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Databricks Unity Catalog Migration Pilot · jeremylongshore | DecimalAI

Databy jeremylongshore

Databricks Unity Catalog Migration Pilot

Pilot a Databricks Hive Metastore (HMS) → Unity Catalog (UC) migration before the deadline — audit every table's migratability, produce a dependency-ordered migration plan, trace UC's two-level permission model, and pick an environment isolation pattern.

Maintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

70%91%of test cases passed +22 pts

Without skill16 of 23

With skill21 of 23

6 fixed15 passed either way1 still failing1 broke

Measured on gemini-3.6-flash8/14/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe want to test our system schema enablement configuration without applying actual changes to our Databricks account. Which parameter should be passed to the system schema script?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install jeremylongshore-databricks-uc-migration-pilot

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md https://app.decimal.ai/s/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("jeremylongshore-databricks-uc-migration-pilot")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add jeremylongshore-databricks-uc-migration-pilot

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md https://app.decimal.ai/s/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/jeremylongshore-databricks-uc-migration-pilot/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/jeremylongshore-databricks-uc-migration-pilot.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/14/2026
Source: jeremylongshore/claude-code-plugins-plus-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Databricks Unity Catalog Migration Pilot

Pilots a Hive Metastore → Unity Catalog migration end to end: it audits which tables can migrate (and why the rest cannot), turns that audit into a dependency-ordered execution plan, traces UC's two-level permission model when access breaks, and picks an environment-isolation pattern for the one-metastore-per-region constraint.

Overview

The September 30, 2026 forcing function. Databricks is deprecating the legacy Hive Metastore. On the cutover date the hive_metastore catalog goes read-only — no new writes, no schema changes — and stays available only for read-through while you migrate. Everything that writes to HMS (jobs, DLT pipelines, dashboards, external BI) must be pointed at a Unity Catalog table before then, or it breaks. This is not optional and it is not fast: the blockers below (legacy storage schemes, DBFS-root managed data, DENY-based ACLs) each require a physical data move or a grant rewrite, so the work is measured in weeks, not an afternoon.

⚠️ Verify the exact date and scope against your workspace's current deprecation notice (Databricks account console → Previews / Announcements). Deprecation dates move; this skill's job is to get you ready well ahead of whatever the live date is — treat 2026-09-30 as the planning deadline, not an excuse to wait for it.

This skill does four things, in order, and each is deterministic where it can be:

Detect — scripts/audit-hms-readiness.py classifies every HMS table by its storage URI into READY (cloud-native, UC-governable), BLOCKED (a named un-migratable condition), or ORPHAN (a dangling HMS row pointing at deleted storage). The LLM never eyeballs a URI; the script owns the verdict.
Plan — the migration-planner subagent turns the readiness CSV into a per-table plan (SYNC / DEEP CLONE / rewrite / skip) ordered by dependencies so a view never migrates before its base tables.
Trace — the uc-permission-tracer subagent walks UC's two-level access model to answer "why can't user X read this?" in one pass — replacing ~90 minutes of doc-spelunking per ticket.
Isolate — /uc-env-pattern-picker recommends one of four dev/test/prod isolation patterns under the single-metastore-per-region limit.

It is architecturally distinct from the v1 databricks-upgrade-migration and databricks-migration-deep-dive skills: those narrate migration in prose. This one runs a real readiness audit, emits a dependency-ordered plan, and traces live permissions. Deep, load-on-demand knowledge lives in references/; the arithmetic-and-classification lives in scripts/.

Prerequisites

The three role grants below are the hard dependencies and the most common reason the pilot stalls mid-flow. The skill checks the role chain first (Step 1) and reports exactly what is missing before doing any work.

Account-admin — required to enable the UC system schemas (system.information_schema, system.access) the audit reads, and to create the metastore/assign it to workspaces. Enable at the account level, not per workspace.
Metastore-admin — required to CREATE CATALOG, run GRANT on UC objects, and create external locations + storage credentials. Migration is a sequence of metastore-admin operations.
Cloud IAM read access (AWS s3/IAM, Azure, or GCP) — required to diagnose a storage-credential failure (the D2 case: a table is cloud-native but the UC storage credential's IAM role cannot assume the bucket). Bash(aws:*) is allowed for read-only IAM inspection; the skill never mutates cloud IAM.
Databricks CLI authenticated (databricks auth login, or the DATABRICKS_HOST + DATABRICKS_TOKEN env pair) and jq for parsing. Enumeration and grant SQL run through the CLI Statement Execution API.
DATABRICKS_WAREHOUSE_ID set to a running SQL warehouse — every statement-execution call requires it.

Authentication. All auth comes from the environment: the CLI's DATABRICKS_HOST + DATABRICKS_TOKEN (or databricks auth login), and read-only cloud credentials from the standard provider chain (aws sts get-caller-identity to confirm). No secrets are hardcoded.

Instructions

The pipeline is verify role chain → detect readiness → plan → migrate → trace access → isolate envs. Do them in order; Step 1 is a hard gate.

Step 1: Verify the Role Chain (hard gate — fail fast, not mid-migration)

Before touching data, confirm the running principal holds the roles the migration needs. A missing role surfaces here, not three tables into a SYNC.

# Am I authenticated, and as whom?
databricks current-user me | jq -r '.userName'

# Metastore-admin check: only a metastore admin can read every grant. If this
# errors with PERMISSION_DENIED, the principal is not metastore-admin.
databricks api post /api/2.0/sql/statements --json "$(jq -n --arg wh "$DATABRICKS_WAREHOUSE_ID" \
  '{warehouse_id:$wh, wait_timeout:"30s",
    statement:"SELECT 1 FROM system.information_schema.catalog_privileges LIMIT 1"}')" \
  | jq -r '.status.state, (.status.error.message // "ok")'

Report the role chain status plainly: account-admin (needed to enable system schemas), metastore-admin (needed for grants + external locations), cloud IAM read (aws sts get-caller-identity). If any is missing, name who must grant it and STOP — do not start an audit you cannot finish.

Step 2: Enable & Confirm the System Schemas

The readiness audit reads system.information_schema; permission tracing reads system.access. Those schemas are individually gated — enabling one does not enable the others — behind an account-level flag AND a metastore-admin grant chain. If they are not enabled, run the bundled idempotent enabler (account-admin auth; a workspace PAT is rejected up front) to enable each schema and grant a group USE CATALOG system + USE SCHEMA + SELECT in one pass:

python3 "${CLAUDE_SKILL_DIR}/scripts/enable-system-schemas.py" \
  --account-id "$DATABRICKS_ACCOUNT_ID" --metastore-id "$METASTORE_ID" \
  --grant-to data-governance --dry-run   # drop --dry-run to apply

Then confirm a UC metastore is attached to this workspace (databricks metastores current) — without one there is nowhere to migrate to. The full two-layer access model (account-admin enables, metastore-admin grants, neither inherits SELECT) is in ${CLAUDE_SKILL_DIR}/references/system-tables-access-model.md.

Step 3: Detect — Run the Readiness Audit

Enumerate the HMS schema(s) and classify every table. The script owns the verdict; feed it either live (--live SCHEMA) or the enumerated rows as JSON.

OUT="${OUT:-$(pwd)/uc-migration-out}" && mkdir -p "$OUT"

# Live enumeration (needs DATABRICKS_WAREHOUSE_ID + an authenticated CLI):
python3 "${CLAUDE_SKILL_DIR}/scripts/audit-hms-readiness.py" \
  --live sales --summary --out "$OUT/readiness-sales.csv"

Every row lands in one of three buckets — READY (cloud-native path, SYNC-able), BLOCKED (a named condition), ORPHAN (dangling HMS entry, cleanup not migration). When a BLOCKED class appears, load ${CLAUDE_SKILL_DIR}/references/uc-migration-blockers.md for that class's canonical error, the physical relocation procedure, and the per-cloud variant. Do not skip the CLONE-drops-history gotcha in that file — a shallow CREATE TABLE ... CLONE breaks time travel post-migration; use DEEP CLONE.

Step 4: Plan — Dependency-Ordered Migration Plan

Hand the readiness CSV to the migration-planner subagent (see agents/migration-planner.md) with the org's catalog naming convention. It emits a numbered execution order with per-step rationale and the right verb per table (SYNC for ready Delta externals, DEEP CLONE/rewrite for blocked, CREATE VIEW after base tables, skip for orphans). Dependency ordering is the point: a view never migrates before the tables it reads.

Invoke it via /audit-hms-readiness (which runs Step 3 then routes here) or directly by handing the subagent the CSV path.

Step 5: Migrate — Execute the Plan

Walk the plan top to bottom:

READY (Delta external, cloud path): SYNC TABLE <uc_cat>.<schema>.<table> FROM hive_metastore.<schema>.<table> — metadata-only, no data copy. SYNC SCHEMA does a whole schema when every table is ready.
BLOCKED (legacy scheme / DBFS root): relocate the data first (DEEP CLONE for Delta, CREATE TABLE AS SELECT for non-Delta) to a UC-governed cloud path, register an external location + storage credential, then create the UC table at the new path. Full procedure per class in uc-migration-blockers.md.
LEGACY_TABLE_ACL: the data may be ready, but the DENY-based grants cannot auto-map to UC's allow-only model — re-author them (Step 6 traces them).

Re-run Step 3 after a batch to confirm the migrated tables drop out of BLOCKED.

Step 6: Trace — Diagnose Access After Migration

When a user hits PERMISSION_DENIED or AccessDenied on s3://… post-migration, route to the uc-permission-tracer subagent (/trace-uc-permission <user> <error>). It walks UC's two levels — account-admin/metastore-admin status → group membership (including nested-group caveats) → catalog/schema/table grants → the external-location/storage-credential grant for a cloud AccessDenied — and returns a single actionable line: "user X needs group Y membership AND GRANT SELECT ON <obj> run by metastore admin W."

When the fix is "add the user to group Y" but the grant still does not apply, the cause is usually the Entra→Databricks SCIM bridge silently dropping nested-group membership — see ${CLAUDE_SKILL_DIR}/references/scim-bridge-patterns.md for the connector's direct-members-only limitation and the three workarounds.

Step 7: Isolate — Pick an Environment Pattern

For a fresh UC layout, run /uc-env-pattern-picker. It asks compliance, cost, and BI-tool questions and recommends one of four patterns for the one-UC-metastore-per-region constraint, emitting a matching Databricks Asset Bundle target stub. The four patterns, their cost models, and tradeoffs live in ${CLAUDE_SKILL_DIR}/references/uc-environment-isolation-patterns.md.

Output

A readiness CSV ($OUT/readiness-<schema>.csv, in the working dir) — one row per HMS table: table_name, storage_uri, scheme, migration_blocker, suggested_action, each table bucketed READY / BLOCKED / ORPHAN with a READY/BLOCKED/ORPHAN tally.
A dependency-ordered migration plan (markdown from migration-planner) — a numbered execution order, the verb per table (SYNC/DEEP CLONE/rewrite/skip), per-step rationale, and views ordered after their base tables.
A permission trace (from uc-permission-tracer) — the exact missing group membership + grant + who must run it, for a specific user + error.
An environment-isolation recommendation — the chosen pattern, its cost model, and a ready-to-paste DAB target stub.
A role-chain status up front — account-admin / metastore-admin / cloud-IAM, with the exact grant and grantor for anything missing.

Error Handling

Error	Cause	Solution
`PERMISSION_DENIED` on `system.information_schema`	Principal is not metastore-admin, or system schemas not enabled	Step 1/2: report the missing role; an account admin enables system schemas, a metastore admin grants access. Stop until resolved.
No metastore attached to the workspace	UC not set up for this workspace	`databricks metastores current`; if empty, an account admin must create/assign a metastore before any migration.
`SYNC` fails with an unsupported-location error	Table is on a legacy scheme (`wasbs://`/`adl://`/`dbfs:/`)	It is BLOCKED, not READY — relocate per `uc-migration-blockers.md` (DEEP CLONE to a cloud path), then create the UC table.
Time travel broken after migration	Shallow `CREATE TABLE ... CLONE` was used	Use `DEEP CLONE` — shallow clone copies metadata only and drops Delta history. Re-clone with `DEEP CLONE`. See the gotcha in `uc-migration-blockers.md`.
`AccessDenied on s3://…` after a READY table migrates	UC storage credential's IAM role cannot assume the bucket	Trace with `/trace-uc-permission`; verify the external location's storage credential role and the bucket trust policy with read-only `aws` calls (D2).
DESCRIBE DETAIL returns no location	Orphaned HMS entry (deleted storage) or a view	The audit marks it ORPHAN — clean up or skip; never report it as a migration blocker.
`DATABRICKS_WAREHOUSE_ID` unset	No SQL warehouse for statement execution	Set it to a running warehouse before Step 1.

Examples

Example 1: "How do I migrate off the Hive metastore?"

Runs the full pipeline. Step 1 confirms metastore-admin, Step 3 audits the schema (12 READY, 5 BLOCKED, 2 ORPHAN), the planner emits a numbered order (SYNC the 12 ready Delta tables first, DEEP CLONE the 3 dbfs:/user/hive managed tables to s3://, re-CREATE the 2 Parquet externals, skip the 2 orphans, then the 4 views), and the skill walks the plan — surfacing the Sept-30-2026 deadline and the CLONE-history gotcha inline.

Example 2: "Which of my tables can't migrate, and why?"

Step 3 only. The audit CSV lists each BLOCKED table with its scheme and the one-line reason; the skill loads uc-migration-blockers.md for each class and gives the physical relocation procedure per cloud.

Example 3: "User can't read a table after we migrated it."

Routes straight to /trace-uc-permission [email protected] "PERMISSION_DENIED: SELECT on main.sales.orders". The tracer returns: "[email protected] is in no group with a grant; add her to data-analysts AND run GRANT SELECT ON TABLE main.sales.orders TO data-analysts as metastore admin — she is not account-admin, so the grant will not inherit."

Example 4: "How do I keep dev/test/prod separate under Unity Catalog?"

/uc-env-pattern-picker asks the compliance/cost/BI questions and, for a cost-sensitive team that accepts shared lineage, recommends single-metastore-catalog-per-env (bronze_dev/bronze_prod) with a DAB var.env-parameterized target stub — noting the lineage-cleanliness tradeoff and the multi-account alternative for hard isolation.

Resources

${CLAUDE_SKILL_DIR}/references/uc-migration-blockers.md — the taxonomy of un-migratable HMS conditions: canonical error, physical relocation procedure, per-cloud variant, and the CLONE-drops-history gotcha.
${CLAUDE_SKILL_DIR}/references/uc-environment-isolation-patterns.md — the four dev/test/prod isolation patterns under one-metastore-per-region, with cost models and a DAB target stub.
${CLAUDE_SKILL_DIR}/references/system-tables-access-model.md — the two-layer access model (account-admin enables, metastore-admin grants), per-schema enablement, and the manual permission traversal.
${CLAUDE_SKILL_DIR}/references/scim-bridge-patterns.md — the Entra→Databricks SCIM nested-group limitation and the three workarounds.
${CLAUDE_SKILL_DIR}/scripts/audit-hms-readiness.py — deterministic HMS-table readiness classifier (READY/BLOCKED/ORPHAN → CSV).
${CLAUDE_SKILL_DIR}/scripts/enable-system-schemas.py — idempotent system-schema enabler + group grant chain (account-admin auth precheck).
${CLAUDE_SKILL_DIR}/agents/migration-planner.md — turns the readiness CSV into a dependency-ordered plan.
${CLAUDE_SKILL_DIR}/agents/uc-permission-tracer.md — traces UC's two-level access model for a user + error.
Databricks: Upgrade to Unity Catalog · SYNC command · UCX (Databricks Labs)

When it activates

Automatically, when a task matches. Use when a user asks how to migrate off the Hive metastore, move to Unity Catalog, which tables are UC-ready, why a table won't migrate, why a user lacks access after migration, or how to isolate dev/test/prod under UC. Trigger with "migrate to

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jeremylongshore / claude-code-plugins-plus-skills

skills/.curated/databricks-uc-migration-pilot/SKILL.md

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License MITImported 10/23/2026Version v1Bundled 6 files · scripts

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Databricks Unity Catalog Migration Pilot

Overview

Prerequisites

Instructions

Step 1: Verify the Role Chain (hard gate — fail fast, not mid-migration)

Step 2: Enable & Confirm the System Schemas

Step 3: Detect — Run the Readiness Audit

Step 4: Plan — Dependency-Ordered Migration Plan

Step 5: Migrate — Execute the Plan

Step 6: Trace — Diagnose Access After Migration

Step 7: Isolate — Pick an Environment Pattern

Output

Error Handling

Examples

Example 1: "How do I migrate off the Hive metastore?"

Example 2: "Which of my tables can't migrate, and why?"

Example 3: "User can't read a table after we migrated it."

Example 4: "How do I keep dev/test/prod separate under Unity Catalog?"

Resources

When it activates

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