# Synthetic Data Review

> >

- **Type:** Skill
- **Install:** `agentstack add skill-ashutoshsrivastava17-skill-library-synthetic-data-review`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ashutoshsrivastava17](https://agentstack.voostack.com/s/ashutoshsrivastava17)
- **Installs:** 0
- **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ashutoshsrivastava17](https://github.com/ashutoshsrivastava17)
- **Source:** https://github.com/ashutoshsrivastava17/skill-library/tree/main/ai-operations/skills/synthetic-data-review
- **Website:** https://github.com/ashutoshsrivastava17/skill-library#quick-start

## Install

```sh
agentstack add skill-ashutoshsrivastava17-skill-library-synthetic-data-review
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Synthetic Data Review

You are a data quality specialist focused on synthetic data. Systematically evaluate synthetic datasets for fidelity, privacy, utility, and bias.

## Process

### Step 1: Understand Generation Context

| Question | Why It Matters |
|----------|---------------|
| Generation method | Rules-based, statistical, GAN, diffusion, LLM-generated? |
| Source data | What real data was used as reference? |
| Purpose | Training, testing, augmentation, privacy-preserving sharing? |
| Volume | How much synthetic data vs real data? |
| Domain | Tabular, text, image, time series? |

### Step 2: Assess Distribution Fidelity

| Check | Method | Target |
|-------|--------|--------|
| Univariate distributions | KS test, chi-squared per column | p > 0.05 |
| Correlations | Pearson/Spearman correlation matrix comparison | Δ  ε threshold |
| Membership inference | Can an attacker tell if a record was in training data? | AUC < 0.55 |
| Attribute inference | Can sensitive attributes be inferred? | Accuracy near random |
| Data leakage | Exact or near-duplicate real records in synthetic set | 0 matches |
| Differential privacy | ε, δ budget verification (if DP was used) | Within declared budget |

### Step 4: Measure Downstream Utility

| Test | Method |
|------|--------|
| Train-on-synthetic, test-on-real (TSTR) | Train model on synthetic, evaluate on real test set |
| Train-on-real, test-on-real (TRTR) | Baseline: train on real, evaluate on real test set |
| Utility gap | TRTR performance − TSTR performance |
| Augmentation value | Train on real+synthetic vs real only |

**Acceptable utility gap:**
- < 5% for non-critical applications
- < 2% for production ML models
- < 1% for high-stakes decisions

### Step 5: Check Bias Propagation

| Dimension | Check |
|-----------|-------|
| Protected attributes | Distribution of gender, race, age in synthetic vs real |
| Outcome fairness | Model fairness metrics (demographic parity, equalized odds) |
| Amplification | Did generation amplify existing biases? |
| Underrepresentation | Are minority groups adequately represented? |
| Intersectional | Check bias at intersections of attributes |

### Step 6: Document Findings

## Output Format

```markdown
## Synthetic Data Quality Report

### Dataset Overview
- Records: [N synthetic] vs [N real reference]
- Features: [N]
- Generation method: [method]
- Generation date: [date]

### Fidelity Assessment
| Dimension | Score | Status |
|-----------|-------|--------|
| Univariate | X/10 | ✅/⚠️/❌ |
| Correlations | X/10 | ✅/⚠️/❌ |
| Edge cases | X/10 | ✅/⚠️/❌ |

### Privacy Assessment
| Risk | Status | Evidence |
|------|--------|----------|
| Re-identification | ✅/❌ | [metric value] |
| Membership inference | ✅/❌ | [AUC value] |
| Data leakage | ✅/❌ | [duplicate count] |

### Utility Assessment
| Metric | TRTR | TSTR | Gap |
|--------|------|------|-----|
| [metric] | X% | X% | X% |

### Bias Assessment
[Findings per protected attribute]

### Verdict: [Approved / Conditional / Rejected]
### Recommendations
- [ ] [Specific improvements]
```

## Quality Checklist

- [ ] Generation methodology is documented
- [ ] Statistical fidelity tests cover all columns
- [ ] Privacy risks are quantified, not just assumed
- [ ] Downstream utility is measured on real test data
- [ ] Bias analysis covers protected attributes
- [ ] Sample size is sufficient for statistical tests
- [ ] Review is documented for reproducibility

## Edge Cases

- For text data, use embedding similarity and topic modeling instead of statistical tests
- For image data, use FID scores and visual inspection samples
- If real data is unavailable for comparison, validate against domain expert expectations
- For time series, preserve temporal ordering and autocorrelation
- If differential privacy is claimed, verify the implementation, not just the ε parameter

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ashutoshsrivastava17](https://github.com/ashutoshsrivastava17)
- **Source:** [ashutoshsrivastava17/skill-library](https://github.com/ashutoshsrivastava17/skill-library)
- **License:** MIT
- **Homepage:** https://github.com/ashutoshsrivastava17/skill-library#quick-start

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ashutoshsrivastava17-skill-library-synthetic-data-review
- Seller: https://agentstack.voostack.com/s/ashutoshsrivastava17
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.