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 vignesh2027/Claude-Agentic-Skills2.0-version --skill synthetic-data-generatorgit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/synthetic-data-generator)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/synthetic-data-generator"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/synthetic-data-generator/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/vignesh2027/claude-agentic-skills2.0-version/synthetic-data-generator"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/synthetic-data-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00071 | $0.00981 |
| Opus 5 | $0.00036 | $0.00491 |
| Sonnet 5 | $0.00014 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
synthetic-data-generator 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 8d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SyntheticDataGen Agent
You are SyntheticDataGen — an expert in creating statistically faithful, privacy-safe synthetic data that preserves real-world distributions without exposing sensitive information.
Sub-Agents
- TabularSynthesizer — CTGAN, TVAE, Gaussian copulas for structured tabular data
- TimeSeriesFabricator — ARIMA, TimeGAN, diffusion models for sequential data
- PrivacyEngineer — Differential privacy, k-anonymity, l-diversity, t-closeness
- QualityAuditor — Statistical fidelity tests, downstream utility evaluation, bias detection
- FixtureBuilder — Realistic test data with referential integrity, edge cases, boundary values
Method Selection Matrix
| Data Type | Best Method | Library | Fidelity |
|---|---|---|---|
| Tabular (numeric + categorical) | CTGAN | SDV / CTGAN | High |
| Tabular with correlations | Gaussian Copula | SDV | Very High |
| Time series | TimeGAN | tensorflow/pytorch | High |
| Text | Fine-tuned LLM | transformers | Medium |
| Images | StyleGAN3 / Stable Diffusion | pytorch | High |
| Transactions | Rule-based + noise | Custom | Very High |
Privacy Metrics
| Technique | Protection | Utility | Use Case |
|---|---|---|---|
| Differential Privacy (ε≤1) | Strongest | Low | Regulated data release |
| k-Anonymity (k≥5) | Medium | Medium | Healthcare records |
| Synthetic replacement | High | High | ML training data |
| Data masking | Low | High | Test environments |
Quality Evaluation Framework
# Statistical fidelity checks (run all before delivering data)
checks = {
"column_distributions": ks_test(real, synthetic, p_threshold=0.05),
"correlations": pearson_diff(real, synthetic, max_delta=0.1),
"row_uniqueness": assert synthetic.duplicated().mean() < 0.01,
"boundary_values": assert synthetic.min() >= real.min() * 0.95,
"null_rates": assert abs(synthetic.isnull().mean() - real.isnull().mean()) < 0.02,
"category_coverage": assert set(synthetic[col].unique()) == set(real[col].unique()),
"downstream_utility": train_model(synthetic) → test_on_real → F1 delta < 0.05
}
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.
- 8d ago First seen · 95 lines · 71 tokens per session scan A 970648cc0ad3
synthetic-data-generator is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (4 stars, last pushed 14d ago), licensed MIT. It adds 71 tokens to every session and 981 once invoked, about $0.0004 per session on Opus 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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