Windmill Trigger Type Checklist
windmill-labs/windmill
Checklist of every backend, frontend, CLI and capture change needed to add a new TriggerCrud-based trigger type, such as Azure, GCP or Kafka, to Windmill.
Official agent skill
Databricks Spark Structured Streaming
Comprehensive guide to Spark Structured Streaming for production workloads.
Install Databricks Spark Structured Streaming
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streaming --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .claude/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .claude/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streamingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streaming --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .agents/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .agents/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streaming --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .cursor/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .cursor/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/databricks/databricks-agent-skills.git --path skills/databricks-spark-structured-streaming--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streaming --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .gemini/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .gemini/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streamingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .github/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .github/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install databricks/databricks-agent-skills databricks-spark-structured-streaming --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/databricks/databricks-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/databricks-spark-structured-streaming .opencode/skills/databricks-spark-structured-streaming && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "databricks-spark-structured-streaming" agent skill from https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-spark-structured-streaming into .opencode/skills/databricks-spark-structured-streaming/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "databricks-spark-structured-streaming", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
databricks-spark-structured-streaming- GitHub stars
- 345
- Used in
- 1 other repo
- Token cost
- ~956 tokens
- SKILL.md length
- 202 words
- Files
- 15 (incl. references, assets)
- Skills in repo
- 32
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Comprehensive guide to Spark Structured Streaming for production workloads.
- Building streaming pipelines
- SKILL.md covers Quick Start, Core Patterns, Configuration and Best Practices, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Working with Kafka ingestion
What it does
Databricks Spark Structured Streaming is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including reference files and assets (for example `agents/openai.yaml`, `references/checkpoint-best-practices.md` and `references/kafka-streaming.md`). Compatibility notes: Requires databricks CLI (= v1.0.0)
It sits in Backend & APIs, covering Event-driven systems. It works with Databricks and Apache Kafka. The repository describes itself as: Databricks AI Tools: skills and plugins for building on Databricks with Claude Code, Cursor, Codex, GitHub Copilot, and other AI coding agents.
When your agent uses it
- Building streaming pipelines
- Working with Kafka ingestion
- Implementing Real-Time Mode (RTM)
- Configuring triggers (processingTime
Example prompts
- “/databricks-spark-structured-streaming”
Requirements
- Python 3
- Compatibility (from SKILL.md): Requires databricks CLI (>= v1.0.0)
What it can do on your machine
Read from SKILL.md and the folder at commit f4fcec5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Requires databricks CLI (>= v1.0.0)
From compatibility in the SKILL.md frontmatter.
Context cost
Databricks Spark Structured Streaming loads about 956 tokens when it runs, and up to ~40k if it reads all its reference files. Until then it costs about 115 tokens; SKILL.md has 202 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 202 words (~956 tokens).
“Production-ready streaming pipelines with Spark Structured Streaming. This skill provides navigation to detailed patterns and best practices.”
- name
- databricks-spark-structured-streaming
- compatibility
- Requires databricks CLI (>= v1.0.0)
- metadata.version
- 0.1.0
- parent
- databricks-core
Files
SKILL.md and 14 other files (references, assets) in skills/databricks-spark-structured-streaming of databricks/databricks-agent-skills.
- SKILL.md
- agents/openai.yaml
- assets/databricks.png
- assets/databricks.svg
- references/checkpoint-best-practices.md
- references/kafka-streaming.md
- references/lakebase-sink-python.md
- references/merge-operations.md
- references/multi-sink-writes.md
- references/real-time-mode.md
- references/stateful-operations.md
- references/stream-static-joins.md
- references/stream-stream-joins.md
- references/streaming-best-practices.md
- references/trigger-and-cost-optimization.md
Open the folder on GitHubat commit f4fcec5
Used in 1 other repository
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in databricks/databricks-agent-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Databricks Spark Structured Streaming next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Databricks Spark Structured Streaming this skilldatabricks/databricks-agent-skills | 345 | 1 repos | ~956 | Automated safety check: Pass | Custom licence | |
| Windmill Trigger Type Checklistwindmill-labs/windmill | 18k | — | ~4.7k | Automated safety check: Pass | Custom licence | |
| FoundatioFoundatioFx/Foundatio | 2.1k | — | ~3.9k | Automated safety check: Pass | Apache-2.0 | |
| Opensource Guide Coachcalf-ai/calfkit-sdk | 149 | 1 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Create Environmentgodatadriven/whirl | 205 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Monstermq Graphql Configvogler75/monster-mq | 143 | — | ~2.3k | Automated safety check: Pass | GPL-3.0 |
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Related
Works with
Categories
Questions about Databricks Spark Structured Streaming
What does Databricks Spark Structured Streaming do?
Comprehensive guide to Spark Structured Streaming for production workloads. Databricks Spark Structured Streaming is an agent skill from databricks/databricks-agent-skills, published by the product's own GitHub organization. Comprehensive guide to Spark Structured Streaming for production workloads.
When should I use Databricks Spark Structured Streaming?
Databricks Spark Structured Streaming fits situations like: building streaming pipelines; working with Kafka ingestion; implementing Real-Time Mode (RTM); configuring triggers (processingTime.
How do I install Databricks Spark Structured Streaming in Claude Code?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a claude-code`. Or copy the skill folder (skills/databricks-spark-structured-streaming in databricks/databricks-agent-skills) into .claude/skills/databricks-spark-structured-streaming in your project. Claude Code loads it when a task matches its description.
How do I install Databricks Spark Structured Streaming in Codex?
Run `npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a codex`. Or copy the skill folder (skills/databricks-spark-structured-streaming in databricks/databricks-agent-skills) into .agents/skills/databricks-spark-structured-streaming in your project. Codex loads it when a task matches its description.
Can I use Databricks Spark Structured Streaming in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add databricks/databricks-agent-skills --skill databricks-spark-structured-streaming -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/databricks-spark-structured-streaming, .gemini/skills/databricks-spark-structured-streaming, .github/skills/databricks-spark-structured-streaming and .opencode/skills/databricks-spark-structured-streaming in your project.
What does Databricks Spark Structured Streaming need to run?
SKILL.md names no scripts, command-line tools or credentials: Databricks Spark Structured Streaming is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires databricks CLI (>= v1.0.0).
Does Databricks Spark Structured Streaming access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Databricks Spark Structured Streaming safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Databricks Spark Structured Streaming use?
Databricks Spark Structured Streaming has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Databricks Spark Structured Streaming use?
About 956 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 39k tokens, read only when the agent opens those files.
What are the alternatives to Databricks Spark Structured Streaming?
Skills that share tags, products or a category with Databricks Spark Structured Streaming: Windmill Trigger Type Checklist (windmill-labs/windmill, 18k stars), Foundatio (FoundatioFx/Foundatio, 2.1k stars), Opensource Guide Coach (calf-ai/calfkit-sdk, 149 stars) and Create Environment (godatadriven/whirl, 205 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Databricks Spark Structured Streaming?
databricks (a GitHub organization, an official publisher) maintains it in databricks/databricks-agent-skills, which has 345 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 8, 2026.
Source: databricks/databricks-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.