tron

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

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/cbeauhilton/mu --skill tron
Or copy as Structured Prompt for Agent
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.