examine

Examine source code to reveal data representations, data flow, and decision logic.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/fpontejos/claude-helpers --skill examine
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: examine
Source: https://github.com/fpontejos/claude-helpers/tree/main/skills/examine
Command: npx skills add https://github.com/fpontejos/claude-helpers --skill examine

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers systematically understand and audit codebases by examining data representations, data flow, and decision logic across modules, enabling faster insights and higher quality.

Core Features & Use Cases

  • Multi-perspective analysis: examine data representations (schemas, models), data flow (transformations), and decision logic (validators, branching) across the codebase.
  • Parallel subagent support: speeds up large-scale examinations by running data, flow, and decisions analyses concurrently.
  • Flexible scoping: target modules, files, or feature areas with guidance on scope resolution and output integration.
  • Documentation alignment: helps verify consistency between code behavior and docs, and can feed documentation-generation workflows.

Quick Start

Use the examine skill to audit a module by analyzing data representations, data flow, and decision logic. Example: /examine all src/utils/ to run all three perspectives in parallel.

Frequently Asked Questions about examine

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

FAQPage Schema
How do I analyze data flow and decision logic across a codebase module?

To analyze data flow and decision logic, you can examine source code to reveal data representations, transformations, and control structures. This allows you to systematically audit modules by extracting schemas, validators, and branching logic.

What is the best way to audit source code for data representations and control structures?

Auditing source code for data representations and control structures is best done through structured extraction of models, transformations, and validators. This approach enforces systematic analysis to inform refactoring and documentation workflows.

Can I run parallel analysis on data flow and decision logic for large files?

Yes, you can run parallel analysis on data flow and decision logic for large files. The examination process supports concurrent subagent analysis to speed up large-scale audits across modules and feature areas.

How do I verify code behavior consistency with documentation during a refactor?

To verify code behavior consistency with documentation during a refactor, examine the source code to extract data representations and decision logic. This helps align actual code behavior with existing documentation.

Does this code analysis approach support scoping to specific feature areas?

Yes, this code analysis approach supports flexible scoping to specific feature areas. You can target modules, files, or scopes with guidance on scope resolution and output integration for precise examinations.

When do I need to systematically examine decision logic and data transformations?

You need to systematically examine decision logic and data transformations when auditing a codebase, preparing for a refactor, or generating documentation. This ensures accurate extraction of validators and control structures.