synthetic-data-generation
LLM-generated training data, augmentation strategies, distillation datasets, self-instruct and Evol-Instruct patterns, quality filtering pipelines.
npx skills add majiayu000/claude-skill-registry --skill synthetic-data-generation-jlaws-dotfiles --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Synthetic Data Generation ## When to Use Generate synthetic training data when real data is scarce, expensive to label, or restricted by privacy/licensing. Also applies to distillation pipelines where a larger model generates training signal for a smaller one. ## Strategy Selection ### Decision Table: Synthetic Data Strategy by Use Case | Use Case | Strategy | Quality Bar | Volume Needed | Key Risk | |----------|----------|-------------|---------------|----------| | Instruction tuning (general) | Self-Instruct | Medium-High | 10K-100K | Diversity collapse | | Instruction tuning (complex) | Evol-Instruct | High | 5K-50K | Over-complexity drift | | Domain adaptation | Paraphrasing + seeding | Medium | 50K-500K | Distribution shift | | Low-resource language | Back-translation | Medium | 10K-100K | Translationese artifacts | | Distillation | Teacher rationale generation | High | 10K-1M | Capacity gap noise | | Classification augmentation | Label-conditioned generation | Medium | 10K-100K | Label leakage | | Safety/alignment | Red-teaming + refusal pairs | High | 1K-10K | Reward hacking patterns | ### Decision Table: Quality vs Quantity Tradeoff | Data Budget | Filtering Strategy | Ex
- When to Use
- Strategy Selection
- Decision Table: Synthetic Data Strategy by Use Case
- Decision Table: Quality vs Quantity Tradeoff
- Self-Instruct Pipeline
- Core Loop
- Evol-Instruct Enhancement
- Quality Filtering
- LLM-as-Judge
- Diversity Sampling
- Back-Translation Augmentation
- Gotchas and Anti-Patterns
- Model Collapse from Synthetic Data Loops
- Diversity Collapse
What does the synthetic-data-generation skill do?
LLM-generated training data, augmentation strategies, distillation datasets, self-instruct and Evol-Instruct patterns, quality filtering pipelines.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill synthetic-data-generation-jlaws-dotfiles --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.