When should I use the Code Discovery skill?

Use this skill during the investigation or design phase of a task to understand how code works, find where a bug was introduced, or identify existing patterns to follow for consistency.

What are 'Confidence Levels' in findings?

It is a framework used to categorize information as High (direct evidence), Medium (inferred from patterns), or Low (possible based on architecture) to ensure clear communication of facts vs. assumptions.

Why does this skill prioritize file:line references?

References make findings verifiable and actionable, allowing developers to jump directly to the code being discussed in their IDE or pull request reviews.

How does it improve git log analysis?

It provides configurable timeframes and specific command patterns to filter logs by file path, commit message, or directory, making it easier to pinpoint relevant changes.

Can this be used for Forge planning files?

No, the skill documentation specifically notes that it should not be used for Forge planning file reading; it is intended for research and investigation of the primary codebase.

Code Discovery & Investigation

Name: Code Discovery & Investigation
Author: flox

byflox

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Herramientas para Desarrolladores

Simplifies codebase exploration and technical research using structured git analysis and systematic code-reading strategies.

The Code Discovery skill empowers developers to perform deep codebase research with a focus on evidence over assumptions. It streamlines complex investigations—such as tracking down regressions, finding implementation precedents, or mapping out system architecture—by utilizing specific git log patterns, tracing call chains, and leveraging project context files. By enforcing the use of precise file:line references and confidence level assessments, it ensures that technical proposals and debugging sessions are grounded in the actual state of the implementation rather than speculation.

Características Principales

01Systematic implementation tracing from entry points to deep-level handlers

02Standardized documentation using precise file:line reference patterns

031 GitHub stars

04Context-aware git log analysis for tracking regressions and historical changes

05Pattern-based search for finding similar features and architectural precedents

06Evidence-based reporting with High, Medium, and Low confidence levels

Casos de Uso

01Performing root cause analysis for suspected regressions by scanning recent commit history

02Conducting design research to find existing patterns before implementing new features

03Onboarding to unfamiliar modules by tracing call chains and reading system context files

Características Principales

01Systematic implementation tracing from entry points to deep-level handlers

02Standardized documentation using precise file:line reference patterns

031 GitHub stars

04Context-aware git log analysis for tracking regressions and historical changes

05Pattern-based search for finding similar features and architectural precedents

06Evidence-based reporting with High, Medium, and Low confidence levels

Casos de Uso

01Performing root cause analysis for suspected regressions by scanning recent commit history

02Conducting design research to find existing patterns before implementing new features

03Onboarding to unfamiliar modules by tracing call chains and reading system context files