---
license: mit
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - benchmark
  - ai-agents
  - skill-evaluation
  - code-generation
  - document-processing
  - neurips-2026
pretty_name: SkillArena Offline Datasets
size_categories:
  - 10K<n<100K
---

# SkillArena Offline Datasets

Offline evaluation data for [SkillArena](https://github.com/Jiaaqiliu/Skill_Arena) — a validated automatic benchmark generation framework for AI agent skills, targeting **NeurIPS 2026 Datasets & Benchmarks Track**.

## Overview

This dataset provides **domain-specific task input data** for 289 AI agent skills across 13 domains. Each skill has **50 curated data files** designed as meaningful agent task inputs — files an agent could receive and act upon (analyze, transform, validate, or generate from). The data is used by SkillArena's 10-stage evaluation pipeline to automatically generate validated evaluation tasks, graders, and pilot tests.

| Metric | Value |
|--------|-------|
| **Total skills** | 289 (16 original + 73 community + 200 new community) |
| **Total files** | 14,450 (skills) + 160 (original-skills) |
| **Files per skill** | 50 |
| **Dataset size** | ~126 MB |
| **File formats** | 5 types: CSV, JSON, YAML, Markdown, Plain Text |
| **Generation method** | Deterministic, domain-aware, seed-reproducible |

## Dataset Structure

```
skillarena-datasets/
├── skills/                         # 289 skills × 50 files = 14,450 files
│   ├── scikit-learn/               # ML training domain
│   │   ├── ml_training_csv_00.csv  # Classification dataset
│   │   ├── ml_training_csv_01.csv  # Regression dataset
│   │   ├── ...                     # 18 more CSV files
│   │   ├── ml_training_json_00.json # Model config
│   │   ├── ...                     # 11 more JSON files
│   │   ├── ml_training_yaml_00.yaml # Training pipeline config
│   │   ├── ...                     # 9 more YAML files
│   │   ├── ml_training_md_00.md    # Model card
│   │   ├── ...                     # 4 more Markdown files
│   │   ├── ml_training_txt_00.txt  # Training log
│   │   └── ...                     # 2 more text files
│   ├── langchain/                  # NLP/LLM domain
│   ├── grafana-dashboards/         # DevOps/Infra domain
│   ├── rdkit/                      # Chemistry domain
│   └── ...                         # 285 more skill directories
├── original-skills/                # 16 core skills (hand-curated, 10 files each)
│   ├── pdf/                        # PDFs, extraction configs
│   ├── xlsx/                       # Spreadsheets, CSV data
│   ├── frontend-design/            # HTML, CSS, JS, design specs
│   └── ...                         # 13 more original skills
└── index.json                      # Dataset index with file metadata
```

## File Distribution Per Skill

Every skill receives exactly **50 files** with a fixed distribution:

| Count | Format | Purpose |
|:-----:|--------|---------|
| 20 | `.csv` | Tabular data for analysis and transformation tasks |
| 12 | `.json` | Structured data, configs, and specifications |
| 10 | `.yaml` | Configuration and specification files |
| 5 | `.md` | Documents for analysis and generation tasks |
| 3 | `.txt` | Plain text for processing tasks |

**Total across all skills**: 5,780 CSV + 3,468 JSON + 2,890 YAML + 1,445 MD + 867 TXT = 14,450 files

## Domain Classification

Skills are classified into 13 domains, each with a specialized data generator:

| Domain | Skills | Example Skills |
|--------|:------:|----------------|
| NLP / LLM | 82 | langchain, llamaindex, huggingface-tokenizers, dspy, outlines |
| Frontend / Web | 53 | react-state-management, tailwind-design-system, nextjs-app-router-patterns |
| Bioinformatics | 27 | biopython, scanpy, anndata, pysam, clinical-decision-support |
| Generic | 23 | file-organizer, contract-analyzer, financial-calc, create-plan |
| Backend | 21 | kafka-producer-consumer, redis-cache-manager, database-schema-designer |
| DevOps / Infra | 18 | k8s-manifest-generator, grafana-dashboards, prometheus-configuration |
| Data Analysis | 16 | pandas (dask, geopandas), seaborn, matplotlib, statsmodels |
| ML Training | 15 | scikit-learn, pytorch-lightning, deepspeed, weights-and-biases |
| Documentation | 12 | changelog-generator, architecture-diagram-creator, cli-demo-generator |
| Chemistry | 9 | rdkit, deepchem, matchms, molfeat, torchdrug |
| Quantum | 5 | qiskit, cirq, pennylane, qutip, sparse-autoencoder-training |
| Security | 5 | secret-scanner, jwt-token-validator, sast-configuration |
| Testing | 3 | bats-testing-patterns, e2e-testing-patterns, temporal-python-testing |

## Data Content Examples

### CSV — ML Training (scikit-learn)
```csv
feature_0,feature_1,feature_2,feature_3,target,split
0.83,0.17,-0.45,1.02,1,train
-0.22,0.91,0.33,-0.67,0,train
```

### JSON — NLP/LLM (langchain)
```json
{
  "chain_name": "qa_retrieval_chain",
  "chain_type": "stuff",
  "retriever": {
    "type": "vectorstore",
    "search_type": "similarity",
    "search_kwargs": {"k": 4}
  }
}
```

### YAML — DevOps (k8s-manifest-generator)
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-gateway
  namespace: production
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
```

## How It's Used

SkillArena's pipeline uses these files in the **Data Acquisition** stage:

```
SKILL.md → Analyze → Plan → Synthesize → [Data Acquisition] → Grade → Validate → Meta-Eval → Pilot → Promote
                                                  ↑
                                         This dataset provides
                                         input data for tasks
```

The `OfflineDataProvider` is the first source in an 8-provider chain:

```
Offline Dataset (this) → GitHub → Web Search → Kaggle → HuggingFace → Programmatic → Binary Renderer → LLM Synthesis
```

When a task is generated, the provider:
1. Looks up `skills/{skill_name}/` directory
2. Picks the **first unused file** (sorted alphabetically)
3. Copies it to `task_dir/input/input.{ext}` as the agent's input

## Original Skills (16)

These hand-curated skills have been validated through the full SkillArena pipeline:

| Skill | Domain | Quality Score | Files |
|-------|--------|:------------:|:-----:|
| frontend-design | Frontend/UI | 9.95/10 | 10 |
| theme-factory | Design Systems | 9.95/10 | 10 |
| web-artifacts-builder | Web Development | 9.95/10 | 10 |
| webapp-testing | Testing | 9.84/10 | 10 |
| mcp-builder | MCP Protocol | 9.44/10 | 10 |
| algorithmic-art | Creative Coding | 9.43/10 | 10 |
| canvas-design | Visual Design | 9.42/10 | 10 |
| internal-comms | Communication | 9.41/10 | 10 |
| slack-gif-creator | Animation | 9.41/10 | 10 |
| brand-guidelines | Brand Design | 9.41/10 | 10 |
| doc-coauthoring | Documentation | 9.38/10 | 10 |
| skill-creator | Meta-Skills | 9.37/10 | 10 |
| pptx | Presentations | 8.60/10 | 10 |
| docx | Documents | 7.86/10 | 10 |
| pdf | PDF Processing | 7.36/10 | 10 |
| xlsx | Spreadsheets | 7.17/10 | 10 |

**Average quality score: 9.12/10** | **Validation pass rate: 100%** (162/162 tasks)

## Generation & Validation

All 14,450 files are **deterministically generated** using domain-specific generators with per-skill random seeds (`hash(skill_name) & 0xFFFFFFFF`) for full reproducibility.

### Validation checks (all passing):
- File count: exactly 50 per skill (20 CSV + 12 JSON + 10 YAML + 5 MD + 3 TXT)
- Minimum file size: >= 500 bytes
- Format validity: CSV parseable with consistent column counts, JSON/YAML parseable
- CSV integrity: header + data rows, no empty columns, consistent column count
- Markdown: has headers and substantial content
- No source code leakage: no Python/Java/Go/JS imports in data files
- Filename convention: `{domain}_{ext}_{NN}.{ext}` pattern

### Regeneration

```bash
# Generate all skills
python -m scripts.generate_task_inputs.main --force

# Single skill
python -m scripts.generate_task_inputs.main --skill scikit-learn --force

# Validate only
python -m scripts.generate_task_inputs.main --validate-only

# Deep validation
python scripts/generate_task_inputs/deep_validate.py
```

## Usage

```python
from skillarena.pipeline.orchestrator import PipelineOrchestrator

orchestrator = PipelineOrchestrator(
    output_dir="pipeline_output",
    offline_data_dir="skillarena-datasets",  # Point to this dataset
    seed=42
)
state = await orchestrator.run(skill_path="skills/pdf/SKILL.md")
```

## Citation

```bibtex
@inproceedings{liu2026skillarena,
  title={SkillArena: Validated Automatic Benchmark Generation for AI Agent Skills},
  author={Liu, Jiaqi},
  booktitle={NeurIPS 2026 Datasets and Benchmarks Track},
  year={2026}
}
```

## License

MIT License — see the [SkillArena repository](https://github.com/Jiaaqiliu/Skill_Arena) for details.