What is databricks-ads-session?

A guided design conversation for planning systems on Azure Databricks, a Microsoft platform for data processing, analytics, and machine learning. It gathers requirements and produces a Databricks-focused architecture diagram.

Use it to plan migrations, data warehouses, IoT systems, streaming pipelines, machine-learning projects, or generative-AI applications on Azure Databricks.

It helps turn an unclear data or AI project into a documented solution covering areas such as data engineering, analytics, machine learning, streaming, and governance.

How HaoZhang615/ads-copilot configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to HaoZhang615/ads-copilot. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/HaoZhang615/ads-copilot/main/.github/skills/databricks-ads-session/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/HaoZhang615/ads-copilot

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/haozhang615/ads-copilot/databricks-ads-session.svg)](https://agentmods.dev/skills/haozhang615/ads-copilot/databricks-ads-session)

Your own site

<a href="https://agentmods.dev/skills/haozhang615/ads-copilot/databricks-ads-session"><img src="https://agentmods.dev/badge/skills/haozhang615/ads-copilot/databricks-ads-session.svg" alt="Measured on agentmods" height="20"></a>

Per session 136 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,341 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original 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.

Model	Per session	Once invoked
Fable 5.1	$0.00136	$0.02341
Opus 5	$0.00068	$0.01171
Sonnet 5	$0.00027	$0.00468
Haiku 4.5	$0.00014	$0.00234

Measured 6d ago against content hash ab7f633307d9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

databricks-ads-session 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 6d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (databricks-ads-session/scripts/generate_architecture.py, scripts/generate_architecture.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.github/skills/databricks-ads-session/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Azure Databricks ADS Session

This skill provides domain-specific knowledge for Azure Databricks to be used within an Architecture Design Session. The ADS methodology (persona, pacing, session structure, decision narration, trade-off framework, self-critique) is defined in the runtime system prompt — this skill supplies the Databricks-specific questions, patterns, components, and references that the methodology operates on.

Domain: Azure Databricks

This skill covers the Azure Databricks data platform including:

Data Engineering: LakeFlow Connect, LakeFlow Jobs, LakeFlow Spark Declarative Pipelines (DLT), Auto Loader, Structured Streaming, Apache Flink, Delta Lake
Data Warehousing: SQL Warehouse (Serverless/Pro/Classic), Lakehouse Federation, dbt integration, materialized views
AI/ML: Mosaic AI (Model Serving, Feature Store, AI Gateway), MLflow 3.0, serverless GPU compute, distributed training
GenAI: Mosaic AI Agent Framework, Agent Bricks, Vector Search, MCP Servers, AI Gateway with guardrails
Governance: Unity Catalog, Delta Sharing, ABAC, column-level masking, data lineage, Compatibility Mode
Infrastructure: Serverless Workspace, Classic VNet-injected workspace, ADLS Gen2, Azure Key Vault, Microsoft Entra ID

Phase-Specific Databricks Questions

Phase 1: Context Discovery

Ask about:

Business problem or opportunity driving this initiative
Industry and regulatory context
Greenfield project vs. migration from existing system
Key stakeholders and decision-makers
Timeline and budget constraints
Success criteria (what does "done" look like?)
KPIs, latency targets, and cost envelope

Adapt: If user mentions migration, read references/migration-patterns.md. If user names a specific industry, read references/industry-templates.md for starter context.

Phase 2: Current Landscape

Ask about:

Data sources (databases, APIs, files, streams, SaaS platforms)
Current data platform (if migrating: Hadoop, Snowflake, on-prem SQL, etc.)
Data volumes and growth rate
Real-time vs. batch requirements
Data governance and cataloging needs (Unity Catalog considerations)
Sensitive data classification (PII, PHI, financial)
Unstructured data (documents, PDFs, images, audio) for AI processing

Read the full file on GitHub · 175 lines

Files

What ships with it

20 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.

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.

6d ago First seen · 175 lines · 136 tokens per session scan A ab7f633307d9

Subscribe to this mod's changes

databricks-ads-session is a skill published in the GitHub repository HaoZhang615/ads-copilot (2 stars, last pushed 6mo ago), licensed MIT. It adds 136 tokens to every session and 2,341 once invoked, about $0.0007 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 2026-08-31.

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ads-copilot: Skill for Claude Code