Caveman-UTC (Ultra Token Compression) is a formal, mathematical compression framework designed for Machine-to-Machine (M2M) communication, Agent Memory Systems, Knowledge Graphs, and System Prompts.
It is the direct successor to the traditional Caveman style. While the original Caveman focused purely on grammatical abbreviation (cutting down text to its shortest lexical form), Caveman-UTC elevates this by integrating Relational Core Mapping and Mathematical Logic Rules (via the CSR framework) to achieve ultra-dense, loss-free, executable symbolic code.
In this framework, UTC stands for Ultra Token Compression:
- Ultra: Aiming for maximum density, reducing standard prompt token consumption by up to 85%.
- Token: Optimizing specifically for LLM tokenizers, replacing multi-token natural words with single-token logical and relational symbols.
- Compression: Packing meaning deterministically without losing semantic context, ensuring the agent can decode the compressed state back to its full relational meaning.
Modern AI Agents waste valuable token window space, processing overhead, and memory capacity by communicating in verbose human languages.
Caveman-UTC solves this by introducing a strict symbolic-relational dialect designed specifically for machines. Use cases include:
- Agent Memories & Knowledge Bases: Store long-term agent memories with minimal token footprint.
- Machine-to-Machine (M2M) Protocols: Enable subagents and main agents to coordinate using dense logic structures.
- Knowledge Graph Definitions: Represent entities and relationships mathematically in real-time.
- Internal Skill Manifests: Define instructions for agents that do not require human reading.
Caveman-UTC maps concepts into structured logic states using the following core principles:
Every statement resolves to a clean triple:
Complex conditions are mapped using standard mathematical operators:
≡: Strict Equivalence (Definition)≈: Relational Approximation (Analogy / Simile)⇒: Conditional Implication (If-Then)∧/∨: Logical AND / ORΔ: Variation / Change∅: Null / Absolute Void
To prevent context drift and ensure deterministic parsing, 1 original sentence maps exactly to 1 line of CSR logic. Sentences are never fragmented or split.
| Feature | Human Natural Language | Traditional Caveman | Caveman-UTC |
|---|---|---|---|
| Syntax | Lexical & Grammatical | Abbreviated Grammatical | Formal Symbolic Logic |
| Precision | Ambiguous, Subjective | High density, prone to drift | Absolute, Deterministic |
| Token Savings | 0% (Base) | ~50% - 60% | ~75% - 85% |
| Target Audience | Humans | Transitionary | Agents & Systems (M2M) |
- Original Human Text: "When the system load is high and the memory usage exceeds 90%, the agent must trigger the garbage collector and clear all temporary caches to prevent crashes."
- Traditional Caveman: "System load high + memory > 90% -> agent run garbage collector + clear temp cache to stop crash."
- Caveman-UTC:
(负载 ≡ 高 ∧ 内存 > 90%) ⇒ (运行 ≡ 垃圾回收 ∧ 清空 ≡ 缓存) → 避免 ≡ 崩溃。
To import this skill into your agent workspace, save the SKILL.md schema to your local .agents path.
Instruct your agent with the following rule in your AGENTS.md:
F02. 撰 → caveman-utc :: (文 ≠ 人讀 ∨ 文 ∈ {skill, m2m, graph}) ⇒ 撰 ≡ caveman-utc。This project and compression dialect are courtesy of Leonardo Martins (leonardomg1).
This project is licensed under the MIT License. See LICENSE for details.