Can Oracle help me automate repetitive tasks?

Yes, Oracle monitors your interactions to detect repeated, deterministic patterns and can suggest or generate local scripts to handle those tasks without using LLM tokens.

How does Oracle improve token efficiency?

Oracle uses a tiered context loading strategy, injecting only high-priority project info or task-relevant knowledge instead of dumping the entire history into the AI's context window.

Where is the project knowledge stored?

All project knowledge is stored locally in a structured .oracle/ directory within your repository, ensuring it is human-readable, private, and version-controllable.

Does Oracle work with other Claude Code plugins?

Yes, Oracle is designed to integrate with other skills like Guardian and Style-Master, providing them with the necessary historical context to perform their specific tasks better.

Oracle: Project Memory & Learning

Name: Oracle: Project Memory & Learning
Author: Overlord-Z

byOverlord-Z

Learning & Documentation

Maintains persistent project context and learns from user feedback to eliminate repetitive mistakes and optimize token usage.

About

Oracle is a sophisticated memory and learning system for Claude Code that acts as a centralized repository for institutional project knowledge. It meticulously tracks session histories, records architectural decisions, and learns from user corrections to ensure that project-specific patterns and preferences are never forgotten. By implementing a tiered context injection strategy, Oracle keeps Claude informed and aligned with your team's standards without bloating the context window, facilitating more efficient and accurate development over long-term projects.

Key Features

Automated session recording and activity tracking
0 GitHub stars
Intelligent learning from corrections and historical mistakes
Identification of automation opportunities for repetitive tasks
Human-readable structured storage for patterns, gotchas, and preferences
Tiered context injection for maximum token efficiency

Use Cases

Maintaining architectural consistency across large-scale software projects
Onboarding new developers by providing a searchable archive of project decisions
Preventing recurring bugs by flagging historical gotchas and previous corrections

About

Key Features

Automated session recording and activity tracking
0 GitHub stars
Intelligent learning from corrections and historical mistakes
Identification of automation opportunities for repetitive tasks
Human-readable structured storage for patterns, gotchas, and preferences
Tiered context injection for maximum token efficiency

Use Cases

Maintaining architectural consistency across large-scale software projects
Onboarding new developers by providing a searchable archive of project decisions
Preventing recurring bugs by flagging historical gotchas and previous corrections