tron

Process TRON wire-format documents at the byte level without deserialization.

Updated Aug 23, 2026

Search Tags:#data-structures #nats #copy-on-write #tron #wire-format #hamt #vector-trie

Ready to run

npx skills add https://github.com/cbeauhilton/mu --skill tron

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tron
Source: https://github.com/cbeauhilton/mu/tree/main/home/dev/pai-skills/tron
Command: npx skills add https://github.com/cbeauhilton/mu --skill tron

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TRON enables treating a document as a data structure and manipulating it directly at the byte level, eliminating the need for full deserialization and enabling efficient history via copy-on-write.

Core Features & Use Cases

In-place traversal of maps and arrays using HAMT and vector tries.
Copy-on-write updates with embedded history for time-travel and diffs.
Efficient wire-format encoding/decoding and seamless integration with NATS KV/Object Store workflows.

Quick Start

Encode a sample dataset into TRON and inspect the root and trailer to verify canonical encoding and history.

Frequently Asked Questions about tron

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I process wire-format data without deserializing the entire document?▼

You can process wire-format data directly at the byte level using canonical encoding rules to achieve deterministic, low-latency access without full deserialization. This enables in-place traversal and manipulation of maps and arrays.

How does copy-on-write work with HAMT and vector-trie data structures?▼

Copy-on-write updates use HAMT and vector-trie data structures to embed history directly within the wire-format document. This enables efficient time-travel access and diffing without duplicating the entire dataset on every modification.

What is the best way to track document history and diffs in a wire-format?▼

Tracking document history in a wire-format is best achieved through copy-on-write updates, which embed historical state directly. This allows seamless time-travel and diffing operations while maintaining low-latency in-place traversal.

Does TRON wire format work with NATS KV and Object Store workflows?▼

Yes, TRON wire format integrates seamlessly with NATS KV and Object Store workflows. This allows you to store and retrieve byte-level encoded documents while maintaining copy-on-write history and deterministic access.

When do I need to use byte-level traversal for maps and arrays?▼

Byte-level traversal is needed when you require low-latency, deterministic access to map and array data without the overhead of deserializing. It is essential for production-grade wire-format workflows using HAMT and vector-trie structures.

What are the limitations of copy-on-write history in wire-format documents?▼

Copy-on-write history in wire-format documents requires strict adherence to canonical encoding rules to maintain data integrity. Limitations arise if in-place traversal or HAMT structures are not correctly implemented, potentially corrupting the embedded history.