Content creation is often a guessing game, leading to stagnant growth. This project offers an "auto-evolving ops expert" that learns an individual account's patterns to predict content performance, enabling creators to systematically "calculate" success rather than rely on intuition. It aims to accelerate growth by providing a personalized, data-driven strategy, claiming significant follower increases within a short period.

How It Works

The system transforms content creation into a calibration experiment. It guides users through a cycle of scoring content drafts, making blind predictions, publishing, and then reviewing performance three days post-publication. This iterative process fuels the evolution of a personalized scoring formula derived from the user's historical data. Unlike generic AI assistants, this tool acts as a dedicated "operations expert" for a specific account, continuously updating its understanding and improving prediction accuracy over time.

Quick Start & Requirements

Installation involves cloning the repository and running an install script:

git clone https://github.com/XBuilderLAB/cheat-on-content.git
cd cheat-on-content
bash install.sh

This links 13 skills to ~/.claude/skills/. Initial setup requires navigating to your content project directory, opening Claude Code, and running 初始化 cheat-on-content. Importing 5-10 sample posts from target accounts is strongly recommended for initial accuracy. The primary dependency is Claude Code.

Highlighted Details

• Personalized Performance Formula: Generates a unique "hit-making formula" tailored to the user's account, evolving with continued use.
• Auto-Evolution & Validation: The system prompts for formula upgrades based on performance deviations and requires re-evaluation against historical data and cross-model checks to prevent self-deception.
• Data-Driven Workflow: Emphasizes logging scores, predictions, and T+3 day data for clear auditing and learning.
• Competitive Advantage: Positions itself as a "cheat code" for content success, enabling users to gain an edge by understanding patterns first.

Licensing & Compatibility

The project is licensed under MIT, permitting commercial use, modification, and integration into closed-source projects without restriction.

Limitations & Caveats

This tool is dependent on the Claude Code environment. Initial prediction accuracy is significantly impacted without importing target account data, with early predictions potentially being ±50% accurate. The "cheat code" framing, while highlighting competitive advantage, may be perceived subjectively. Performance claims are based on the author's experience and may vary in real-world application.