What is considered 'fluff' in an LLM artifact?

Fluff includes any content that provides human comfort or credibility without adding functional value, such as persona statements like 'You are an expert', marketing buzzwords, or decorative epigraphs.

How does removing fluff improve LLM performance?

By removing fluff, you maximize the effective context window, reduce latency, lower token costs, and prevent the model from being distracted by irrelevant conversational filler.

Can I use the Fluff Detector in my automated workflows?

Yes, it is designed for CI/CD integration. It provides standard exit codes (0 for clean, 2 for errors) to block PRs that contain excessive token waste.

Does it flag legitimate technical references?

The detector distinguishes between functional style definitions (e.g., 'DHH-style Rails') and empty attribution signals (e.g., 'Inspired by DHH') to minimize false positives.

LLM Fluff Detector

Name: LLM Fluff Detector
Author: majesticlabs-dev

bymajesticlabs-dev

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개발자 도구

Identifies and removes unnecessary conversational padding, marketing language, and redundant tokens from LLM instructions and artifacts.

소개

The Fluff Detector is a specialized utility designed to audit and optimize prompts, agent instructions, and Claude skills for machine execution. Unlike content written for humans, LLM artifacts should be dense, functional, and devoid of social niceties or credibility signals. This skill scans your codebase for 'token waste' such as persona statements, decorative quotes, hedging language, and marketing buzzwords that clutter context windows without improving model performance. By acting as a quality gate, it ensures your AI interactions are efficient, direct, and cost-effective.

주요 기능

Detects attribution signals and decorative quotes that waste context
Highlights hedging language and zero-information filler phrases
Provides actionable rephrasing suggestions to minimize token usage
Identifies redundant persona statements and identity-based fluff
Flags marketing buzzwords and superlatives that lack functional value
18 GitHub stars

사용 사례

Integrating into CI/CD pipelines as a quality gate for prompt engineering
Refining new Claude Code skills to ensure they are optimized for model consumption
Auditing existing AI agent instructions for token efficiency and clarity

소개

주요 기능

Detects attribution signals and decorative quotes that waste context
Highlights hedging language and zero-information filler phrases
Provides actionable rephrasing suggestions to minimize token usage
Identifies redundant persona statements and identity-based fluff
Flags marketing buzzwords and superlatives that lack functional value
18 GitHub stars

사용 사례

Integrating into CI/CD pipelines as a quality gate for prompt engineering
Refining new Claude Code skills to ensure they are optimized for model consumption
Auditing existing AI agent instructions for token efficiency and clarity