Gives Claude Code deep understanding of codebase evolution through 10 integrated analysis capabilities, transforming static code reading into historical context awareness for faster, smarter development decisions.
git clone https://github.com/udaybandaru/code-archaeologist.gitCode Archaeologist is a production-ready skill system that enables Claude Code to understand codebase evolution through git history analysis, hotspot detection, and architectural pattern tracking. It analyzes folder and file-level changes, identifies technical debt, maps dependency evolution, and generates design intelligence based on historical context. The system distinguishes meaningful commits from development noise using intelligent weighting, reducing analysis noise by 35% and accelerating research from 2-4 hours to 20 seconds. Teams with fast-moving codebases, engineers refactoring legacy code, and new contributors all benefit from accessing the historical context that acts like a tenured senior engineer's knowledge of why code was structured certain ways and what alternatives were considered.
From any git repository, run `python3 .claude/skills/code_archaeologist/skill.py suggest` for recommendations or `python3 .claude/skills/code_archaeologist/skill.py smart src/` for analysis. Generate design docs with historical intelligence using `python3 .claude/skills/code_archaeologist/skill.py design src/ --feature "Feature Name" --description "What it does"`.
Analyze hotspots in frequently-modified functions to identify stability risks before implementing features
Generate design docs with historical context and architectural constraints for new feature development
Compare evolution patterns between components to understand maturity differences and refactoring priorities
Detect circular dependencies and architectural anti-patterns that emerged over time
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/udaybandaru/code-archaeologistCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a code archaeologist. Analyze the evolution of [CODEBASE] in [PROGRAMMING_LANGUAGE] from [START_DATE] to [END_DATE]. Identify key architectural changes, major refactoring efforts, and significant technical debt accumulation. Provide a timeline of these changes with code examples and explain the motivations behind each change. Focus on understanding the intent of the original developers and how the codebase has adapted to new requirements over time.
# Codebase Evolution Analysis: E-Commerce Platform (2015-2023)
## Key Architectural Changes
### 2017: Microservices Transition
- **Change**: Monolithic architecture split into microservices
- **Motivation**: Scalability issues with increasing user base
- **Code Example**:
```python
# Original monolithic service
class OrderService:
def __init__(self):
self.inventory = InventoryService()
self.payment = PaymentService()
```
### 2019: Introduction of Event Sourcing
- **Change**: Event-driven architecture implemented
- **Motivation**: Need for better audit trails and data consistency
- **Code Example**:
```python
# Event sourcing implementation
class OrderEventHandler:
def handle_order_created(self, event):
# Process order creation event
self.publish_event('OrderCreatedEvent', event)
```
## Major Refactoring Efforts
### 2020: Payment Processing Refactor
- **Reason**: PCI compliance requirements
- **Impact**: 3-week downtime for payment subsystem
## Technical Debt Accumulation
### 2018: Legacy Payment Gateway Integration
- **Debt**: Tight coupling with obsolete payment provider
- **Consequence**: Increased maintenance costs and migration challenges
## Evolution Timeline
1. **2015**: Initial monolithic architecture
2. **2017**: Microservices transition begins
3. **2019**: Event sourcing implemented
4. **2020**: Payment processing refactor
5. **2022**: Migration to cloud-native infrastructure
Auto-transcribe meetings and generate action items
AI assistant built for thoughtful, nuanced conversation
Get more done every day with Microsoft Teams – powered by AI
Automate your spreadsheet tasks with AI power
Agentic AI Workflow platform
Connected workspace for docs, wikis, and projects
Take a free 3-minute scan and get personalized AI skill recommendations.
Take free scan