Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jnPiyush/AgentX --skill synthetic-data-generationgit clone --depth 1 https://github.com/jnPiyush/AgentXWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jnpiyush/agentx/synthetic-data-generation)<a href="https://agentmods.dev/skills/jnpiyush/agentx/synthetic-data-generation"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/synthetic-data-generation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jnpiyush/agentx/synthetic-data-generation"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/synthetic-data-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01118 |
| Opus 5.5 | $0.00027 | $0.00447 |
| Sonnet 5.5 | $0.00014 | $0.00224 |
| Haiku 4.5 | $0.00007 | $0.00112 |
Grade A, and why
synthetic-data-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Synthetic Data Generation
Purpose: Build training and eval datasets quickly without violating user privacy, while controlling for quality, diversity, and contamination.
When to Use This Skill
- Bootstrapping eval sets when you have no real labeled data
- Augmenting fine-tuning data for under-represented intents / slices
- Generating adversarial test cases (combine with
ai-safety-and-red-teaming) - Creating QA pairs over a private RAG corpus
- Privacy-safe substitutes for production data in dev/test
When NOT to Use
- If real, well-labeled data exists at sufficient scale -- use it
- For final benchmark numbers in regulated decisions -- use human-labeled holdouts
Common Techniques
| Technique | Use For |
|---|---|
| Self-Instruct | Generate instruction/response pairs from a small seed |
| Evol-Instruct | Iteratively rewrite prompts to be deeper, broader, harder |
| Persona-based | Generate from N personas to enforce demographic / role diversity |
| Distillation | Strong-model answers used to train smaller model (check provider terms) |
| Back-translation | Multilingual coverage |
| Counterfactual augmentation | Flip protected attributes to test fairness |
| Programmatic / templated | Slot-fill structured data with controlled noise |
| RAG-grounded QA | Generate Q & A from each chunk; ideal for eval sets |
Pipeline
1. Define target distribution and slices (intents, difficulty, demographics)
2. Choose generator model (often a strong reasoning model)
3. Seed with examples or schema
4. Generate at over-sample rate (2-5x target)
5. Quality filter: schema validation, toxicity, dedupe
6. Diversity filter: embedding clustering, slice balancing
7. Decontamination: remove overlap with eval/test sets
8. Human review on a sample (5-10%)
9. Dataset card + version + provenance
Quality Filters
| Filter | Tool |
|---|---|
| Schema valid | JSON Schema, Pydantic |
| Length sane | Token count bounds |
| Language correct | fastText / lid.176 |
| Toxicity / unsafe | LlamaGuard, Azure Content Safety |
| PII present | Presidio |
| Dedupe (near) | MinHash / SimHash, embedding cosine |
| Factuality (RAG) | Citation present and supports claim |
| Difficulty balanced | LLM judge classifies easy/medium/hard |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed 5d14c8a303fe
- 1mo ago First seen · 140 lines · 68 tokens per session scan A f138a3fdc7ea
synthetic-data-generation is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 68 tokens to every session and 1,118 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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