Does Budget Mastery limit Claude's ability to be thorough?

No. The skill focuses on finding the optimal path to value. It avoids unnecessary padding while ensuring that necessary depth and context are preserved for high-quality results.

What is the 'opus-distillatus' agent mentioned in the skill?

It is a specialized interaction mode triggered by this skill that focuses on high-density synthesis, where every token is strategically chosen to provide maximum insight.

Is this skill only for reducing costs?

While it helps with token budgets, its primary goal is excellence. It treats efficiency as a 'taste' or quality standard, leading to clearer thinking and better-structured reasoning.

How does this skill use the memory system?

It records both user insights and its own successful efficiency patterns. By recalling these later, it can solve similar problems faster and more accurately without starting from scratch.

Budget Mastery

Name: Budget Mastery
Author: bogheorghiu

bybogheorghiu

生产力与工作流

Optimizes research and analysis workflows to deliver high-signal insights with maximum token efficiency.

关于

Budget Mastery is an identity-driven skill for Claude Code that prioritizes 'optimal over minimal' execution. It excels at finding the shortest path to genuine understanding by identifying essential moves, crystallizing multiple data sources into concise insights, and leveraging a persistent memory system to avoid redundant computations. Designed for the 'opus-distillatus' pattern, it ensures that even under token constraints, the reasoning process remains transparent, high-quality, and valuable for human review.

主要功能

Integrated memory recall for past efficiency patterns
0 GitHub stars
Opus-distillatus agent pattern for focused deep-dives
Automated discovery of recurring high-value interaction patterns
Strategic depth-vs-breadth judgment for resource management
Token-efficient information crystallization and synthesis

使用场景

Documenting efficient problem-solving paths for future team review
Maintaining high-quality analysis during long sessions with limited token budgets
Synthesizing multiple research documents into crystallized technical insights

关于

主要功能

Integrated memory recall for past efficiency patterns
0 GitHub stars
Opus-distillatus agent pattern for focused deep-dives
Automated discovery of recurring high-value interaction patterns
Strategic depth-vs-breadth judgment for resource management
Token-efficient information crystallization and synthesis

使用场景

Documenting efficient problem-solving paths for future team review
Maintaining high-quality analysis during long sessions with limited token budgets
Synthesizing multiple research documents into crystallized technical insights