# Sv Data

> Build and manage Security Verifiers datasets. Use when asked to build E1 or E2 datasets, create test fixtures, validate data, or manage dataset files for network-logs or config-verification environments. Use when this capability is needed.

- Skill: `tomevault-io/sv-data` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/sv-data`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/sv-data/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/sv-data

---


# Security Verifiers Data Management

Build, validate, and manage datasets for E1 (network-logs) and E2 (config-verification) environments.

## Dataset Types

| Type | Purpose | Location | Committed |
|------|---------|----------|-----------|
| Production | Full training/eval data | `environments/sv-env-*/data/` | No (private) |
| Test fixtures | CI/unit tests | `environments/sv-env-*/data/` | Yes (small) |
| HuggingFace | Remote dataset access | HF Hub | Private repos |

## E1: Network Logs Datasets

### Build Production Dataset (IoT-23)

```bash
# Full dataset (1800 examples, 60/20/20 split)
make data-e1

# Custom limit
make data-e1 LIMIT=3000
```

Outputs: `environments/sv-env-network-logs/data/iot23-train-dev-test-v1.jsonl`

### Build OOD (Out-of-Distribution) Datasets

```bash
# CIC-IDS-2017 and UNSW-NB15 (600 examples each)
make data-e1-ood

# Custom count
make data-e1-ood N=1000
```

Outputs:
- `cic-ids-2017-ood-v1.jsonl`
- `unsw-nb15-ood-v1.jsonl`

### Build Test Fixtures

```bash
# Small datasets for CI (20-30 examples)
make data-e1-test
```

## E2: Config Verification Datasets

### Clone Source Repositories

E2 datasets are built from real Kubernetes and Terraform configs:

```bash
# Clone recommended repos
make clone-e2-sources
```

Clones to `scripts/data/sources/`:
- `kubernetes/` - K8s YAML manifests
- `terraform/` - Terraform HCL configs

### Build Production Dataset

```bash
# Using cloned sources
make data-e2-local

# Using custom paths
make data-e2 K8S_ROOT=/path/to/k8s TF_ROOT=/path/to/terraform
```

Outputs:
- `environments/sv-env-config-verification/data/k8s-labeled-v1.jsonl`
- `environments/sv-env-config-verification/data/terraform-labeled-v1.jsonl`

### Build Test Fixtures

```bash
# Requires clone-e2-sources first
make clone-e2-sources
make data-e2-test
```

## Build All Datasets

```bash
# All E1 production datasets
make data-all

# All test fixtures (for CI)
make data-test-all
```

## Data Validation

Validate datasets with Pydantic before HuggingFace push:

```bash
# Validate E1 splits
make validate-e1-data

# Validate E2 splits
make validate-e2-data

# Validate all
make validate-data
```

## Dataset Schema

> **Note:** Examples below show schema structure only. Actual benchmark data is gated to prevent training contamination. See `plans/ROADMAP-Q1-2026.md` for benchmark integrity policy.

### E1 Schema (network-logs)

Hub/local JSONL format:
```json
{
  "question": "<network log entry - content gated>",
  "answer": "Benign|Malicious",
  "meta": {
    "source": "<dataset source>",
    "scenario": "<capture scenario>",
    "attack_family": "<attack type if malicious>",
    "hash": "<content hash>",
    "split": "train|dev|test"
  }
}
```

### E2 Schema (config-verification)

Hub/local JSONL format:
```json
{
  "question": "<k8s/terraform config - content gated>",
  "info": {
    "violations": [
      {
        "tool": "kube-linter|semgrep|opa",
        "rule_id": "<rule identifier>",
        "severity": "low|medium|high",
        "msg": "<violation message>",
        "loc": "<file:line if available>"
      }
    ],
    "patch": "<optional unified diff>"
  },
  "meta": {
    "lang": "k8s|terraform",
    "source": "<source repository>",
    "hash": "<content hash>"
  }
}
```

### Schema Conversion

When datasets are loaded, the environment converts them to internal format:

- **E1**: `question` → prompt, `answer` → expected label
- **E2**: `question` → prompt, `info` → `answer` (JSON string with oracle violations)

The conversion happens in `_convert_e2_format()` in `sv_env_config_verification.py`.

## Dataset Locations

| Environment | Data Directory |
|-------------|----------------|
| E1 network-logs | `environments/sv-env-network-logs/data/` |
| E2 config-verification | `environments/sv-env-config-verification/data/` |

## Loading Datasets

Environments support multi-tier loading:
1. **Local**: JSONL files in `data/` directory
2. **Hub**: HuggingFace (requires HF_TOKEN)
3. **Synthetic**: Built-in test fixtures (fallback)

```python
import verifiers as vf

# Auto mode (tries local → hub → synthetic)
env = vf.load_environment("sv-env-network-logs")

# Explicit source
env = vf.load_environment("sv-env-network-logs", dataset_source="local")
env = vf.load_environment("sv-env-network-logs", dataset_source="hub")
env = vf.load_environment("sv-env-network-logs", dataset_source="synthetic")
```

## Troubleshooting

**HF_TOKEN required**: Set in `.env` for gated dataset access.
**Missing sources**: Run `make clone-e2-sources` before E2 data builds.
**Validation fails**: Check schema matches expected Pydantic models in `scripts/data/validate_splits_*.py`.

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/intertwine) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-13 -->