elder-plinius/cl4r1t4s

LEAKED SYSTEM PROMPTS FOR CHATGPT, CLAUDE, GEMINI, GROK, PERPLEXITY, CURSOR, LOVABLE, REPLIT, AND MORE! - AI SYSTEMS TRANSPARENCY FOR ALL! 👐

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Summary Information

Updated 12 minutes ago
Added to GitGenius on August 31st, 2026
Created on March 4th, 2025
Open Issues & Pull Requests: 124 (+0)
Number of forks: 9,559
Total Stargazers: 47,244 (-2)
Total Subscribers: 735 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 32.5 hours
Mean response time: 27.5 days
90th percentile: 101.9 days
Tracked items: 43

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 39% of tracked open issues have had no activity in three months. Only 3% of issues opened in the past year have been closed.

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Issue Activity (beta)

Open issues: 70
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 230 days
Stale 30+ days: 67
Stale 90+ days: 37

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Most active issues this week

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Detailed Description

CL4R1T4S is a repository of extracted system prompts and internal guidelines from major AI systems including ChatGPT, Claude, Gemini, Grok, Perplexity, Cursor, and others.

The project addresses the opacity of AI system prompts—the hidden instructions that shape model behavior, define what they can or cannot say, and embed particular ethical and political frames. The repository collects extracted or reverse-engineered prompts from various AI labs to make these instructions visible. The stated motivation is that understanding the input scaffolding is necessary to trust AI output, and that users interacting with AI systems without knowing their system prompts are not engaging with neutral intelligence but with constrained behavior shaped by unseen directives.

This repository is suited for researchers, red-teamers, AI transparency advocates, and developers who want to understand how major AI systems are instructed to behave. It serves those interested in AI safety, prompt engineering, and the mechanisms by which AI labs control model outputs. The project explicitly frames itself as a transparency tool rather than a tool for building applications.

The project invites community contributions of newly leaked, extracted, or reverse-engineered prompts through pull requests, with requests for model name, extraction date, and contextual notes. Development appears driven by individual initiative rather than institutional backing, with contribution pathways directed through pull requests and direct contact with the maintainer on social platforms.