tweakcc-system-prompts

🥈Silver

@bl-ue

Customized Claude Code system prompts for use with tweakcc . ~48k bytes smaller, 30% faster, same accuracy

3640Updated Today

Intermediate30min to implementautomation

Saves ~60 min per use

Quick InstallView Source

git clone https://github.com/bl-ue/tweakcc-system-prompts.git

Works with:

Claude

Overview

About This Skill

Customized Claude Code system prompts for use with tweakcc . ~48k bytes smaller, 30% faster, same accuracy

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/bl-ue/tweakcc-system-prompts

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a customized system prompt for Claude Code to use with tweakcc. The prompt should be optimized for [INDUSTRY] applications, focusing on [SPECIFIC_TASK]. Ensure it is ~48k bytes smaller and 30% faster than the default prompt while maintaining the same accuracy. Include specific instructions for handling [DATA_TYPE] and adhering to [COMPANY] guidelines.

Example Output

# Customized System Prompt for Claude Code

## Industry: Healthcare
## Specific Task: Medical Data Analysis

**Instructions:**
- Analyze patient records efficiently, focusing on trends and anomalies.
- Ensure HIPAA compliance in all responses.
- Optimize for speed and accuracy in processing large datasets.
- Handle unstructured data such as doctor's notes and lab reports.

**Guidelines:**
- Prioritize patient privacy and data security.
- Provide concise and actionable insights.
- Avoid speculative or hypothetical responses.

**Example Output:**
- **Trend Analysis:** Increased instances of diabetes among patients aged 45-65.
- **Anomaly Detection:** Unusual spike in patient admissions on weekends.
- **Recommendation:** Schedule additional staff for weekend shifts.