what is personal-prompts-by-anorak999 fr?

anorak999/personal-prompts-by-anorak999 — explained in plain English

Analysis updated 2026-05-18

★ 2Audience · developerComplexity · 1/5LicenseSetup · easy

vibe map

mindmap
  root((Personal-Prompts))
    What it does
      Code performance review
      Security auditing
      Prompt optimization
    Prompt formats
      Claude XML
      ChatGPT Markdown
      Llama control tokens
      Gemini
      Mistral
    Use cases
      Audit existing code
      Security checks
      Improve vague prompts
    Setup
      No install needed
      MIT license
      Three Markdown files

mindmap root((Personal-Prompts)) What it does Code performance review Security auditing Prompt optimization Prompt formats Claude XML ChatGPT Markdown Llama control tokens Gemini Mistral Use cases Audit existing code Security checks Improve vague prompts Setup No install needed MIT license Three Markdown files

what do people make with this?

VIBE 1

Run a structured performance audit on your codebase to find slow algorithms, memory leaks, and blocking I/O.

VIBE 2

Get a security review of an API or service before deploying, with ranked findings and fix suggestions.

VIBE 3

Turn a vague AI prompt into a precise, model-specific version using the ZETA four-step optimizer.

what's the stack?

Markdown

how it stacks up fr

	anorak999/personal-prompts-by-anorak999	0-bingwu-0/live-interpreter	0xkaz/llm-governance-dashboard
Stars	2	2	2
Language	—	Python	Python
Setup difficulty	easy	moderate	hard
Complexity	1/5	2/5	4/5
Audience	developer	general	ops devops

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

in plain english

This repository is a collection of three system prompts designed to help developers get more consistent, structured output from AI assistants. The author has written each prompt in five versions, one formatted for each of the major AI models: Claude, ChatGPT, Gemini, Llama, and Mistral. The idea is that each model responds better to its own native syntax, so Claude gets XML, ChatGPT gets Markdown with JSON, and Llama gets special control tokens. The three prompts are named VECNA, BLOB, and ZETA. VECNA handles code performance reviews. You paste in a codebase, and the prompt instructs the AI to find specific performance problems: slow algorithms, memory leaks, blocking database calls, and concurrency bugs. The output comes as a ranked list of findings, each with a before-and-after comparison and implementation guidance. BLOB covers security auditing. It directs the AI to look for injection attacks, authentication bypasses, logic errors, race conditions, and weak error handling. Results include exact file and line numbers, a description of how each issue could be exploited, and a suggested fix. ZETA is a prompt optimizer. If you have a vague request you want to run through an AI, ZETA helps you rewrite it into something more precise and better tailored for the specific model you are using. It applies a four-step process: break down the request, diagnose what is missing, develop a better version, and deliver the result. All three prompts have been compressed by 32 to 62 percent compared to their original versions, according to the README. The author's goal was to keep the same quality of output while spending fewer tokens. Setup is simple: there is nothing to install. The repository is three Markdown files. Each file includes all five LLM variants. You copy the block matching your tool of choice, paste it into your chat session or API call, and follow it with your actual input. The license is MIT.

prompts (copy fr)

prompt 1

Using the VECNA prompt framework, review this Python service for performance bottlenecks and rank your findings by impact: [paste code]

prompt 2

Act as a security auditor using the BLOB prompt. Review this Node.js API for SQL injection, auth bypass, and race conditions: [paste code]

prompt 3

Use the ZETA 4D methodology to improve this vague prompt: 'I need a script that processes CSV files and does something useful with the data.'

prompt 4

I want to adapt the VECNA prompt to focus only on memory issues. Help me reorder the focus_areas section to deprioritize I/O and concurrency.

prompt 5

What is the difference between the Claude XML and ChatGPT Markdown versions of the BLOB security audit prompt, and why does each format work better for its target model?

Frequently asked questions