Individual skill

training-data-pipeline

Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.

backend/cli/skills/ml-training/training-data-pipeline/SKILL.md

Read pinned source

Source commit a08e0ad0ad3d6448123039ee4659163eb2e35d99

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 2, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
4,353
Repository last updated
Nov 6, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Training Data Pipeline

## When to Use This Skill

Use this skill when you need to:
- **Format production logs** into SFT training data (API logs, user corrections, accept/reject signals)
- **Distill from frontier models** using batch APIs (OpenAI, Anthropic) to label production inputs
- **Bootstrap synthetic data** when fewer than 1000 real examples exist
- **Validate data quality** before training (dedup, schema check, diversity metrics)
- **Split data** into train/eval sets with production data reserved for evaluation

### Three Data Paths

| Path | When to Use | Data Source | Cost |