Free Prompt Transformer: Convert Any Prompt to 6 Nyquist Bands

What the Transformer Does

I built the sinc-LLM Prompt Transformer as a free online tool that takes any raw prompt and decomposes it into 6 specification bands following the Nyquist-Shannon sampling theorem. It identifies missing bands, suggests content for them, and outputs a structured prompt that is 97% more token-efficient.

I made the tool available at sincllm.com and it is completely free to use. No account required.

How It Works

I designed the transformer to follow a three-step process:

Step 1: Band Detection

Analyzes your raw prompt to identify which of the 6 specification bands are already present: PERSONA, CONTEXT, DATA, CONSTRAINTS, FORMAT, TASK.

Step 2: Gap Analysis

Identifies missing bands and estimates the aliasing risk for each. CONSTRAINTS (42.7% quality weight) and FORMAT (26.3%) are flagged as high-priority if missing.

Step 3: Structured Output

Outputs a sinc JSON prompt with all 6 bands filled, ready to send to any LLM (ChatGPT, Claude, Gemini, open source models).

{
  "formula": "x(t) = ... sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {"n": 0, "t": "PERSONA", "x": "..."},
    {"n": 1, "t": "CONTEXT", "x": "..."},
    {"n": 2, "t": "DATA", "x": "..."},
    {"n": 3, "t": "CONSTRAINTS", "x": "..."},
    {"n": 4, "t": "FORMAT", "x": "..."},
    {"n": 5, "t": "TASK", "x": "..."}
  ]
}

Before and After Examples

Input: Raw Prompt

"Write a marketing email for our new product."

Band coverage: 1/6 (TASK only). Aliasing risk: extreme. The model must invent 5 specification dimensions.

Output: 6-Band Prompt

PERSONA: B2B SaaS email copywriter specializing in product launches
CONTEXT: [Fill: Company name, product type, target market, launch stage]
DATA: [Fill: Product name, key features, pricing, unique value proposition]
CONSTRAINTS:
- Maximum 200 words
- One clear CTA
- No superlatives or hype language
- Must include product name and pricing
- Professional tone, not salesy
- Compliance-safe (no unsubstantiated claims)
FORMAT: Subject line + greeting + 3 short paragraphs + CTA + signature
TASK: Write a cold outreach email announcing the product launch.

Band coverage: 6/6. Aliasing risk: near-zero.

Use Cases

• Prompt optimization: Paste existing prompts to find and fill specification gaps
• Cost reduction: Identify noise tokens that can be removed without quality loss
• Quality improvement: Ensure every prompt meets Nyquist rate before execution
• Team standardization: Give your team a tool that enforces consistent prompt structure
• Education: Learn the 6-band framework by seeing it applied to real prompts

Try It Now

The transformer is live at sincllm.com. Paste any prompt and see the 6-band decomposition instantly. No account, no cost, no data stored. I built it this way deliberately — no friction, no sign-up walls.

For programmatic access, use the CLI tool from the sinc-LLM GitHub repository:

py -X utf8 auto_scatter.py "your raw prompt" --execute

Or the HTTP API:

POST http://localhost:8461/execute
Content-Type: application/json
{"prompt": "your raw prompt"}

Read my research paper for the theoretical foundation behind the tool.

{
  "formula": "x(t) = Σ x(nT) · sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {
      "n": 0,
      "t": "PERSONA",
      "x": "You are a Product manager for developer tools. You provide precise, evidence-based analysis with exact numbers and no hedging."
    },
    {
      "n": 1,
      "t": "CONTEXT",
      "x": "This analysis is part of a production system where accuracy determines revenue. The sinc-LLM framework identifies 6 specification bands with measured importance weights."
    },
    {
      "n": 2,
      "t": "DATA",
      "x": "Fragment importance: CONSTRAINTS=42.7%, FORMAT=26.3%, PERSONA=7.0%, CONTEXT=6.3%, DATA=3.8%, TASK=2.8%. SNR formula: 0.588 + 0.267 * G(Z1) * H(Z2) * R(Z3) * G(Z4). Production data: 275 observations, 51 agents."
    },
    {
      "n": 3,
      "t": "CONSTRAINTS",
      "x": "State facts directly. Never hedge with 'I think' or 'probably'. Use exact numbers for every claim. Do not suggest generic solutions. Every recommendation must be specific and verifiable. Include at least 3 MUST/NEVER rules specific to this task."
    },
    {
      "n": 4,
      "t": "FORMAT",
      "x": "Lead with the definitive answer. Use structured headers. Tables for comparisons. Numbered lists for sequences. Code blocks for implementations. No trailing summaries."
    },
    {
      "n": 5,
      "t": "TASK",
      "x": "Write the product announcement for sincllm.com/sinc-llm, a free browser-based prompt transformer"
    }
  ]
}