---
name: tech-stack-scanner-511f733898b9
description: Automated technical architecture review, security assessment, scalability analysis
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
  - WebFetch
metadata:
  specialization: venture-capital
  domain: business
  skill-id: vc-skill-013
graph:
  domains: [domain:venture-capital]
  skillAreas: [skill-area:investment-analysis, skill-area:valuation-analysis, skill-area:financial-modeling]
  roles: [role:financial-analyst]
---

# Tech Stack Scanner

## Overview

The Tech Stack Scanner skill provides automated technical architecture review for venture capital due diligence. It assesses technology choices, infrastructure scalability, security posture, and technical debt to inform investment decisions.

## Capabilities

### Architecture Assessment
- Evaluate technology stack choices
- Assess microservices vs. monolith architecture
- Review database and data store choices
- Analyze API design and integration patterns

### Infrastructure Review
- Evaluate cloud infrastructure (AWS, GCP, Azure)
- Assess scalability and auto-scaling capabilities
- Review disaster recovery and backup strategies
- Analyze infrastructure costs and efficiency

### Security Assessment
- Review authentication and authorization
- Assess data encryption practices
- Check for common vulnerabilities
- Evaluate security monitoring and incident response

### Scalability Analysis
- Model load handling capabilities
- Identify potential bottlenecks
- Assess horizontal vs. vertical scaling
- Review performance optimization practices

## Usage

### Scan Tech Stack
```
Input: Company technical documentation, access credentials
Process: Automated scanning, architecture analysis
Output: Tech stack assessment, architecture diagram
```

### Assess Security Posture
```
Input: Security policies, system access
Process: Security scanning, policy review
Output: Security assessment, vulnerability report
```

### Evaluate Scalability
```
Input: Current load, growth projections, infrastructure
Process: Load modeling, bottleneck analysis
Output: Scalability assessment, recommendations
```

### Review Infrastructure Costs
```
Input: Cloud billing data, infrastructure inventory
Process: Cost analysis, optimization identification
Output: Cost assessment, efficiency recommendations
```

## Assessment Categories

| Category | Key Areas |
|----------|-----------|
| Architecture | Stack choices, patterns, technical debt |
| Infrastructure | Cloud, scaling, DR, monitoring |
| Security | Auth, encryption, vulnerabilities, compliance |
| Performance | Response times, throughput, optimization |
| DevOps | CI/CD, deployment, testing, monitoring |

## Integration Points

- **Technical Due Diligence**: Core skill for tech DD
- **Code Quality Analyzer**: Detailed code-level analysis
- **IP Patent Analyzer**: Coordinate IP and tech review
- **Technical Assessor (Agent)**: Support DD agent

## Technology Checklist

- Frontend frameworks and mobile platforms
- Backend languages and frameworks
- Databases and data storage
- Cloud infrastructure and services
- Third-party integrations and dependencies
- Development and deployment tools
- Monitoring and observability

## Best Practices

1. Request architecture documentation upfront
2. Supplement automated scanning with interviews
3. Focus on scalability relative to growth plans
4. Assess technical debt pragmatically
5. Consider technology choices in industry context


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing, Product Management, Sales
Roles: Business Development, Data Analyst, Growth Marketer, Marketing Analyst, Product Manager
Specialties: API, Backend
Tools: Aws, Azure, Gcp
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 785066631db78e2aef5c6022c793eea17e6679f45dc204ffe0e01d6b0731fa34