🛠️ Contributor Workflow

“How to Add a New RTTcode (Step by Step)”#

Contributor Workflow: Adding a New RTTcode#

This guide walks you through creating a new RTTcode from scratch and integrating it into the TriadicFrameworks documentation system.


1. Create a payload file#

Start with a minimal JSON payload:

{
  "domain": "rtt",
  "artifact_type": "paper",
  "version": "v2.1.0",
  "triad": {
    "f_R": "1.00",
    "tau_R": "144ms",
    "Q_R": "0.97"
  },
  "url": "https://triadicframeworks.org/docs/rtt/"
}

Save as:

payload.json

2. Validate the payload#

Use either validator:

JavaScript:

node validate_js.js payload.json

Python:

python validate_python.py payload.json

If validation passes, continue.


3. Generate the RTTcode PNG#

JavaScript:

node generate_rttcode.js payload.json out.png

Python:

python generate_rttcode.py payload.json out.png

This produces a QR‑compatible RTTcode image.


4. (Optional) Apply domain‑specific styling#

Use the guidelines in:

docs/rttcodes/style/visual-guidelines.md

You may add:

  • resonance waves (RTT)
  • field lines (SET)
  • lattice geometry (Substrate)

Ensure the QR remains scannable.


5. Add the RTTcode to the examples folder#

Place your files in:

docs/rttcodes/examples/<domain>/

Include:

  • payload.json
  • <domain>-rttcode.png
  • README.md describing the artifact

6. Update the top‑level RTTcodes README#

Add a link to your new example and describe its purpose.


7. Commit and open a pull request#

Include:

  • the payload
  • the generated PNG
  • any style updates
  • README updates

Your RTTcode is now part of the canonical system.

This workflow is simple, repeatable, and contributor‑friendly.


🧬 3. TriadicFrameworks‑Wide Metadata Standard#

The layer RTTcodes plug into.#

TriadicFrameworks Metadata Standard (TF‑MS v1.0)#

RTTcodes are one component of a broader metadata ecosystem within TriadicFrameworks. This document defines the shared metadata model that all artifacts, tools, and documentation layers plug into.


1. Purpose#

The TriadicFrameworks Metadata Standard (TF‑MS) provides:

  • a unified identity model for all artifacts
  • consistent versioning
  • domain classification
  • optional resonance‑time triad metadata
  • compatibility with RTTcodes, docs, diagrams, and build systems

RTTcodes implement TF‑MS in a QR‑compatible form.


2. Core Metadata Fields#

Every artifact in TriadicFrameworks SHOULD define:

Field Description
domain Which part of the ecosystem the artifact belongs to
artifact_type What kind of artifact it is (paper, README, diagram, model, etc.)
version Semantic version string
title Human‑readable name
description Short summary
url Canonical location
authors Optional list of contributors
triad Optional resonance‑time metadata

RTTcodes use a subset of these fields.


3. Domains#

Domains define the conceptual space an artifact belongs to:

rtt
set
substrate
observer
governance
docs
other

Domains MUST be lowercase ASCII.


4. Triad Metadata#

Triad metadata describes resonance‑time characteristics:

  • f_R — resonance frequency
  • tau_R — resonance time constant
  • Q_R — quality factor

These fields are optional but recommended for RTT, SET, and Substrate artifacts.


5. Versioning#

Artifacts MUST use semantic versioning:

vMAJOR.MINOR.PATCH

Examples:

  • v1.0.0 — initial stable release
  • v2.1.0 — minor update
  • v2.1.3 — patch

RTTcodes embed the version directly into the URL token.


6. RTTcode Integration#

RTTcodes implement TF‑MS by:

  • encoding domain, version, and triad into a compact token
  • linking to the canonical url
  • providing a QR‑compatible entry point into the metadata system

The RTTcode schema is a strict subset of TF‑MS.


7. Compliance#

An artifact is TF‑MS compliant if:

  1. It defines all required metadata fields
  2. It uses valid domain identifiers
  3. It uses semantic versioning
  4. It provides a canonical URL
  5. (Optional) It includes triad metadata

RTTcodes are the portable, scannable representation of this metadata.


TF‑MS ensures that all TriadicFrameworks artifacts — from theory papers to substrate models — share a common identity layer that is stable, searchable, and machine‑readable.