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 skills add j4flmao/agent-skills --skill distributed-computegit clone --depth 1 https://github.com/j4flmao/agent-skillsWrote 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/j4flmao/agent-skills/distributed-compute)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/distributed-compute"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/distributed-compute/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/j4flmao/agent-skills/distributed-compute"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/distributed-compute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00085 | $0.05150 |
| Opus 5.5 | $0.00034 | $0.02060 |
| Sonnet 5.5 | $0.00017 | $0.01030 |
| Haiku 4.5 | $0.00009 | $0.00515 |
Grade A, and why
data-distributed-compute 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 1mo 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 — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Distributed Compute
Purpose
Design and tune distributed compute systems for large-scale data processing. Select the right framework (Spark, Dask, Ray, MapReduce), configure YARN/K8s resource management, optimize shuffle and data locality, and tune executors for throughput.
Agent Protocol
Trigger
Exact user phrases: "Hadoop MapReduce", "Spark", "Dask", "Ray", "YARN", "cluster computing", "resource manager", "shuffle", "data locality", "executor", "worker", "task scheduling", "distributed compute", "cluster mode", "dynamic allocation", "speculative execution".
Input Context
Before activating, verify:
- Compute framework preference (Spark, Dask, Ray, MapReduce)
- Data size and shape (TB per run, row counts, join complexity)
- Cluster size and resource per node (cores, memory, network)
- Workload type (batch ETL, ML training, real-time inference, iterative algorithms)
- Storage backend (HDFS, S3, local SSD)
- Scheduling layer (YARN, K8s, standalone)
Output Artifact
Distributed compute architecture with framework selection, cluster configuration, and tuning parameters.
Response Format
Compute Framework: {Spark | Dask | Ray | MapReduce}
Cluster Mode: {YARN | K8s | Standalone | Slurm}
Execution Model: {driver-executor | scheduler-worker | GCS}
Resource: {N executors x M cores x G memory}
Shuffle: {sort-based | hash-based | external}
Locality: {PROCESS_LOCAL | NODE_LOCAL | RACK_LOCAL | ANY}
# spark-submit or Ray cluster config
# Tuning parameters
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Framework selected with trade-off analysis
- Cluster resource config calculated (executors, cores, memory, overhead)
- Shuffle strategy defined with spill/tune settings
- Data locality configuration set
- Speculative execution policy defined
- Dynamic allocation or static partitioning configured
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/distributed-compute-advanced.md 12 KB
- references/distributed-compute-fundamentals.md 11 KB
- references/distributed-compute-memory.md 4.0 KB
- references/distributed-compute-scheduling.md 3.9 KB
- references/distributed-frameworks.md 4.9 KB
- references/gpu-accelerated-compute.md 3.8 KB
- references/polars-dataframe.md 4.2 KB
- references/spark-execution.md 4.2 KB
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.
- 1mo ago First seen · 558 lines · 85 tokens per session scan A aa7233a749e4
data-distributed-compute is a skill published in the GitHub repository j4flmao/agent-skills (34 stars, last pushed 12d ago), licensed MIT. It adds 85 tokens to every session and 5,150 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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