data-analysis

name	data-analysis
description	AI-powered data analysis for Empathy Ledger - themes, quotes, story suggestions, transcript analysis.

Data Analysis

Patterns for AI-powered analysis: themes, quotes, summaries, and story suggestions.

When to Use

• Adding quotes to story cards
• Implementing story suggestions/related content
• Building theme-based filtering or search
• Creating analytics dashboards
• Integrating AI analysis results

Quick Reference

Analysis Pipeline

Transcript → AI Analysis → themes[], key_quotes[], ai_summary
Story → Connections → Related Stories, Suggested Content

Key Tables

Table	Analysis Fields
transcripts	themes, key_quotes, ai_summary, ai_processing_status
stories	themes, cultural_tags, featured_quote
storytellers	expertise_themes, connection_strength

Theme Categories

• cultural: identity, heritage, tradition, language, ceremony
• family: kinship, elders, children, ancestors, community
• land: country, connection, seasons, wildlife, sacred-sites
• resilience: survival, adaptation, strength, healing, hope
• knowledge: wisdom, teaching, learning, stories, dreams

Common Queries

-- Stories with matching theme
SELECT * FROM stories WHERE themes && ARRAY['identity', 'heritage'];

-- Theme frequency
SELECT unnest(themes) as theme, count(*) FROM stories GROUP BY theme ORDER BY count DESC;

API Endpoints

Endpoint	Purpose
POST /api/transcripts/{id}/analyze	Trigger AI analysis
GET /api/stories/{id}/suggestions	Get related stories
GET /api/themes	List themes with counts

Reference Files

Topic	File
Code patterns	refs/analysis-patterns.md
SQL queries	refs/supabase-queries.md
Theme hierarchy	refs/theme-taxonomy.md
Sync status	refs/sync-status.md

Related Skills

• database-navigator - Database exploration
• supabase-connection - Database clients
• design-component - UI patterns for analysis display