Israeli Tech Nano-Influencers Database

Israeli Tech Nano-Influencers Database

A SQLite database with direct sqlite3 command-line access for managing Israeli tech nano-influencer data. Pure SQL with no abstractions.

Features

• Direct sqlite3 Access: Execute any SQL query using the sqlite3 CLI
• Schema Inspection: View database schema and table structures
• Flexible: Craft any query you need with full SQL power
• Version Controlled: Database file is tracked in git for easy collaboration

Database Schema

Tables

influencers - Single table for all influencers (active and excluded)

Profile & Identity:

• twitter_handle (TEXT PRIMARY KEY) - Twitter/​X username
• name (TEXT NOT NULL) - Full name
• role (TEXT) - Professional role/​title
• focus (TEXT) - Areas of expertise or interest
• background (TEXT) - Professional background
• profile_url (TEXT) - Link to Twitter/​X profile

Engagement & Activity:

• recent_activity (TEXT) - Description of recent posts/​activity
• engagement_potential (TEXT) - Assessment of engagement value (HIGH/​MEDIUM/​LOW)
• last_tweet_date (TEXT) - Date of most recent tweet
• last_reply_date (TEXT) - Date of most recent reply

Location & Language:

• location (TEXT) - Geographic location
• language (TEXT) - Languages used in content
• hebrew_writer (BOOLEAN) - Whether they write in Hebrew (0/​1)

X API Metrics (matching UserInfoResponse model):

• followers (INTEGER) - Number of followers
• following (INTEGER) - Number of accounts they follow
• statuses_count (INTEGER) - Total number of tweets/​statuses
• media_count (INTEGER) - Total media items posted

Discovery & Tracking:

• discovery_path (TEXT) - How the influencer was discovered/​found (e.g., web search query, xai-grok search, website URL, referral path, Twitter list, recommendation from another influencer, etc.)
• rationale (TEXT) - Why this influencer was added to the database (e.g., specific expertise, notable projects, unique perspective, community influence, content quality, etc.)
• added_date (TEXT NOT NULL) - Date added to database (ISO 8601 format)
• last_verified_date (TEXT) - Date profile was last verified (ISO 8601 format)

Exclusion Management:

• excluded (BOOLEAN DEFAULT 0) - Whether excluded from active list (0=active, 1=excluded)
• excluded_date (TEXT) - Date of exclusion (ISO 8601 format, nullable)
• exclusion_reason (TEXT) - Reason for exclusion (nullable)

Metadata:

• notes (TEXT) - Additional notes or observations
• created_at (TIMESTAMP DEFAULT CURRENT_TIMESTAMP) - Record creation timestamp
• updated_at (TIMESTAMP DEFAULT CURRENT_TIMESTAMP) - Last update timestamp (auto-updated)

Indexes:

• idx_influencers_location - Fast queries by location
• idx_influencers_hebrew_writer - Fast queries by language
• idx_influencers_followers - Fast queries by follower count
• idx_influencers_excluded - Fast queries for active vs excluded

Using sqlite3

The database is accessed using the sqlite3 command-line tool (pre-installed on most systems).

Basic Usage

# Open database in interactive mode
sqlite3 influencers.db

# Execute a single query
sqlite3 influencers.db "SELECT * FROM influencers LIMIT 5"

# Get JSON output
sqlite3 influencers.db ".mode json" "SELECT * FROM influencers LIMIT 5"

Common sqlite3 Commands

Meta commands (start with .):

.tables                    # List all tables
.schema influencers        # Show table schema
.mode json                 # Set output to JSON format
.mode column               # Set output to column format
.headers on                # Show column headers
.quit                      # Exit sqlite3

SQL Query Examples

SELECT Queries

# Get all influencers
sqlite3 influencers.db "SELECT * FROM influencers"

# Get specific influencer
sqlite3 influencers.db "SELECT * FROM influencers WHERE twitter_handle = 'oriSomething'"

# Get with JSON output
sqlite3 influencers.db -json "SELECT * FROM influencers WHERE location LIKE '%Tel Aviv%'"

# Count by location
sqlite3 influencers.db "SELECT location, COUNT(*) as count FROM influencers GROUP BY location"

INSERT Queries

# Add new influencer
sqlite3 influencers.db "INSERT INTO influencers (twitter_handle, name, location, followers, hebrew_writer, added_date, last_verified_date) VALUES ('test_user', 'Test User', 'Tel Aviv', 1500, 1, '2025-10-26', '2025-10-26')"

# Add with more fields
sqlite3 influencers.db "INSERT INTO influencers (twitter_handle, name, role, focus, location, language, followers, following, statuses_count, media_count, hebrew_writer, engagement_potential, discovery_path, added_date) VALUES ('example', 'Example User', 'Developer', 'AI/​ML', 'Israel', 'Hebrew, English', 2000, 500, 1500, 300, 1, 'HIGH', 'web search: Israeli AI developers', '2025-10-26')"

UPDATE Queries

# Update follower count
sqlite3 influencers.db "UPDATE influencers SET followers = 2000 WHERE twitter_handle = 'test_user'"

# Update multiple X API metrics
sqlite3 influencers.db "UPDATE influencers SET followers = 2500, following = 600, statuses_count = 2000, media_count = 400 WHERE twitter_handle = 'test_user'"

DELETE Queries

# Delete specific influencer
sqlite3 influencers.db "DELETE FROM influencers WHERE twitter_handle = 'test_user'"

Architecture

.claude/​skills/​influencer-db/
├── SKILL.md              # This documentation
└── src/
    └── schema.sql       # Database schema (for reference)
influencers.db           # SQLite database (version controlled in git)

Tips for Agents

• Use JSON output: Add -json flag for JSON output: sqlite3 influencers.db -json "SELECT ..."
• Use transactions: For multiple operations, wrap in transaction (BEGIN/​COMMIT)
• Check constraints: twitter_handle is PRIMARY KEY - handle conflicts gracefully
• Use indexes: location, followers, hebrew_writer, and excluded are indexed for fast queries
• Track discovery: Always populate discovery_path (how found) and rationale (why added) when adding new influencers to track sourcing and decision-making