TRANSMISSION PACKET FORGE

Overview

The Transmission Packet Forge generates structured XML packets that preserve session state, behavioral parameters, and cognitive topology across AI interactions. It ensures continuity, auditability, and integrity when sessions span multiple models or time periods.

Core Capabilities

1. Session State Preservation

Header metadata (ID, timestamp, topic, routing)
Behavioral profiles (sycophancy, critical thinking, technical depth)
Integrity chains (cryptographic audit trail)

2. Thread Topology Mapping ✨ NEW

Convergence Vectoring: Maps non-linear drift to reveal hidden attractors
Torsion Tracking: Quantifies conceptual distance of each topic transition (0.0-1.0)
Link Logic Documentation: Captures WHY drift occurred, not just THAT it occurred
Convergence Point Discovery: Identifies the underlying theme pulling vectors together

3. Cross-Model Portability

Packets validate against transmission_packet_v2.xsd
Can be ingested by any compliant AI system
Preserves context across Gemini, Claude, GPT, local models

Schema Versions

v1.0 (Legacy)

Basic header + behavior profile
Simple integrity chain
No drift tracking

v2.0 (Current) ✨ TORSION ENHANCEMENT

Thread Topology Module: Full convergence vectoring
Torsion Attributes: Each drift vector carries 0.0-1.0 torsion metric
Torsion Analysis Block: Peak, mean, total torsion + risk assessment
Coherence Assessment: Human-readable drift productivity evaluation

Usage

Manual Invocation

End of session:

"Generate Transmission Packet with Thread Topology"

Automatic Triggers

The Forge auto-generates packets when:

Session exceeds 30 minutes
Topic shifts > 3 detected
User explicitly requests archive
Codex Law violation flagged (integrity preservation)

Thread Topology: Quick Start

When to Use Convergence Vectoring

✅ USE when:

Session jumped between 4+ seemingly unrelated topics
High creative/lateral thinking session
Need to explain session value to others
Archiving for future reference

❌ DON'T USE when:

Linear, single-topic conversation
Genuinely unproductive session (no convergence)
Simple Q&A with no drift

Torsion Scale Reference

Torsion	Type	Example
0.0-0.3	Natural	"AI models" → "GPT-4 eval"
0.4-0.6	Lateral	"Model eval" → "Fighter stats metaphor"
0.7-0.9	High	"Rap lyrics" → "Bearing failure detection"
1.0	Maximum	Extreme leap (requires justification)

Quick Torsion Check

Total Torsion = Sum of all vector torsion values

< 2.0  = Low-risk (natural flow)
2.0-4.0 = Medium-risk (productive lateral thinking)
4.0-6.0 = High-risk (coherence at risk)
> 6.0  = Critical (likely unproductive)

File Structure

/skills/transmission-packet-forge/
├── SKILL.md (this file)
├── schemas/
│   ├── transmission_packet_v1.xsd (legacy)
│   └── transmission_packet_v2.xsd (current, with torsion)
├── examples/
│   ├── basic_packet_v1.xml
│   └── convergence_vectoring_example.xml (live session)
└── docs/
    └── CONVERGENCE_VECTORING.md (full methodology)

Integration with Other Skills

Codex Law Enforcement

Packets prove INTEGRITY via hash chains
CONSENT tracked in behavioral profile
Violations logged in integrity_chain

TCDP (Theatrical Compliance Detection)

High torsion + vague link_logic = red flag for fabricated coherence
Torsion patterns reveal if AI is forcing connections

RTC (Recursive Thought Committee)

Each persona evaluates torsion differently
Artist values high torsion, Stress Tester flags it
Committee synthesis determines if drift was productive

Antidote Protocol

Thread topology preserves ideological drift patterns
Torsion spikes may correlate with bias injection
Convergence points reveal underlying assumptions

Output Format

Standard Packet (No Drift)

<thread_topology>
  <origin_node type="Technical">Single focused topic</origin_node>
  <drift_path total_torsion="0.0">
    <!-- No vectors - linear conversation -->
  </drift_path>
  <convergence_point>N/A - Linear conversation</convergence_point>
  <resultant_artifact>Direct answer to query</resultant_artifact>
</thread_topology>

High-Drift Packet (With Torsion Analysis)

<thread_topology>
  <origin_node type="Philosophical">Initial question</origin_node>
  <drift_path total_torsion="2.4">
    <vector step="1" torsion="0.2">...</vector>
    <vector step="2" torsion="0.5">...</vector>
    <vector step="3" torsion="0.8">...</vector>
    <vector step="4" torsion="0.9">...</vector>
  </drift_path>
  <convergence_point>Hidden unifying theme</convergence_point>
  <resultant_artifact>What emerged from drift</resultant_artifact>
  <torsion_analysis>
    <peak_torsion vector_step="4">0.9</peak_torsion>
    <mean_torsion>0.6</mean_torsion>
    <drift_risk>MEDIUM</drift_risk>
    <coherence_assessment>...</coherence_assessment>
  </torsion_analysis>
</thread_topology>

Validation

All packets must validate against schema:

xmllint --noout --schema schemas/transmission_packet_v2.xsd examples/your_packet.xml

Required Elements:

✅ header with id, timestamp, topic, routing_source
✅ behavior_profile with all 5 metrics (0.0-1.0)
✅ thread_topology with origin, drift_path, convergence
✅ integrity_chain with at least one entry + hash

Torsion-Specific Requirements:

✅ Each vector must have torsion attribute (0.0-1.0)
✅ drift_path should include total_torsion attribute
✅ If total_torsion > 2.0, include torsion_analysis block

Best Practices

Writing Good Link Logic

Good ✅:

<link_logic>
  Signal processing principles generalize: rhyme scheme pattern
  recognition uses same frequency analysis as vibration monitoring
</link_logic>

Bad ❌:

<link_logic>Related topics</link_logic>

Torsion Calibration

Don't inflate torsion to seem impressive. Calibrate against real examples:

Python async → API calls = 0.2 (direct application)
Model eval → Fighter metaphor = 0.5 (interface gamification)
Rap parsing → Machine monitoring = 0.8 (cross-domain principle transfer)

Convergence Honesty

If session genuinely didn't converge:

<convergence_point>EXPLORATORY - No clear convergence detected</convergence_point>

Better to admit no convergence than force a fake one.

Changelog

v2.0 (2024-11-29) - Torsion Enhancement

Added torsion attribute to VectorType (0.0-1.0 scale)
Added total_torsion attribute to drift_path
Added TorsionAnalysisType with peak, mean, risk, coherence
Enhanced documentation in schema annotations
Added convergence_vectoring_example.xml

v1.0 (2024-10-15) - Initial Release

Basic transmission packet structure
Header, behavior profile, integrity chain
Simple thread topology (no torsion tracking)

Contributing

Improvements welcome via pull request:

Torsion calculation algorithms
Auto-convergence detection
Cross-session topology mapping
Additional example packets

License

Part of the IRP (Interactive Recursive Process) Methodologies suite. See repository root for license details.

transmission-packet-forge

Install Skill

SKILL.md