competitor-analysis

name	competitor-analysis
description	Scrape competitor websites to extract features and estimate reimplementation costs in developer hours. Store results in SQLite/Turso for visualization. Use when analyzing competitor products, extracting feature lists from URLs, estimating development effort, or building competitive intelligence databases.

Competitor Analysis

Extract features from competitor websites and estimate reimplementation costs.

Workflow

1. Initialize database (first time only)

   python scripts/init_db.py <path>.db
   # or for Turso:
   python scripts/init_db.py libsql://your-db.turso.io --token <TOKEN>
   

2. Scrape competitor URL(s) using WebFetch tool

3. Extract features from page content:

   • Identify distinct product capabilities
   • Note feature name, description, source URL
   • Assign category (core/integration/ui-ux/security/analytics/collaboration/automation/admin)

4. Estimate complexity for each feature:

   • simple (2-8h): Standard CRUD, basic UI, config toggles
   • medium (8-24h): Multi-step flows, 3rd-party integrations, dashboards
   • complex (24-80h): Real-time collab, ML/AI, custom engines
   • See references/cost-estimation.md for detailed rubrics

5. Store in database:

   -- Add competitor
   INSERT INTO competitors (name, url) VALUES ('Acme', 'https://acme.com');
   
   -- Add feature
   INSERT INTO features (competitor_id, name, description, category_id, complexity, estimated_hours_min, estimated_hours_max, source_url)
   VALUES (1, 'Real-time notifications', 'Push notifications across devices',
           (SELECT id FROM categories WHERE name = 'core'), 'medium', 12, 20, 'https://acme.com/features');
   
   -- Log analysis run
   INSERT INTO analysis_runs (competitor_id, urls_analyzed, features_found)
   VALUES (1, '["https://acme.com/features"]', 15);
   

6. Query results:

   SELECT * FROM feature_summary WHERE competitor_name = 'Acme';
   SELECT * FROM competitor_cost_summary;
   

7. Generate interactive dashboard:

   python scripts/generate_dashboard.py <path>.db [output_dir] [--serve]
   

   Opens browser with feature selection UI. Select features, filter by category/complexity, export as JSON/CSV.

Database Schema

Table	Purpose
competitors	Company name, URL, timestamps
categories	Feature categories (seeded with defaults)
features	Extracted features with complexity and hour estimates
analysis_runs	Tracks scraping sessions
feature_summary	View joining features with competitor/category names
competitor_cost_summary	View with aggregated hour totals per competitor

Output Example

After analyzing a competitor:

Competitor: Acme Corp (https://acme.com)
Features found: 23

| Feature | Category | Complexity | Hours (min-max) |
|---------|----------|------------|-----------------|
| SSO/SAML | security | complex | 32-48 |
| Dashboard builder | analytics | complex | 40-60 |
| Slack integration | integration | medium | 12-20 |
| Dark mode | ui-ux | simple | 4-6 |

Total estimate: 280-420 hours