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Querying Fabric for Claude Dashboards · data-goblin | DecimalAI

Databy data-goblin

Querying Fabric for Claude Dashboards

Query Microsoft Fabric and Power BI semantic models with DAX (and Fabric SQL endpoints with T-SQL) to feed a Claude Dashboard or any dashboard built from a Fabric connector or MCP server.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

64%100%of test cases passed +36 pts

Without skill14 of 22

With skill22 of 22

8 fixed14 passed either way0 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOur query developer wrote: `DEFINE VAR _A = 10, VAR _B = 20 EVALUATE ROW("Test", _A + _B)`. The query fails to execute in Fabric. What is the syntactic rule for variable definitions in DAX queries?

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 data-goblin-querying-fabric
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("data-goblin-querying-fabric")
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 data-goblin-querying-fabric

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/data-goblin-querying-fabric/SKILL.md https://app.decimal.ai/s/data-goblin-querying-fabric/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/data-goblin-querying-fabric/SKILL.md

This gets SKILL.md only. The skill's 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/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/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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("data-goblin-querying-fabric")
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/data-goblin-querying-fabric/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/data-goblin-querying-fabric/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 7 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 data-goblin-querying-fabric

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/data-goblin-querying-fabric/SKILL.md https://app.decimal.ai/s/data-goblin-querying-fabric/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/data-goblin-querying-fabric/SKILL.md

This gets SKILL.md only. The skill's 7 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/data-goblin-querying-fabric/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/data-goblin-querying-fabric.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 11/2/2026
Source: data-goblin/power-bi-agentic-development ↗
License: GPL-3.0

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

Querying Fabric for Claude Dashboards

A dashboard is only as trustworthy as its queries. The semantic model already holds the business's agreed definitions of its metrics: the measures. A dashboard that reuses them shows the same numbers as every Power BI report on that model; a dashboard that recomputes them shows numbers that look right and quietly disagree. So the work here is mostly finding the right measures and slicing them well, not writing DAX formulas.

Three rules shape every dashboard this skill builds:

reuse measures:  every number comes from an existing model measure; no new measure logic in the queries
kpi contract:    every KPI shows its value, a target, and a trend
crossfiltering:  selecting a mark in one visual filters every other visual

Workflow

Find the model. list_models or find_items (type SemanticModel); resolve_item for a URL. Confirm the model with the user when several look alike (dev, test and prod copies are common).
Read the schema before writing any query. get_model_schema with part Measures, then Columns and Relationships for the tables you will slice by. Read measure descriptions, display folders, format strings and expressions. See Reusing the model's measures. Look for data-quality columns too (estimated, imputed, provisional, source, is-complete flags): decide which rows count and apply that filter to every query, or the dashboard averages fabricated rows.
Answer one business question. Pick the measures that answer it, the target each KPI is judged against, the date grain of its trend, and the dimensions the user will click to crossfilter.
Write one query per visual grain. One EVALUATE per visual; see references/dax-query-patterns.md.
Lint, then test every query. Save each query to a .dax file and run python3 <this skill's directory>/scripts/lint_dax.py queries/*.dax. Rewrite every query it flags until it prints ok, then run each with run_dax_query before the dashboard uses it. See Testing.
Wire crossfiltering through query parameters; see references/crossfiltering.md.
Check the numbers against a known total. Compare one KPI against the same measure with no filters, or against a figure the user already trusts from a report. A dashboard that is wrong by a filter is worse than no dashboard.

Reusing the model's measures

Measures are the governed definitions. Use them by name, [Measure], and let the dashboard decide only how the measure is sliced: which columns group it, which filters apply, which period it covers.

do:
  - reference model measures directly: "Turnover", [Turnover]
  - prefer an existing variant over deriving one: use [Turnover 1YP] or [Budget YTD] when the model has them
  - shift or filter an existing measure when no variant exists: CALCULATE([Turnover], DATEADD('Date'[Date], -1, YEAR))
  - keep the model's format strings (FormatString or FormatStringDefinition in the schema) for display
avoid:
  - re-aggregating raw columns when a measure exists: SUM('Sales'[Amount]) next to an existing [Total Sales]
  - DEFINE MEASURE blocks that re-implement business logic (margins, rates, OTD %, conversions)
  - ratios computed in the dashboard from two measures when the model has the ratio measure
  - averaging or summing a ratio measure client-side; ask the model for the ratio at the grain you need

Why this matters: a model's [Turnover] may add surcharges, subtract fees and convert currency; a SUM('Invoices'[Net Value]) returns a plausible figure that matches no report. Users notice when two tools disagree, and they stop trusting both.

When the question needs a metric the model does not have:

Say so plainly, and name the closest existing measures.
Suggest adding the measure to the model, where the model owner can review it and every report can share it.
Only if the user wants it in the dashboard anyway, compute it from existing measures (never from raw columns when a measure covers them), and label it in the dashboard as a dashboard-only calculation.

Measures that need a selection

Many enterprise models have measures that return BLANK unless a slicer table has one value selected: a reporting currency, a value unit, a selected metric or period. Their expressions start with HASONEVALUE, SELECTEDVALUE or IF.EAGER(HASONEVALUE(...)), and good models say so in the description. A dashboard that ignores this shows empty cards. Worse, a measure that reads MAX ( 'Exchange Rate'[Rate] ) or sums across a currency table returns a number with no selection, just the wrong one: several times too high, with nothing to say so.

Read the expression and description of every measure you use, and look for disconnected tables (tables with no relationships, often named like Selected Unit, Exchange Rate, Selected Period)
Pass the selection as a filter, e.g. TREATAS({"EUR"}, 'Exchange Rate'[Currency]), and show that choice in the dashboard (a currency switch, or a label stating the currency)
Ask the user which default to use when the model does not document one

When expressions are hidden

get_model_schema returns empty expressions when the user may not read them. Rely on names, descriptions, display folders and format strings, and test the measure at a known grain before relying on it.

The KPI contract: value, target, trend

A number on its own does not tell anyone whether to act. Every KPI in the dashboard carries three things:

value:   the measure for the selected period and filters
target:  what the value is judged against, from the model, with the variance to it
trend:   the same measure over time, so the reader sees direction, not just position

Read references/kpi-design.md for finding targets, choosing the trend grain and the query shapes. The short version:

Target from the model first: budget, plan, goal, target, forecast or SLA measures, or a "vs target" measure already in the model. Use the model's variance measures ([Budget vs. Turnover (%)]) when they exist
No target in the model: ask the user for one and show it as a stated target ("Target 95%, set by the user"), or fall back to a prior-period comparison and label it as such. Never invent a target
Trend: the KPI measure by month (or the grain the business reviews) over enough history to see a pattern, typically 12 to 24 periods, with the same period last year where it helps

Crossfiltering

Every visual is also a filter. Clicking a bar, a row or a point sets a selection, and every other visual re-queries with that selection applied; clicking again clears it. This is what makes a dashboard an analysis tool rather than a poster.

Keep one selection state for the whole dashboard, keyed by model column ('Customers'[Key Account])
Pass the selection into each query as parameters (@account) and apply it with a filter that is a no-op when nothing is selected
Re-query rather than filter client-side whenever the visual shows a ratio, a distinct count or any non-additive measure; only the model can compute those correctly for a subset
The visual that owns a selection keeps showing all its members and highlights the selected one, so the user can see what they clicked and change it
Show active selections in one place with a way to clear them

Read references/crossfiltering.md for the tested DAX patterns.

Writing DAX queries

Query rules that prevent most errors:

references:  columns fully qualified with quoted table names, 'Table'[Column]; measures unqualified, [Measure]
evaluate:    EVALUATE takes a table expression; wrap scalars in ROW(...)
define:      one DEFINE block; VAR and MEASURE separated by new lines, no commas
grouping:    SUMMARIZECOLUMNS(group-by columns, then filter tables, then "Name", [Measure] pairs)
filters:     filter arguments to SUMMARIZECOLUMNS are tables: TREATAS({...}, 'T'[C]) or FILTER(ALL('T'[C]), ...)
ordering:    ORDER BY for any multi-row result, so results are stable between runs
no sql:      no SELECT, WHERE, HAVING or method chaining inside DAX
blanks:      let measures return BLANK; adding + 0 or DIVIDE(x, y, 0) makes SUMMARIZECOLUMNS return every combination
dates:       derive "current" periods from the data (MAX of the fact date) or the model's date flags, not hard-coded years
formatting:  return raw numbers and format in the dashboard; FORMAT() turns numbers into text that cannot be charted or sorted

A typical dashboard query:

DEFINE
    VAR _Region = TREATAS ( { "EMEA" }, 'Geography'[Region] )
    VAR _Years = TREATAS ( { 2024, 2025 }, 'Date'[Year] )

EVALUATE
    SUMMARIZECOLUMNS (
        'Date'[Year],
        'Date'[Month Number],
        _Region,
        _Years,
        "Total Sales", [Total Sales],
        "Total Sales PY", [Total Sales PY],
        "Sales Target", [Sales Target]
    )
ORDER BY 'Date'[Year], 'Date'[Month Number]

Every column in it is a model measure; the query only decides the grain and the filters.

Query rules that keep queries small and fast. scripts/lint_dax.py checks them: a query that breaks one is rewritten before the dashboard uses it.

one context:  all measures for one visual are evaluated in ONE filter context: SUMMARIZECOLUMNS, or
              CALCULATETABLE ( ROW ( ... ), filters ); never CALCULATE ( [m], filters ) once per column, and
              never ADDCOLUMNS ( table, "a", CALCULATE ( ... ), "b", CALCULATE ( ... ) ) with the filter repeated
windows:      several periods (7/30/90 days, YTD vs PY) are rows: GENERATE over a DATATABLE of windows, below
no FILTER:    never FILTER ( 'Table', ... ) over a whole table, in a VAR or anywhere else; filter columns with
              TREATAS or a column predicate ('T'[Flag] = FALSE, 'T'[Date] >= _From)
no iterating: no AVERAGEX / MEDIANX / COUNTROWS ( FILTER ( ... ) ) / GROUPBY ( CURRENTGROUP ) over fact rows;
              buckets (price bands, order-size bands) are a column or a DATATABLE of bands with the measures
              evaluated per band, like windows
no dates:     no DATE ( 2025, ... ) literals; every boundary derives from _Last = MAX of the fact date

DEFINE
    VAR _Valid = TREATAS ( { FALSE }, 'Fact'[Is Provisional] )
    VAR _Last = CALCULATE ( MAX ( 'Fact'[Date] ), _Valid )
    VAR _Windows = DATATABLE ( "Window", STRING, "Days", INTEGER, { { "7d", 7 }, { "30d", 30 }, { "90d", 90 } } )

EVALUATE
    GENERATE (
        _Windows,
        CALCULATETABLE (
            ROW ( "Average", [Average Value], "Target Rate", [Target Rate], "Days", [Days] ),
            DATESBETWEEN ( 'Date'[Date], _Last - [Days] + 1, _Last ),
            _Valid
        )
    )
ORDER BY [Days]

On the test model this shape returned the same numbers as the copied-block version in 62 ms instead of 109, with 35 storage-engine queries instead of 87.

Read references/query-performance.md for the measured before-and-after of each rule and how to benchmark a query with te query --runs.

References, read when you need them:

references/query-performance.md: fewer storage-engine scans, the windows pattern, benchmarking
references/dax-query-patterns.md: query structure, worked patterns for dashboard visuals (KPI row, trend, ranked list, TopN with "other", small multiples), anti-patterns
references/dax-core-reference.md: CALCULATE filter arguments, SUMMARIZECOLUMNS, TREATAS, ALL and ALLEXCEPT, BLANK semantics, common syntax mistakes
references/dax-time-intelligence.md: trends, prior year, YTD, rolling windows, and establishing date context inside a query
references/kpi-design.md: targets, variance and trend queries per KPI
references/crossfiltering.md: selection state, parameterised filters, multi-select, and which visuals re-query

Lakehouses and warehouses

When the data lives only in a warehouse, lakehouse SQL endpoint or SQL database, use get_sql_schema and run_sql_query (one read-only SELECT, parameters as @name). There are no measures there, so the measure-reuse rule becomes: reuse the views and columns the data owners publish (a gold or reporting schema) before writing aggregations, and ask whether a semantic model already covers the question. A semantic model, when one exists, is the better source for a dashboard because its definitions are shared.

Testing and iteration

Write the query
Run it with run_dax_query (model, query, parameters, maxRows)
Inspect column names, types, row count and the values themselves; a row count of 0 or a column of nulls usually means a missing selection (see Measures that need a selection) or a filter on the wrong column
On an error, check the syntax rules above and references/dax-core-reference.md
Re-run with each crossfilter parameter set and cleared, so both states are known to work

Results are capped by maxRows (1000 by default), and some connectors also cut a response by its size, so a wide result can stop short of maxRows; both are flagged with truncated: true. A dashboard visual rarely needs that many rows: aggregate in DAX to the visual's grain, or use TOPN, rather than raising the cap. Queries run as the signed-in user, so row-level security applies; two users can see different numbers, which is correct.

Troubleshooting

model not found:            list_models with part of the name; use workspace/model to disambiguate
blank KPI or empty result:  measure needs a slicer selection, or a filter hits a column with no matching values
numbers differ from report: query re-aggregates raw columns, or misses a filter the report applies (page, report
                            or slicer default); compare against the report's filters
every combination returned: a measure in the query never returns BLANK (+ 0, COALESCE, DIVIDE with 0)
time intelligence wrong:    no date context in the query, or the model's date table is not marked; see
                            references/dax-time-intelligence.md
syntax error near VAR:      commas between VARs, or a second DEFINE

When it activates

Automatically, when a task matches. Use this skill whenever you build, extend or debug a dashboard, KPI card, chart or table on top of a semantic model, call a tool like run_dax_query, get_model_schema or list_models, or the user asks for numbers "from Fabric", "from the model" or "from Power BI", even if they never say DAX. Covers reusing the mo

About this skill

Automatic

Data Analyst pack

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

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LLM judge · does the skill do what it claims

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LLM judge · policy & harmful-content review

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Imported

data-goblin / power-bi-agentic-development

plugins/dashboards/skills/querying-fabric/SKILL.md

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License GPL-3.0Imported 11/1/2026Version v2Bundled 7 files · scripts

Illustrative Want to see what data-goblin/querying-fabric would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Querying Fabric for Claude Dashboards

Workflow

Reusing the model's measures

Measures that need a selection

When expressions are hidden

The KPI contract: value, target, trend

Crossfiltering

Writing DAX queries

Lakehouses and warehouses

Testing and iteration

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