optimizer▌

Analyzes markdown files and suggests token-reduction optimizations while preserving clarity. \n \n Counts tokens using a 4-character-per-token approximation and identifies token-wasting patterns including emojis, verbosity, duplication, and oversized code blocks \n Generates a detailed suggestions table showing issue location, description, recommended fix, and estimated token savings \n Targets SKILL.md files for under 500 tokens and reference files for under 1000 tokens \n Provides recommendati

Optimize LinkedIn profiles for recruiter visibility, searchability, and job opportunity attraction. \n \n Provides section-by-section optimization guidance covering headline, About section, Experience, Skills, and Featured content with specific formulas and examples \n Includes keyword strategy for recruiter searches, profile completeness checklist, and recruiter visibility settings configuration \n Explains key differences between LinkedIn and resume formats, with structured approach to syncing

Reduces token count in prompts and docs by 20–40% using 41 research-backed optimization rules. \n \n Applies rules across six categories: Claude behavior, token efficiency, structure, reference integrity, perception, and LLM comprehension \n Three optimization modes: light (text cleanup only), medium (balanced restructuring, default), and deep (aggressive compression and rephrasing) \n Processes single files or entire directories of markdown files, with built-in quality checks for reference vali

Analyze a draft prompt, critique it, match it to ECC ecosystem components, and output a complete optimized prompt the user can paste and run.

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Analyze and rewrite tweets to maximize reach using Twitter's open-source recommendation algorithms. \n \n Applies Real-graph, SimClusters, TwHIN, and Tweepcred models to identify optimization opportunities and explain algorithmic ranking decisions \n Identifies engagement signals (likes, replies, retweets, bookmarks) and negative signals (blocks, reports, mutes) that affect tweet distribution \n Rewrites tweets to trigger specific engagement patterns, target community resonance, and strengthen t

Create optimized Nginx configurations for high-performance web serving and reverse proxying.

Implements the NOWAIT technique from the paper "Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency" (Wang et al., 2025).

Transform vague prompts into precise, testable specifications using EARS methodology and domain theory grounding. \n \n Converts natural language requirements into five EARS patterns (ubiquitous, event-driven, state-driven, conditional, unwanted behavior) with explicit triggers, conditions, and measurable criteria \n Applies relevant domain frameworks (GTD, BJ Fogg, Gestalt, Zero Trust, etc.) to enhance requirements with established best practices \n Generates structured prompts using Role/Skill

Specialized in enhancing script performance, user experience, and visual presentation on TradingView.