- name
- omega-notation
- description
- Structured output compression for AI agents. Dramatically reduces token cost on structured data (evals, decisions, routing, policies, media summaries). Designed for machine-to-machine agent communication, not prose.
- version
- 1.0.1
- author
- Shadow Rose
- tags
- [compression, tokens, cost-reduction, structured-output, agent-communication]
Ω Notation — Token Compression for AI Agents
What It Does
Compresses structured agent outputs into ultra-dense shorthand that other agents can parse. Designed for machine-to-machine communication where every token costs money.
Compression Performance
| Data Type | Reduction | Notes |
|---|---|---|
| JSON evals/decisions | ~95-98% | Highest gains — format-heavy, payload-light |
| Routing/dispatch | ~90-95% | Repetitive structure compresses well |
| Policy rules | ~85-90% | Conditional logic has moderate density |
| Media summaries | ~80-85% | Mixed structure + free text |
| Semi-structured logs | ~60-70% | Less redundant format to strip |
| Conversational text | ~30-40% | High semantic density, low format redundancy |
Key insight: Compression scales with how much of the original is *format* vs *meaning*. Structured data is mostly format (brackets, keys, boilerplate). Conversation is mostly meaning. Omega Notation strips format — it doesn't compress meaning.
When To Use
- Agent-to-agent structured messages (evals, routing, decisions)
- High-volume pipelines where token cost matters (batch processing, multi-agent orchestration)
- Decision crystallization (fitness scores, deltas, confidence)
- Policy enforcement outputs
- Media/video summary digests
- Any structured data flowing between AI systems
When NOT To Use
- Conversational replies to humans
- Prose, documentation, or creative writing
- Anything where human readability matters
- As a global default for all outputs (will break conversational ability)
- Free-form text with no repeating structure
Format
Every Ω message starts with a header:
!omega v1 dict=autoSupported Types
| Prefix | Type | Example |
|---|---|---|
e.d | Eval digest | e.d {c:0.95 d:proceed} [cat:finance] |
d.c | Decision crystallize | d.c "task-name" {fit:0.98} Δfit:+0.03 |
r.d | Route dispatch | r.d "handler" {to:opus pri:high} |
p.e | Policy enforce | p.e "safety" {if:conf<0.5 then:escalate} |
t.es | Tier escalate | t.es {from:1 to:2 reason:"low-conf"} |
m.c | Media compress | m.c "vid-1" {h:phash:abc len:142 cap:"""summary"""} |
Tags
Append tags in brackets: [cat:finance] [pri:high] [src:apex]
Deltas
Use Δ prefix for changes: Δfit:+0.03 Δconf:-0.1
Multi-line
Multiple operations in one message:
!omega v1 dict=auto
e.d {c:0.92 d:hold} [cat:trading]
d.c "btc-position" {fit:0.87} Δfit:-0.05
t.es {from:1 to:2 reason:"regime-shift"}Round-Trip Integrity
Omega Notation includes a TypeScript serializer/deserializer with full round-trip verification. Structured data compressed → decompressed returns identical objects. The test() function validates this automatically.
Usage
When you want structured output compressed, include Ω Notation format in your request:
Give me the eval results in Ω Notation format.The agent will use the prefix syntax (e.d, d.c, r.d, etc.) for that response. Conversational replies stay normal — Ω Notation is invoked per-request, not globally.
Dictionary System
dict=auto— agent builds shorthand mappings over time within a sessiondict=none— no dictionary, all explicit- Custom:
dict={proceed:p, escalate:e, hold:h}— define upfront
Technical Details
- TypeScript implementation with serialize/deserialize functions
- No external dependencies
- Built-in round-trip test
- Extensible type system — add new prefixes for domain-specific structured data
Modes
mode=struct (default, shipped)
Structured data compression. 90-98% reduction. Round-trip verified. Use this.
mode=context (v2, coming soon)
Prose/context compression using law-derived predictive encoding. Based on the Law of Non-Closure applied to LLM-to-LLM communication — the decoder's knowledge IS the codebook, so only surprise content needs transmitting. Theoretical ceiling: ~70-80% reduction on conversational text. Not yet implemented.
Theoretical Basis
Omega Notation exploits the fact that structured data is mostly *format* (brackets, keys, whitespace, boilerplate) with small *payloads* (values, scores, names). Stripping predictable format while preserving payload achieves high compression on structured types. Conversational text has the inverse ratio — mostly payload, little format — which is why compression drops for prose.
v2 will use a fundamentally different approach for prose: predictive compression where the LLM's training acts as a shared codebook between encoder and decoder. Only tokens the decoder can't predict need transmitting. The compression floor is H(message | decoder_knowledge) — a result derived from information theory and thermodynamic law.