IPD‑12 Multi‑Process Capture Bundle Template (JSON)

{
  "bundle_name": "string",
  "description": "string",
  "processes": [
    {
      "name": "string",
      "purpose": "string",
      "boundaries": ["string"],
      "structural_layers": ["string"],
      "operational_flow": ["string"],
      "coherence_baseline": "string",
      "domain": "string",
      "regime_level": "surface | mid | deep"
    }
  ],
  "bundle_metadata": {
    "version": "1.0",
    "engine": "RTT-IPD-12",
    "process_count": "auto",
    "required_fields": [
      "name",
      "purpose",
      "boundaries",
      "structural_layers",
      "operational_flow",
      "coherence_baseline",
      "domain",
      "regime_level"
    ]
  }
}

IPD‑12 Multi‑Process Capture Bundle Template (YAML)#

bundle_name: string
description: string
 
processes:
  - name: string
    purpose: string
    boundaries:
      - string
    structural_layers:
      - string
    operational_flow:
      - string
    coherence_baseline: string
    domain: string
    regime_level: surface | mid | deep
 
bundle_metadata:
  version: "1.0"
  engine: RTT-IPD-12
  process_count: auto
  required_fields:
    - name
    - purpose
    - boundaries
    - structural_layers
    - operational_flow
    - coherence_baseline
    - domain
    - regime_level

How the Bundle Works (Aligned with your active tab)#

1. Every process is validated individually#

Using the validation schema you approved.

2. The bundle unlocks multi‑process operators#

compare_process()
cross_system()
multi‑domain drift chains
paradox detection across domains
RTT/3 triangulation

3. Bundle metadata auto‑tracks process count#

Useful for multi‑domain drift analyzers and RTT/12 composite packs.

4. Bundle is the canonical input for multi‑domain drift analyzers#

This matches the structure of your IPD‑12 composite operator grammar.

Ready‑to‑Use Example (Minimal)#

{
  "bundle_name": "notes_workflow_bundle",
  "description": "Human vs AI note-taking processes",
 
  "processes": [
    {
      "name": "human_notes",
      "purpose": "capture information manually",
      "boundaries": ["time", "attention"],
      "structural_layers": ["listening", "thinking", "writing"],
      "operational_flow": ["listen", "interpret", "write"],
      "coherence_baseline": "clear notes",
      "domain": "workflow",
      "regime_level": "surface"
    },
    {
      "name": "ai_notes",
      "purpose": "capture information automatically",
      "boundaries": ["input quality", "model constraints"],
      "structural_layers": ["input", "processing", "output"],
      "operational_flow": ["receive", "transform", "emit"],
      "coherence_baseline": "clear notes",
      "domain": "workflow",
      "regime_level": "surface"
    }
  ]
}

This example is fully compatible with your paradox explainer and drift‑tensor layers.

Updated Jul 11, 2026