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 agentmods add skills/dominodatalab/domino-claude-plugin/distributed-computingnpx skills add dominodatalab/domino-claude-plugin --skill distributed-computinggit clone --depth 1 https://github.com/dominodatalab/domino-claude-pluginWrote 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/dominodatalab/domino-claude-plugin/distributed-computing)<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/distributed-computing"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/distributed-computing.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.02124 |
| Opus 5 | $0.00031 | $0.01062 |
| Sonnet 5 | $0.00012 | $0.00425 |
| Haiku 4.5 | $0.00006 | $0.00212 |
Grade A, and why
domino-distributed-computing 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 5d 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 — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domino Distributed Computing Skill
Description
This skill helps users work with distributed computing frameworks in Domino - Spark, Ray, and Dask clusters for scaling compute-intensive workloads.
Activation
Activate this skill when users want to:
- Run Spark, Ray, or Dask clusters in Domino
- Scale data processing or ML training
- Configure distributed cluster settings
- Understand when to use each framework
Supported Frameworks
| Framework | Best For |
|---|---|
| Apache Spark | Large-scale data processing, SQL, ETL |
| Ray | Distributed ML, hyperparameter tuning, RL |
| Dask | Parallel pandas, NumPy at scale |
| MPI | Scientific computing, HPC workloads |
When to Use Each Framework
Spark
- Processing terabyte-scale data
- SQL analytics on big data
- ETL pipelines
- Structured data processing
Ray
- Distributed model training
- Hyperparameter optimization
- Reinforcement learning
- Generic Python parallelization
Dask
- Scaling pandas workflows
- Parallel NumPy operations
- Lazy evaluation needed
- Familiar pandas/NumPy API preferred
Launching On-Demand Clusters
Via Domino UI
- Start a workspace or job
- Check Attach compute cluster
- Select:
- Cluster Type: Spark, Ray, or Dask
- Worker Count: Number of workers
- Hardware Tier: Resources per worker
- Auto-scaling: Enable/disable
- Launch
Via Python SDK
from domino import Domino
domino = Domino("project-owner/project-name")
# Start workspace with Spark cluster
workspace = domino.workspace_start(
hardware_tier_name="medium",
cluster_config={
"clusterType": "Spark",
"workerCount": 4,
"workerHardwareTier": "medium",
"masterHardwareTier": "medium"
}
)
Apache Spark
Connecting to Spark
from pyspark.sql import SparkSession
# Domino auto-configures Spark
spark = SparkSession.builder.getOrCreate()
# Check configuration
print(f"Spark version: {spark.version}")
print(f"Executors: {spark.sparkContext.defaultParallelism}")
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.
- 5d ago First seen · 382 lines · 62 tokens per session scan A 9b3dd725980d
domino-distributed-computing is a skill published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 2,124 once invoked, about $0.0003 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 2026-08-30.
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