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issdandavis
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scbe-training-pipeline

Merge and validate SFT training data sources for publishing.

6|1|Updated Jan 17, 2026
Search Tags:#automation#governance#pipeline#deduplication#training-data#huggingface#sft
One-click install
npx skills add https://github.com/issdandavis/SCBE-AETHERMOORE --skill scbe-training-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scbe-training-pipeline
Source: https://github.com/issdandavis/SCBE-AETHERMOORE/tree/main/.claude/skills/scbe-training-pipeline
Command: npx skills add https://github.com/issdandavis/SCBE-AETHERMOORE --skill scbe-training-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Centralizes end-to-end management of the Ouroboros SFT training workflow—from collecting sources to publishing data and triggering model training.

Core Features & Use Cases

  • Merge and deduplicate multiple SFT sources from training-data (instructions, knowledge bases, kernel data, and more).
  • Enforce quality gates, track corpus statistics, and generate new SFT pairs from the codebase.
  • Trigger training runs and push artifacts to HuggingFace for rapid iteration and governance.

Quick Start

Merge new SFT sources, validate quality, and push results to HuggingFace to start a training cycle.

Frequently Asked Questions about scbe-training-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate an end-to-end SFT data pipeline from codebase to model deployment?▼

An SFT data pipeline automates merging, validating, and generating training data, then triggers training and uploads models to HuggingFace. This Skill orchestrates that entire cycle, enforcing data quality and deduplication to produce a publishable corpus.

What's the best way to merge and deduplicate multiple SFT training-data sources?▼

Merging and deduplicating SFT training-data sources requires centralizing instructions and knowledge bases into a validated corpus. This pipeline applies quality gates and tracks corpus statistics to ensure clean, publishable SFT data.

Can I automatically trigger HuggingFace model training after generating SFT pairs?▼

Yes, you can trigger HuggingFace model training after generating SFT pairs. The pipeline automates training-cycle orchestration, pushing validated artifacts directly to HuggingFace for rapid iteration and governance.

How do I enforce quality gates and track corpus statistics for SFT training data?▼

Enforcing quality gates for SFT training data involves validating merged sources and tracking corpus statistics. This pipeline applies automated quality enforcement across the training workflow to maintain a clean, publishable dataset.

What is an SFT generation pipeline and when do I need it for automated training cycles?▼

An SFT generation pipeline creates supervised fine-tuning pairs from a codebase to feed model training. You need it when automating end-to-end training cycles, ensuring new data is continuously merged, validated, and published.

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