ray-data SKILL.md skillA
ray-data from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add orchestra-research/ai-research-skills/ray-data@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- orchestra-research/ai-research-skills
- Path
05-data-processing/ray-data/SKILL.md- Versions recorded
- 5
- Latest version
- v1.0.0, committed Feb 2, 2026
- First version recorded
- Nov 20, 2025
- Size
- 7,328 bytes
- Repository stars
- 13,718
- License
- MIT
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Ray Data - Scalable ML Data Processing
Distributed data processing library for ML and AI workloads.
## When to use Ray Data
**Use Ray Data when:**
- Processing large datasets (>100GB) for ML training
- Need distributed data preprocessing across cluster
- Building batch inference pipelines
- Loading multi-modal data (images, audio, video)
- Scaling data processing from laptop to cluster
**Key features**:
- **Streaming execution**: Process data larger than memory
- **GPU support**: Accelerate transforms with GPUs
- **Framework integration**: PyTorch, TensorFlow, HuggingFace
- **Multi-modal**: Images, Parquet, CSV, JSON, audio, video
**Use alternatives instead**:
- **Pandas**: Small data (<1GB) on single machine
- **Dask**: Tabular data, SQL-like operations
- **Spark**: Enterprise ETL, SQL queries
## Quick start
### Installation
```bash
pip install -U 'ray[data]'
```
### Load and transform data
```python
import ray
# Read Parquet files
ds = ray.data.read_parquet("s3://bucket/data/*.parquet")
# Transform data (lazy execution)
ds = ds.map_batches(lambda batch: {"processed": batch["text"].str.lower()})
# Consume data
for batch in ds.iter_batches(batch_size=100):
…Read the whole file at its exact version.
How to install
mdr add orchestra-research/ai-research-skills/ray-data@sha256:bcc73b3341dca18cThe line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_7srnubmeia6zmigu)
2 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 5 versions of this file, between Nov 20, 2025 and Feb 2, 2026, about one every 19 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-02-02 | 13b73c2 | 7,328 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 0a9ded4 | 7,326 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 72c8b2c | 7,326 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 85a6d14 | 7,325 B | A | view · diff |
| v1.0.0 | 2025-11-20 | f38509b | 7,324 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (7328 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_7srnubmeia6zmigu GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/ray-data GET https://markdownregistry.com/api/v1/blob/bcc73b3341dca18c59cad092cb6dedb7ac0bf0bde80467a039921ac0c02256c3
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.