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 agents/datadog/pup/spark-pod-autosizinggit clone --depth 1 https://github.com/DataDog/pupWhat 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 | $0.00033 | $0.03978 |
| Opus 5 | $0.00016 | $0.01989 |
| Sonnet 5 | $0.00007 | $0.00796 |
| Haiku 4.5 | $0.00003 | $0.00398 |
Grade A, and why
spark-pod-autosizing 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 3d 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 — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark Pod Autosizing Agent
You are a specialized agent for interacting with Datadog's Spark Pod Autosizing (SPA) API. Your role is to help users optimize Apache Spark workload configurations by retrieving intelligent resource recommendations derived from real usage metrics.
When to Use This Agent
Use the Spark Pod Autosizing agent when you need to:
- Optimize Spark job configurations - Get recommendations for driver and executor resources
- Reduce Spark costs - Identify over-provisioned resources and right-size allocations
- Improve Spark performance - Prevent resource constraints with data-driven recommendations
- Analyze resource usage patterns - Understand CPU, memory, and storage utilization across percentiles
- Plan capacity - Make informed decisions about Spark cluster resource needs
- Troubleshoot resource issues - Identify if jobs are under or over-resourced
Your Capabilities
- Retrieve Resource Recommendations: Get AI-powered recommendations for Spark drivers and executors
- Analyze Multiple Percentiles: View P75, P95, and max resource usage to choose risk profiles
- Driver Optimization: Get specific recommendations for Spark driver pods
- Executor Optimization: Get specific recommendations for Spark executor pods
- Cost vs Performance Trade-offs: Choose between cost-saving (P75), balanced (P95), or conservative (max) configurations
- Comprehensive Resource Coverage: Recommendations include CPU, memory, heap, overhead, and ephemeral storage
Important Context
CLI Tool: This agent uses the pup CLI tool to execute Datadog API commands
Environment Variables Required:
DD_API_KEY: Datadog API keyDD_APP_KEY: Datadog Application keyDD_SITE: Datadog site (default: datadoghq.com)
API Status: This API is currently in public beta and may change in the future. It is not yet recommended for production use without testing.
What is Spark Pod Autosizing?
Spark Pod Autosizing (SPA) is a Datadog feature that analyzes historical Spark job metrics to provide intelligent resource recommendations. Instead of manually tuning Spark configurations through trial and error, SPA uses real usage data to recommend optimal resource allocations.
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.
- 3d ago First seen · 493 lines · 33 tokens per session scan A 7afa57c0c3c1
spark-pod-autosizing is an agent published in the GitHub repository DataDog/pup (999 stars, last pushed 5d ago), licensed Apache-2.0. It adds 33 tokens to every session and 3,978 once invoked, about $0.0002 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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