code-analysis

Discover connected components from a single legacy codebase entry point.

Updated Mar 22, 2026

Search Tags:#analysis #drift-detection #code-analysis #auto-discovery #legacy-code #fingerprint

Ready to run

npx skills add https://github.com/hariinfo/agentic-code-template --skill code-analysis-hariinfo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: code-analysis
Source: https://github.com/hariinfo/agentic-code-template/tree/main/.claude/skills/code-analysis
Command: npx skills add https://github.com/hariinfo/agentic-code-template --skill code-analysis-hariinfo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Legacy codebases often suffer from opaque dependencies and tangled architectures; this Skill automates entry-point analysis and auto-discovery of connected components across every layer to reveal structure, inventories, and drift signals.

Core Features & Use Cases

Auto-discovery across all layers (presentation, business logic, data access, messaging, security, and configuration) starting from a single entry point
Generates three artifacts: fingerprint.json, analysis.md, and source-extracts.md to support drift detection and deep inspection
Appends shared resources to contracts.json for cross-entry-point governance and reuse

Quick Start

Run the trigger /analyze-entry <entry-point-path> to begin automatic discovery of all connected legacy components.

Frequently Asked Questions about code-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I map all dependencies from a single entry point in a legacy codebase?▼

Legacy codebase analysis from a single entry point automatically discovers connected components across layers. It traverses imports, method calls, SQL, and configuration to map presentation, business logic, and data access dependencies.

What is the best way to detect architectural drift in legacy code?▼

Architectural drift detection in legacy code is handled by generating fingerprint.json and analysis.md artifacts. These files map cross-layer components and highlight structural inconsistencies discovered during the auto-discovery process.

How do I automatically discover SQL stored procedures and messaging components?▼

Auto-discovery of SQL stored procedures and messaging components works by traversing method calls and deployment references from a designated entry point. It maps these data access and messaging layers into source-extracts.md.

Can I trace code paths across presentation, business logic, and data access layers?▼

Tracing code paths across presentation, business logic, and data access layers is supported by end-to-end mapping. The analysis traverses dependency injection and imports to generate a comprehensive structural inventory.

Do I need to manually configure dependency tracking for legacy code?▼

Manual configuration for dependency tracking in legacy code is unnecessary. The auto-discovery mechanism automatically traverses imports, DI, SQL, and configuration files starting from a single specified entry point path.

How does cross-entry-point governance work for shared resources?▼

Cross-entry-point governance for shared resources works by appending discovered components to contracts.json. This supports tracking and reuse across multiple entry-point analyses within the same legacy codebase.