Honey - Cuts your token usage by 70%

The problem

LLMs are naturally verbose, often generating speculative code and conversational filler that inflates token usage. This "agent tax" leads to higher costs, slower responses, and cluttered contexts that make debugging harder.

What it does

• Enforces YAGNI (You Ain't Gonna Need It) principles to prevent speculative generality and redundant abstractions.
• Prioritizes language-native idioms and standard libraries over hand-rolled helpers or unnecessary dependencies.
• Eliminates conversational filler, hedging, and wind-up/wind-down prose to keep responses technical and dense.
• Compresses agent-to-agent communication using minified JSON or columnar formats to maximize token efficiency.
• Optimizes input costs by using targeted reads, greps, and outlines instead of pulling entire files into context.

Why this beats prompting it yourself

System prompts often fail to curb the deep-seated "helpful" bias of LLMs, which tends to resurface during complex tasks. This skill provides a structured framework for different intensities, ensuring the agent remains terse even when the workload scales, without sacrificing safety-critical validation or error handling.

Use cases

• Reducing API costs during autonomous coding loops or long-running agent tasks.
• Refactoring legacy code into clean, standard-library-first implementations.
• Generating high-density data payloads for sub-agents to process.
• Quickly navigating large codebases using outline-only reads and targeted grep patterns.

Known limitations

Requires the user to manually step up intensity if a learner needs deep explanations. Does not compress auth secrets, financial data, or destructive operations where schema validation is required.