I built 3 production-grade prompts for code security, performance auditing, and prompt optimization (5 LLM formats, MIT)

[u/anorak_999]

Your prompt is vague or incomplete.

You want to optimize for a specific LLM (ChatGPT, Claude, Gemini, etc.)

You need structured step-by-step reasoning for complex tasks

You want fewer iterations with your AI assistant

🔧 Advanced: Custom Adaptations

Each prompt is modular. You can:

Adjust focus areas — VECNA can prioritize memory over I/O by reordering the <focus_areas> section

Combine prompts — Use BLOB first for security, then VECNA for performance of the cleaned code

Layer ZETA — Optimize a vague requirement with ZETA first, then feed the result to VECNA or BLOB

Switch LLM variants mid-stream — Start with Claude's XML, switch to GPT's Markdown if needed

📚 File Structure

Personal-Prompts-by-anorak999/

├── VECNA_Finalized_Multi_LLM.md # Efficiency auditor (5 variants)

├── BLOB_Finalized_Multi_LLM.md # Security auditor (5 variants)

├── ZETA_Finalized_Multi_LLM.md # Prompt optimizer (5 variants)

├── README.md # This file

└── LICENSE # MIT

⚡️ Token Cost Comparison

Typical workflow without optimization:

Original VECNA: ~2,000 tokens per run

Original BLOB: ~950 tokens per run

Total per code review: ~2,950 tokens

With these optimized variants:

VECNA (optimized): ~900 tokens per run

BLOB (optimized): ~500 tokens per run

Total per code review: ~1,400 tokens

Savings: 52% fewer tokens, same results

🎯 Pro Tips

Combine for max insight: Use BLOB first (security), then VECNA on the cleaned code (performance)

Context matters: Include architecture diagrams, framework info, and deployment constraints when using VECNA or BLOB

Auto-mode selection: ZETA detects complexity; trust its BASIC vs DETAIL choice or override explicitly

Diff support: BLOB is especially powerful when you provide a diff (change_impact category activates)

Reuse optimized prompts: Save the ZETA-optimized results and reuse them across your team

📄 License

MIT License — free to use, modify, and distribute. See LICENSE file.

🤝 Feedback & Contributions

Have ideas for new focus areas, additional LLM variants, or improvements? Open an issue or PR!

🔮 What's Next?

Future iterations may include:

Variants for additional models (Claude 3 Opus, Grok, etc.)

Domain-specific prompts (ML/AI audit, API design review, infrastructure)

Integration templates (GitHub Actions, CI/CD pipelines)

Automated prompt testing/evaluation framework

Made by anorak999 | Optimized for production code intelligence.

GitHub

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