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.
curl -O https://raw.githubusercontent.com/HaoZhang615/ads-copilot/main/.github/skills/databricks-ads-session/SKILL.mdgit clone --depth 1 https://github.com/HaoZhang615/ads-copilotWrote 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/haozhang615/ads-copilot/databricks-ads-session)<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/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/haozhang615/ads-copilot/databricks-ads-session"><img src="https://agentmods.dev/badge/skills/haozhang615/ads-copilot/databricks-ads-session.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00054 | $0.00936 |
| Sonnet 5.5 | $0.00027 | $0.00468 |
| Haiku 4.5 | $0.00014 | $0.00234 |
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 2mo 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 — 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
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.
- databricks-ads-session/references/conversation-framework.md 23 KB
- databricks-ads-session/references/databricks-patterns.md 22 KB
- databricks-ads-session/references/industry-templates.md 9.2 KB
- databricks-ads-session/references/migration-patterns.md 14 KB
- databricks-ads-session/references/probing-questions.md 17 KB
- databricks-ads-session/references/readiness-checklist.md 8.4 KB
- databricks-ads-session/references/technical-deep-dives.md 20 KB
- databricks-ads-session/references/trade-offs-and-failure-modes.md 25 KB
- databricks-ads-session/scripts/generate_architecture.py 22 KB runs code
- databricks-ads-session/skill.json 430 B
- databricks-ads-session/SKILL.md 10 KB
- references/conversation-framework.md 23 KB
- references/databricks-patterns.md 22 KB
- references/industry-templates.md 9.2 KB
- references/migration-patterns.md 14 KB
- references/probing-questions.md 17 KB
- references/readiness-checklist.md 8.4 KB
- references/technical-deep-dives.md 20 KB
- references/trade-offs-and-failure-modes.md 25 KB
- scripts/generate_architecture.py 22 KB runs code
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.
- 2mo ago First seen · 175 lines · 136 tokens per session scan A ab7f633307d9
databricks-ads-session is a skill published in the GitHub repository HaoZhang615/ads-copilot (2 stars, last pushed 8mo ago), licensed MIT. It adds 136 tokens to every session and 2,341 once invoked, about $0.0005 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 2026-08-31.
databricks-ads-session compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| databricks-ads-sessionHaoZhang615/ads-copilot | Skill | 2 | 8mo ago | MIT | A | 136 | Claude Code, Codex |
| apex-azure-kustojonathan-vella/apex-accelerator | Skill | 50 | 2d ago | MIT | A | 100 | Claude Code |
| databricks-platform-provisioningdatabricks-solutions/ai-platform-kit | Skill | 22 | 24d ago | none | A | 69 | Claude Code |
| azure-search-documents-dotnetmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 102 | Claude Code |
| azure-ai-ml-pymicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 59 | Claude Code |
| azure-ai-vision-imageanalysis-pymicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 66 | Claude Code |
| azure-ai-contentsafety-tsmicrosoft/skills | Skill | 3.2k | yesterday | MIT | A | 56 | Claude Code |