SCBE-AETHERMOORE
Training Data Marketplace

Training data built from a real, running governance system

These datasets were assembled from SCBE-AETHERMOORE source, documentation, experiments, and project sessions. Counts and formats are concrete; benchmark results are project-authored and should be read with the scope and limitations in the evidence ledger.

5,188+ SFT pairs 91 red team prompts JSONL / QLoRA-ready Direct purchase
5,188+
SFT training pairs
91
Red team prompts
48
Session transcripts
6
Theory documents
~52MB
Total dataset size
Public
HuggingFace hub access

Targeted datasets for specific use cases.

Each pack is documented and formatted for QLoRA-style pipelines. Validate its schema, license, split, and task fit with your chosen trainer before spending compute.

Governance
📐

SCBE Governance SFT Pack

$99

5,188 supervised fine-tuning pairs covering governance, system-level instructions, and codebase understanding.

  • Merged multi-source SFT corpus (~5,188 pairs) covering governance + system + codebase
  • QLoRA-ready JSONL with governance decisions, risk labels, and Sacred Tongue routing tags
  • Sources: HF scbe-aethermoore-training-data, internal sft_repo_merged.jsonl
  • Use as one input to governance-oriented fine-tuning experiments; results depend on the base model, mix, and evaluation design
JSONL · QLoRA-Ready Purchase -- $99
Adversarial
🛡

Red Team Fortress

$149

91 project-authored adversarial prompts across 10 categories, labeled by failure layer (L1-L14). The current 91/91 result is regression-corpus fit, not a generalization claim.

  • 91 attack prompts across 10 adversarial categories
  • Each prompt labeled by failure layer (L1 through L14)
  • Compliance evals and attack scenario theory docs included
  • Stress-test AI models and build adversarial training sets
  • Also available as a free preview on HuggingFace (95 downloads)
JSONL Purchase -- $149
Conlang
🌱

Six Tongues Conlang + Tokenization Pack

$79

Constructed language design docs, tokenization theory, and bijective encoding patterns from the Six Sacred Tongues system.

  • theory_doc_conlang_intent.jsonl -- language design theory
  • Tongues session transcripts
  • Spiralverse codex SFT pairs
  • Novel tokenization research and conlang AI training
  • Creative AI fine-tuning for unique output patterns
JSONL Purchase -- $79
Creative
🎮

Spiralverse Session Transcripts

$49

48 session transcript files across 11 categories of structured RPG and worldbuilding dialogue.

  • 48 session files: game, NPC roundtable, game design, lore
  • Architecture, tongues, gacha, math, DM, music, space commerce
  • ~400KB of structured dialogue data
  • RPG AI training and game NPC dialogue
  • Interactive fiction fine-tuning
JSONL Purchase -- $49
Research
📚

Theory Documents Bundle

$129

6 deep-dive theory documents covering the full intellectual foundation of the SCBE-AETHERMOORE framework.

  • Architecture theory -- full pipeline design rationale
  • Spiralverse lore -- worldbuilding knowledge injection
  • Security attacks -- adversarial taxonomy and defenses
  • GeoSeal crypto -- bijective encryption theory
  • Conlang intent -- constructed language design principles
  • Patent claims -- USPTO #63/961,403 methodology
JSONL · Structured Q&A Purchase -- $129

The Full Arsenal.

Everything above in one download. Save $107 versus buying each pack individually.

Save $107

The Full Arsenal

$399 $506 SAVE $107

The combined SCBE-AETHERMOORE data collection for governed-AI experiments. A usable fine-tune still requires a compatible base model, trainer, compute budget, validation split, and independent evaluation.

  • 5,188+ SFT training pairs
  • 91 red team adversarial prompts
  • 6 deep-dive theory documents
  • 48 session transcripts (11 categories)
  • Full conlang + tokenization pack
  • Context capsules
  • Knowledge base files
  • Eval suites
JSONL ~52MB Total QLoRA-Ready
Purchase The Full Arsenal -- $399

Start here for free.

The framework is MIT-licensed. The HuggingFace datasets are free to download. No strings attached.

MIT Licensed
🛡

SCBE-AETHERMOORE

FREE

The experimental 14-layer pipeline, Sacred Tongues, and hyperbolic cost engine. Open source for inspection and testing; production readiness depends on your threat model and validation.

  • Full 14-layer security pipeline
  • Sacred Tongues tokenizers
  • Hyperbolic cost scaling (H(d,R) = R^(d²))
  • Project-authored evaluation artifacts with scope and limitations published separately
npm i scbe-aethermoore
View on GitHub
Free Download
🤗

HuggingFace Datasets

FREE

Community datasets available for immediate download on HuggingFace.

  • scbe-aethermoore-training-data
  • scbe-red-team-benchmarks
  • polly-training-data
  • polly-chat-seed
Browse on HuggingFace
Project baseline · 0.9201 AUC
📈

Prompt Injection → Bit Signatures

FREE

24,254 labeled prompts from 4 public injection datasets, each mapped through the Six Sacred Tongues bijective tokenizer into a lossless bit signature. Published as a HuggingFace dataset with stratified train/val/test splits.

  • Sources: neuralchemy (6,274), SPML (16,012), jackhhao (1,306), deepset (662)
  • Stratified 70/15/15 splits by source × label
  • SHA-256 + bit histogram + Shannon entropy + phi-weighted sums
  • Baseline: 0.9201 AUC on 31 features (no neural net)
  • Apache-2.0 license, immediate download, 44 MB
  • Full pipeline + classifier code in scbe-experiments
Browse on HuggingFace Read the article

HuggingFace Model Zoo.

Experimental checkpoints published from SCBE data work. Model-card descriptions identify their intended lane; they are not claims of frontier capability.

Need something specific?

Custom dataset work can be scoped around a model architecture, domain, format, and measurable validation plan.

Custom Dataset Requests

Tell us your model, your domain, and your format requirements. We will package a dataset specifically for your fine-tuning pipeline.

Custom JSONL packaging
Domain-specific extraction
QLoRA / LoRA / full fine-tune
Red team augmentation
Documentation mapped to the agreed controls
Contact Us -- aethermoregames@pm.me