🥗 Token-Diet: Advanced LLM Prompt Optimization Framework

Token-Diet is a comprehensive framework designed to minimize token consumption in Large Language Model (LLM) prompts without sacrificing output quality. By integrating linguistic, algorithmic, and architectural patterns, Token-Diet enables up to 20x reduction in token usage, significantly lowering API costs and improving inference latency.

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🚀 Key Features

1. ⚡ Prompt Caching (Static & Ephemeral)

Leverage provider-level caching (e.g., Anthropic's cache_control) to mark static system prompts or large context blocks.

• Impact: Reduces repeat turn costs by up to 90%.
• Best for: Multi-turn agentic workflows and stable system instructions.

2. 🔄 Rolling Conversational Summarization

Prevents linear context bloat by maintaining a fixed window of recent history while summarizing older turns.

• Impact: Maintains performance in long-running sessions without hitting context limits.
• Mechanism: Automatically condenses turns 1 to (N-6) into a concise summary block.

3. 📦 Tool Call Batching

Optimizes agentic loops by grouping multiple tool requests into a single round-trip.

• Impact: Minimizes redundant context re-reads and reduces total input tokens.
• Example: get_users(ids: list[str]) instead of multiple get_user(id) calls.

4. 🚦 Conditional Model Routing

Intelligently dispatches tasks to the most cost-effective model based on complexity.

• Simple Tasks: Routed to lightweight models (e.g., Haiku, GPT-4o-mini).
• Complex Tasks: Routed to high-reasoning models (e.g., Opus, GPT-4o).

5. 🦴 Linguistic Compression (Caveman Ultra)

Applies extreme linguistic pruning to instructions and queries.

• Impact: Cuts token usage by ~75% while retaining 100% technical accuracy.
• Rules: Strips articles, fillers, and conjunctions; uses abbreviations and causal arrows (X → Y).

6. 🛠️ PCodec (Prompt-Codec) & Schema-First

Encodes complex rules and JSON schemas into short, referenceable codes (R1, R2, S1).

• Mechanism: Gateway-level expansion ensures the model receives full context only when necessary.

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📊 Effectiveness Comparison

Feature	Token-Diet v2.0	Standard RAG	LLMLingua-2
Compression	Ultra (20x+)	Low (1.2x)	High (10x)
Latency	Lowest	High	Mid
Cost	Lowest	High	Mid
Scope	Full Stack	Context Only	Token Pruning

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🛠️ Usage Checklist

1. Cache static system prompts to save on repeat turns.
2. Summarize conversation history once it exceeds token thresholds.
3. Batch tool calls to reduce API round-trips.
4. Route simple extraction/formatting tasks to cheaper models.
5. Compress instructions using Caveman Ultra linguistic rules.

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🚀 Integration

To leverage the Token-Diet skill in your AI workflows, follow these integration guidelines:

Download the SKILL.md file and upload to AI tool (manus) as a skill, some tools may require it to be downloaded as a zip file (claude)

For Manus Agents

Manus agents can directly utilize the Token-Diet skill by referencing its name. When you want to apply token reduction techniques, simply invoke the skill with the /token-diet command, followed by your prompt or specific instructions. The agent will automatically apply the defined strategies (e.g., Caveman Ultra, caching, summarization) to optimize token usage.

Example Usage within Manus:

/token-diet Analyze the provided log data for anomalies and summarize key findings concisely.

For Claude and Other LLMs (via PCodec/Gateway)

For direct integration with Claude or other LLMs, the Token-Diet principles are best applied at the gateway or orchestration layer using the PCodec (Prompt-Codec) mechanism. This involves an intermediary service that intercepts and transforms prompts before sending them to the LLM.

1. Implement a Gateway: Set up a proxy or gateway service that sits between your application and the LLM API.
2. Apply PCodec Rules: Configure the gateway to apply the Token-Diet's PCodec rules. This means defining short codes (e.g., R1, S1) that expand into full, but optimized, instructions or schema definitions.
3. Integrate Advanced Techniques: The gateway can also manage prompt caching, rolling summarization, tool call batching, and conditional model routing before the prompt reaches the LLM.

Example Prompt (after gateway processing):

User: R1: Analyze data. Summarize. S1: JSON output.

(Where R1 expands to a Caveman Ultra compressed instruction and S1 ensures a structured JSON output.)

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🧑‍💻 Author

• Name: Vijay Dixon
• Email: vijaydixon2026+aitools@gmail.com

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

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