Babysitter code-quality-analyzer

Static code analysis, technical debt assessment, engineering velocity metrics

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/venture-capital/skills/code-quality-analyzer" ~/.claude/skills/a5c-ai-babysitter-code-quality-analyzer && rm -rf "$T"

manifest: library/specializations/domains/business/venture-capital/skills/code-quality-analyzer/SKILL.md

Code Quality Analyzer

Overview

The Code Quality Analyzer skill provides detailed code-level analysis for technical due diligence. It performs static code analysis, assesses technical debt, and evaluates engineering team velocity to understand code health and development productivity.

Capabilities

Static Code Analysis

Run automated code quality checks
Identify code smells and anti-patterns
Measure code complexity metrics
Detect potential bugs and vulnerabilities

Technical Debt Assessment

Quantify technical debt backlog
Identify high-priority refactoring needs
Assess test coverage and quality
Evaluate documentation completeness

Engineering Velocity Metrics

Measure deployment frequency
Track lead time for changes
Analyze cycle time and throughput
Assess sprint velocity trends

Code Health Indicators

Analyze code churn patterns
Review pull request metrics
Assess code review practices
Evaluate dependency management

Usage

Analyze Code Quality

Input: Repository access, analysis parameters
Process: Run static analysis, aggregate metrics
Output: Code quality report, issue summary

Assess Technical Debt

Input: Codebase access, debt categorization
Process: Inventory debt, estimate remediation
Output: Technical debt assessment, prioritization

Measure Engineering Velocity

Input: Git history, project management data
Process: Calculate velocity metrics
Output: Velocity report, trend analysis

Review Code Health

Input: Repository data, team practices
Process: Analyze patterns, compare benchmarks
Output: Code health scorecard, recommendations

Key Metrics

Metric	Description	Target Range
Test Coverage	% of code covered by tests	70-90%
Code Complexity	Cyclomatic complexity average	< 10
Tech Debt Ratio	Debt remediation time / dev time	< 5%
Deployment Frequency	Deployments per week	Daily to weekly
Change Failure Rate	% of deployments causing issues	< 15%

Integration Points

Technical Due Diligence: Detailed code analysis for DD
Tech Stack Scanner: Complement architecture review
Technical Assessor (Agent): Support agent analysis
IP Patent Analyzer: Code-level IP assessment

Analysis Tools Integration

SonarQube for code quality
CodeClimate for maintainability
GitHub/GitLab analytics
Jira/Linear for velocity data
Custom scripts for specific checks

Best Practices

Calibrate expectations by company stage
Focus on trends over absolute numbers
Consider context of rapid iteration
Balance debt against velocity needs
Assess relative to team size and resources