databricks-spark-structured-streaming

databricks-spark-structured-streaming is a skill for Claude Code, Codex from databricks/databricks-agent-skills. It costs 80 tokens per session (881 once invoked), scanned A, original, no licence file.

A guide for Spark Structured Streaming, the part of Apache Spark that processes data continuously as it arrives. It covers streaming from Kafka, a message-streaming system, along with timing, saved progress, and stateful operations.

In plain words
What is it for?
Use it to build real-time data pipelines, ingest Kafka events, configure processing triggers, manage watermarks and checkpoints, or optimize streaming jobs.
Why use it?
It helps developers design streaming pipelines that can recover their progress, handle late data, and process ongoing workloads reliably. It also explains production settings and performance considerations.

Skill for Claude CodeCodex

Part of the databricks plugin — 31 skills shipped together

Install

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.

agentmods
npx agentmods add skills/databricks/databricks-agent-skills/databricks-spark-structured-streaming
Any agent
npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming
Clone the repo
git clone --depth 1 https://github.com/databricks/databricks-agent-skills

Made for: Claude Code, Codex.

Or install databricks, the plugin that ships this one along with the rest of its 31 skills.

Wrote 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.

agentmods badge for databricks-spark-structured-streaming

README.md
[![agentmods](https://agentmods.dev/badge/skills/databricks/databricks-agent-skills/databricks-spark-structured-streaming.svg)](https://agentmods.dev/skills/databricks/databricks-agent-skills/databricks-spark-structured-streaming)
Your own site
<a href="https://agentmods.dev/skills/databricks/databricks-agent-skills/databricks-spark-structured-streaming"><img src="https://agentmods.dev/badge/skills/databricks/databricks-agent-skills/databricks-spark-structured-streaming.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 881 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.00881
Opus 5 $0.00040 $0.00441
Sonnet 5 $0.00016 $0.00176
Haiku 4.5 $0.00008 $0.00088

Measured 5d ago against content hash 0bef93492116, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

databricks-spark-structured-streaming 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.

plugins/databricks/claude/skills/databricks-spark-structured-streaming/SKILL.md · 72 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

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.

  1. 5d ago First seen · 72 lines · 80 tokens per session scan A 0bef93492116

Subscribe to this mod's changes

databricks-spark-structured-streaming is a skill published in the GitHub repository databricks/databricks-agent-skills (300 stars, last pushed 2d ago), with no licence file. It adds 80 tokens to every session and 881 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

agent-platform-rag-engine-management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…

google/skills · 85 tokens

agent-platform-model-registry

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

google/skills · 60 tokens

foundry-config-setup

Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.

microsoft/agent-framework · 65 tokens

google-cloud-solution-agentic-analytics-spark-knowledge-catalog

Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…

google/skills · 138 tokens

training-check

Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.

wanshuiyin/Auto-claude-code-research-in-sleep · 35 tokens

nemo-automodel-launcher-config

Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.

NVIDIA/skills · 30 tokens