:**
- Full name (required)
- Company (helpful)
- Role/title (helpful)
- Location (helpful)

### Type 2: COMPANY
**Triggers:** Company names, domains (.io, .com), "startup", "company", linkedin.com/company/

**Context to extract:**
- Company name (required)
- Domain (helpful)
- Industry (helpful)
- Location (helpful)

### Type 3: TOPIC
**Triggers:** Abstract concepts, questions, hashtags, "news about", "trends in"

**Context to extract:**
- Keywords (required)
- Time frame (helpful)
- Industry/niche (helpful)

---

## PERSON Search Workflow

### Phase 1: Find & Identify Person

```
1. search_linkedin_users
   - keywords: "[name]"
   - company_keywords: "[company]" if known
   - title: "[role]" if known
   - count: 10

2. If multiple matches → present top 5 for confirmation
3. If YC founder suspected → search_yc_founders(query="[name]")
```

### Phase 2: Deep Profile Analysis

```
1. get_linkedin_profile
   - user: "[username or URL]"
   - with_experience: true
   - with_education: true
   - with_skills: true
   
   CRITICAL: Extract and save:
   - Full URN: urn:li:fsd_profile:ACoAAA...
   - Current company slug/URN
   - Current role & start date

2. get_linkedin_user_posts
   - urn: "[full URN from above]"
   - count: 50
   
3. get_linkedin_user_comments
   - urn: "[full URN]"
   - count: 30

4. get_linkedin_user_reactions
   - urn: "[full URN]"
   - count: 50
```

### Phase 3: Cross-Platform Presence

```
1. search_twitter_users
   - query: "[name] [company]"
   - count: 5
   
2. get_twitter_user (if found)
   - user: "[handle]"

3. get_twitter_user_posts
   - user: "[handle]"
   - count: 100

4. search_reddit_posts
   - query: "[name] OR [username]"
   - count: 20

5. get_instagram_user (if B2C/personal brand)
   - username: "[handle]"

6. duckduckgo_search
   - query: "[name] [company] speaker OR interview OR article OR podcast"
   - count: 10

7. duckduckgo_search
   - query: "[name] site:github.com OR site:medium.com OR site:substack.com"
   - count: 10
```

### Phase 4: CASCADE → Company Analysis

**Always analyze person's current company:**

```
1. get_linkedin_company
   - company: "[company slug from profile]"

2. get_linkedin_company_posts
   - urn: "[company URN]"
   - count: 20

3. parse_webpage
   - url: "[company website]"
   - only_main_content: true

4. parse_webpage
   - url: "[company website]/about"

5. search_yc_companies (check if YC company)
   - query: "[company name]"

6. duckduckgo_search
   - query: "[company] funding news 2024 2025"
   - count: 10
```

### Phase 5: CASCADE → Key Colleagues

```
1. get_linkedin_company_employees
   - companies: ["[company slug]"]
   - keywords: "founder OR CEO OR CTO OR VP"
   - count: 10

2. For top 2-3 executives:
   - get_linkedin_profile (brief)
```

---

## COMPANY Search Workflow

### Phase 1: Find & Identify Company

```
1. search_linkedin_companies
   - keywords: "[company name]"
   - location: "[location]" if known
   - industry: "[industry]" if known
   - count: 10

2. search_yc_companies
   - query: "[company name]"
   - hits_per_page: 20

3. If multiple matches → present options for confirmation
```

### Phase 2: Deep Company Profile

```
1. get_linkedin_company
   - company: "[slug]"
   
   Extract: URN, employee count, industry, description

2. get_linkedin_company_employee_stats
   - urn: "[company URN]"

3. get_linkedin_company_posts
   - urn: "[company URN]"
   - count: 30
```

### Phase 3: Website Intelligence

```
1. parse_webpage (homepage)
   - url: "https://[domain]"
   - only_main_content: true
   - extract_contacts: true

2. parse_webpage (about)
   - url: "https://[domain]/about"

3. parse_webpage (pricing)
   - url: "https://[domain]/pricing"

4. parse_webpage (team/leadership)
   - url: "https://[domain]/team" OR "/about#team"

5. get_sitemap
   - url: "https://[domain]/sitemap.xml"
   - count: 50
```

### Phase 4: Social & Community Presence

```
1. search_twitter_users
   - query: "[company name]"
   - count: 5

2. get_twitter_user
   - user: "[company handle]"

3. get_twitter_user_posts
   - user: "[handle]"
   - count: 50

4. search_twitter_posts
   - query: "[company] OR @[handle]"
   - count: 100

5. search_reddit_posts
   - query: "[company name]"
   - count: 50

6. search_reddit_posts
   - query: "[company name]"
   - subreddit: "[relevant sub]" (e.g., "startups", "SaaS", industry-specific)
   - count: 30

7. search_instagram_posts (if B2C)
   - query: "#[company] OR [company name]"
   - count: 20
```

### Phase 5: CASCADE → Leadership Team Analysis

**Always analyze founders and C-level:**

```
1. get_linkedin_company_employees
   - companies: ["[slug]"]
   - keywords: "founder"
   - count: 10

2. get_linkedin_company_employees
   - companies: ["[slug]"]
   - keywords: "CEO OR CTO OR CPO OR CFO OR COO"
   - count: 10

3. For each founder/C-level (top 5):
   a. get_linkedin_profile
      - with_experience: true
      - with_education: true
      - with_skills: true
   
   b. get_linkedin_user_posts
      - count: 20
   
   c. search_twitter_users → get_twitter_user_posts
      - count: 30

4. search_yc_founders
   - query: "[founder names]"
```

### Phase 6: News & External Intelligence

```
1. duckduckgo_search
   - query: "[company] funding news"
   - count: 10

2. duckduckgo_search
   - query: "[company] launch product announcement"
   - count: 10

3. duckduckgo_search
   - query: "[company] review OR competitor OR alternative"
   - count: 10

4. parse_webpage (top news articles)
   - Parse 3-5 most relevant results

5. If tech company:
   duckduckgo_search
   - query: "[company] site:github.com"
   - count: 5
```

### Phase 7: Y Combinator Check

```
1. search_yc_companies
   - query: "[company name]"

2. If found:
   get_yc_company
   - company: "[slug]"
   
   Extract: batch, status, funding, team size, founders

3. search_yc_founders
   - query: "[company name]"
```

---

## TOPIC Search Workflow

### Phase 1: Web Overview

```
1. duckduckgo_search
   - query: "[topic keywords]"
   - count: 15

2. duckduckgo_search
   - query: "[topic] trends 2024 2025"
   - count: 10

3. duckduckgo_search
   - query: "[topic] news recent"
   - count: 10

4. parse_webpage
   - Parse top 5 most authoritative results
```

### Phase 2: Professional Discussion (LinkedIn)

```
1. search_linkedin_posts
   - keywords: "[topic]"
   - count: 30

2. search_linkedin_companies
   - keywords: "[topic] OR [related terms]"
   - count: 20

3. search_linkedin_users
   - keywords: "[topic] expert OR thought leader"
   - count: 10
```

### Phase 3: Real-time Sentiment (Twitter)

```
1. search_twitter_posts
   - query: "[topic]"
   - count: 100

2. search_twitter_posts
   - query: "[topic] #[hashtag]"
   - count: 50

3. search_twitter_users
   - query: "[topic] expert"
   - count: 10
```

### Phase 4: Community Insights (Reddit)

```
1. search_reddit_posts
   - query: "[topic]"
   - count: 50

2. search_reddit_posts
   - query: "[topic]"
   - subreddit: "[most relevant sub]"
   - count: 30

3. get_reddit_post_comments (on popular posts)
   - Parse top 3 most discussed threads
```

### Phase 5: Visual Content (Instagram)

```
1. search_instagram_posts
   - query: "#[topic_hashtag]"
   - count: 20
```

### Phase 6: Startup Landscape (Y Combinator)

```
1. search_yc_companies
   - query: "[topic]"
   - industries: ["[related industry]"]
   - hits_per_page: 50

2. For top 5 relevant YC companies:
   get_yc_company
   - company: "[slug