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Bootstrap a new dbt data product · hashgraph-online | DecimalAI

Databy hashgraph-online

Bootstrap a new dbt data product

Bootstrap a brand-new dbt data product from scratch — create dbt_project.yml, the Entropy Data model layout (input_ports, staging, intermediate, output_ports/v1), README with uv install instructions, .gitignore, and a profiles.yml.example for the chosen warehouse. After scaffolding, hands off to the entropy-data-sync skill to add the publishing layer (ODPS, ODCS, OpenLineage, GitHub Actions).

Maintained

SkillScore

SkillScore36/100

Test results 36

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%36%of test cases passed +18 pts

Without skill4 of 22

With skill8 of 22

5 fixed3 passed either way13 still failing1 broke

Measured on gemini-3.6-flash9/1/2026v120 comparable

Track record

Agent runs: 13; All time

See one task, with and without the skill

One test taskWe are running pre-checks for bootstrapping a new dbt repository. The CLI tool is installed but entropy-data connection test fails due to missing credentials. Should the bootstrapping tool ask the user directly for their API key secret during script execution?

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 hashgraph-online-dataproduct-bootstrap
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("hashgraph-online-dataproduct-bootstrap")
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 hashgraph-online-dataproduct-bootstrap

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/hashgraph-online-dataproduct-bootstrap/SKILL.md https://app.decimal.ai/s/hashgraph-online-dataproduct-bootstrap/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/hashgraph-online-dataproduct-bootstrap/SKILL.md

This gets SKILL.md only. The skill's 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/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/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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("hashgraph-online-dataproduct-bootstrap")
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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/hashgraph-online-dataproduct-bootstrap/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 5 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 hashgraph-online-dataproduct-bootstrap

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/hashgraph-online-dataproduct-bootstrap/SKILL.md https://app.decimal.ai/s/hashgraph-online-dataproduct-bootstrap/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/hashgraph-online-dataproduct-bootstrap/SKILL.md

This gets SKILL.md only. The skill's 5 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/hashgraph-online-dataproduct-bootstrap/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/hashgraph-online-dataproduct-bootstrap.json

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/1/2026
Source: hashgraph-online/awesome-codex-plugins ↗
License: Apache-2.0

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

Bootstrap a new dbt data product

Create a new dbt data product project that follows the Entropy Data conventions. This skill handles the greenfield case — empty directory, no dbt project yet. For an existing dbt project that just needs the Entropy Data layer, use the entropy-data-sync skill instead.

What this skill produces

After running, the directory contains:

.
├── dbt_project.yml
├── pyproject.toml
├── .gitignore
├── README.md
├── profiles.yml.example
├── models/
│   ├── input_ports/_models.yml
│   ├── staging/_models.yml
│   ├── intermediate/_models.yml
│   └── output_ports/v1/_models.yml
├── analyses/      # empty
├── macros/        # empty
├── seeds/         # empty
├── snapshots/     # empty
└── tests/         # empty

It then invokes entropy-data-sync to add <id>.odps.yaml, the output-port contract under models/output_ports/v1/<contract>.odcs.yaml, openlineage.yml, and .github/workflows/data-product.yml.

How to run this skill

${PLUGIN_ROOT} below refers to the root of this plugin — the directory that contains skills/. On Claude Code it is set automatically as ${CLAUDE_PLUGIN_ROOT} — use that. On any other agent (Codex, Copilot CLI, etc.) it is unset; resolve it as ../.. relative to this SKILL.md file's directory (i.e. the grandparent of skills/<this-skill>/).

Plan announcement (before Step 1)

Before running Step 1, print this plan to the user verbatim:

Running dataproduct-bootstrap. I'll:

Pre-checks: confirm the working directory is empty (greenfield only), then ask whether this is a brand-new data product or one that already has an ODPS draft in Entropy Data.

Gather parameters. If you point me at an existing draft, I pull them from the fetched ODPS; otherwise I'll ask you in one batched question (data product id, team, platform, catalog/schema, table).

Pick the dbt adapter and profile block for the chosen platform.

Scaffold the dbt project (dbt_project.yml, profiles.yml.example, model layout, README, .gitignore), and check whether the user's existing ~/.dbt/profiles.yml would collide with the new profile.

Hand off to entropy-data-sync for the publishing layer (ODPS, ODCS, OpenLineage, GitHub Actions).

Summarize what was scaffolded and the next manual steps.

Then proceed.

Step 1 — Pre-checks

Confirm the working directory is empty, or that it contains only files the user is fine with (e.g. an empty git repo, a LICENSE, or a README.md that will be overwritten).
If dbt_project.yml already exists, stop and tell the user to use the entropy-data-sync skill instead. This skill is for greenfield only.
Check whether the data product already exists in Entropy Data. Ask the user: "Is there already an ODPS draft for this data product in Entropy Data, or is it entirely new? If existing, paste the data product id or URL." Three outcomes:
- New — continue to Step 2 with no DATA_PRODUCT preloaded.
- Existing, id/URL given — extract the trailing id from a URL, then run entropy-data dataproducts get <id> -o yaml. If the lookup succeeds, remember the response as DATA_PRODUCT and use it in Step 2. If it returns a not-found error, tell the user and ask whether to (a) try a different id, (b) proceed as new with that id, or (c) abort.
- Unsure — if the user does not know, default to asking for the id anyway and run the lookup. Do not silently assume "new".
CLI prerequisites for the lookup (bootstrap is the one skill where this is global, not via the project venv — see AGENTS.md § Install pattern. The directory has no pyproject.toml yet, so uv run entropy-data is unavailable until Step 4 scaffolds the project and the user runs uv sync. Subsequent skills use uv run entropy-data exclusively.): entropy-data --version must be on PATH (install once with uv tool install entropy-data if missing) and entropy-data connection test must succeed. If either fails, surface the error and ask the user whether to (a) fix the CLI and retry, or (b) skip the lookup and proceed as if new. Don't prompt for the API key yourself; tell the user to run entropy-data connection add <name> --host <host> --api-key <key>.

Step 2 — Gather parameters

Set DBT_PROJECT_NAME = DATA_PRODUCT_ID once DATA_PRODUCT_ID is known.

Step 2a — If `DATA_PRODUCT` was loaded from Entropy Data

Derive parameters from the fetched ODPS. Treat the draft as authoritative; only ask the user for fields it does not specify.

Parameter	Source from `DATA_PRODUCT`
`DATA_PRODUCT_ID`	`id`
`DATA_PRODUCT_NAME`	`name`
`PURPOSE`	`description.purpose` (fall back to ask)
`TEAM_NAME`	`team.name` or `team.id` (fall back to ask, see picking note below)
`PLATFORM`	output port's `server.type` (fall back to ask)
`CATALOG`	output port's `server.catalog` / `server.database` / `server.project` (fall back to ask)
`SCHEMA`	output port's `server.schema` / `server.dataset` (fall back to ask)
`TABLE`	output port's `server.table`, or the linked contract's `models:` key (fall back to ask)

If the draft declares more than one output port, ask the user which one to use for PLATFORM/CATALOG/SCHEMA/TABLE. Default to the first.

Show the user the derived parameters and ask for confirmation before continuing. Collect any missing fields in one batched question.

Step 2b — If no draft was loaded (new product)

Ask the user for these in a single prompt. Do not generate any files until you have all of them.

Parameter	Description	Example
`DATA_PRODUCT_ID`	Stable id, snake_case, also the dbt project name	`dp_acme_customer_activity`
`DATA_PRODUCT_NAME`	Human-friendly name	`Customer Activity`
`PURPOSE`	One sentence — why this data product exists	`Customer activity for customer success.`
`TEAM_NAME`	Owning team	`customer-success` (see note below)
`PLATFORM`	`databricks`, `snowflake`, `bigquery`, or `postgres`	`databricks`
`CATALOG` (or equivalent)	Databricks catalog / Snowflake database / BigQuery project / Postgres database	`entropy_data_prod`
`SCHEMA`	Schema / dataset	`dp_acme_customer_activity`
`TABLE`	First output port table name	`customer_activity`

Picking TEAM_NAME: prefer a team id that already exists in Entropy Data so the data product slots into the team-scoped views in the UI. If the user does not already know the team id, invoke the entropy-data-teams skill (in this same plugin), let them pick, and use the returned id as TEAM_NAME. A free-text value is still accepted (the ODPS schema does not enforce membership), but the registered id is preferred.

Step 3 — Pick the dbt adapter and profile block

Map PLATFORM to the right dbt adapter package and the profiles.yml.example body:

PLATFORM	`DBT_ADAPTER`	`PROFILE_BLOCK` (substituted into `profiles.yml.example`)
`databricks`	`dbt-databricks`	`type: databrickscatalog: <CATALOG>schema: <SCHEMA>host: <fill in>http_path: <fill in>token: <fill in>threads: 4`
`snowflake`	`dbt-snowflake`	`type: snowflakeaccount: <fill in>user: <fill in>password: <fill in>role: <fill in>database: <CATALOG>warehouse: <fill in>schema: <SCHEMA>threads: 4`
`bigquery`	`dbt-bigquery`	`type: bigquerymethod: oauthproject: <CATALOG>dataset: <SCHEMA>location: <fill in>threads: 4`
`postgres`	`dbt-postgres`	`type: postgreshost: <fill in>user: <fill in>password: <fill in>port: 5432dbname: <CATALOG>schema: <SCHEMA>threads: 4`

Step 4 — Scaffold the dbt project

Templates are at ${PLUGIN_ROOT}/skills/dataproduct-bootstrap/templates/. Copy each template into the working directory, substituting placeholders.

Template	Destination
`pyproject.toml`	`pyproject.toml` (substitute `{{DBT_ADAPTER}}` — `dbt-snowflake`, `dbt-databricks`, etc. — so `uv sync` installs the right adapter alongside the other dev deps)
`dbt_project.yml`	`dbt_project.yml`
`.gitignore`	`.gitignore` (merge if one already exists; do not overwrite)
`README.md`	`README.md` (merge or back up if one already exists)
`profiles.yml.example`	`profiles.yml.example`
`models/input_ports/_models.yml`	`models/input_ports/_models.yml`
`models/staging/_models.yml`	`models/staging/_models.yml`
`models/intermediate/_models.yml`	`models/intermediate/_models.yml`
`models/output_ports/v1/_models.yml`	`models/output_ports/v1/_models.yml`

Also create empty directories analyses/, macros/, seeds/, snapshots/, tests/. If a directory cannot be empty in git, drop a single .gitkeep file.

Check the user's existing `~/.dbt/profiles.yml`

After scaffolding, run a read-only check against ~/.dbt/profiles.yml (it likely already exists if the user works on other dbt projects). Do not modify it. Record one of three outcomes — Step 6 uses this to pick the right next-steps bullet and table entry.

missing — file does not exist. User can copy profiles.yml.example as-is.
exists, no collision — file exists but does not define a top-level key <DBT_PROJECT_NAME>:. User must merge the new profile block into it.
exists, collision — file exists and already defines <DBT_PROJECT_NAME>:. Flag prominently; user must reconcile (rename this project, replace the existing block, or confirm it already points at the right warehouse).

Check with test -f ~/.dbt/profiles.yml for existence and grep -nE '^<DBT_PROJECT_NAME>:' ~/.dbt/profiles.yml for the collision. Top-level YAML keys only — do not match nested occurrences.

Step 5 — Hand off to entropy-data-sync

Now the dbt skeleton is in place. Invoke the entropy-data-sync skill (in this same plugin) to add ODPS, ODCS, OpenLineage transport, and the GitHub Actions workflow.

Pass the parameters you already collected (DATA_PRODUCT_ID, DATA_PRODUCT_NAME, PURPOSE, TEAM_NAME, PLATFORM, CATALOG, SCHEMA, TABLE) so the user does not have to answer them again. entropy-data-sync resolves API_HOST itself from the entropy-data CLI connection.

If DATA_PRODUCT was loaded from Entropy Data in Step 1, do this before invoking entropy-data-sync so its audit sees the artifacts as already present (no template-generated stubs that would clobber the draft):

Write the fetched ODPS to <DATA_PRODUCT_ID>.odps.yaml (the same YAML the CLI returned — do not regenerate from the template).
For each output port in the draft that references a contract id, fetch and save it: entropy-data datacontracts get <contract-id> -o yaml > models/output_ports/v<N>/<contract-id>.odcs.yaml (default v1 if the output port does not declare a version).
If any of these fetches fail (404, network error), surface the error and continue — entropy-data-sync will create stubs from templates for whatever is missing.

The integration skill will run its own audit. For a brand-new product, every artifact is missing and created; for a draft-loaded product, ODPS and ODCS show as already present and sync just fills in OpenLineage, the workflow, and the model-layout placeholders.

Step 6 — Final report

After both skills have run, end with this two-part recap. Use the same Status enum the other skills use: created, updated, already present, deferred, skipped.

Part 1 — outcome table. State the mode at the top of the recap — one of Mode: new product or Mode: bootstrapped from existing draft <DATA_PRODUCT_ID>.

Artifact	Status	Details
`dbt_project.yml`	…	adapter = `<DBT_ADAPTER>`, models block configured
`profiles.yml.example`	…	platform = `<PLATFORM>`
`README.md`	…	new or merged into existing
`.gitignore`	…	new or merged into existing
Model layout	…	`models/{input_ports,staging,intermediate,output_ports/v1}/` + `_models.yml` placeholders
Empty dbt dirs	…	`analyses/`, `macros/`, `seeds/`, `snapshots/`, `tests/`
`<DATA_PRODUCT_ID>.odps.yaml` (from draft)	…	only when bootstrapped from existing draft: `created` (fetched) or `skipped` (fetch failed)
Output-port ODCS files (from draft)	…	only when bootstrapped from existing draft: `<N>` file(s) under `models/output_ports/v<N>/`, or `skipped` if no contracts were linked
`~/.dbt/profiles.yml` (local)	`deferred`	one of: `missing`, `exists – merge required`, or `collision: <DBT_PROJECT_NAME> already defined`
`entropy-data-sync` handoff	…	"ran" / "skipped" — see sync's own report for ODPS/ODCS/OpenLineage/workflow rows

Part 2 — next steps. Bullet list, include only what applies:

uv venv && source .venv/bin/activate && uv pip install dbt-core <DBT_ADAPTER> openlineage-dbt datacontract-cli entropy-data
Wire up ~/.dbt/profiles.yml (pick the bullet that matches the Step 4 check):
- missing → cp profiles.yml.example ~/.dbt/profiles.yml, then fill in credentials.
- exists, no collision → append the <DBT_PROJECT_NAME>: block from profiles.yml.example to ~/.dbt/profiles.yml, then fill in credentials.
- exists, collision → <DBT_PROJECT_NAME>: is already defined in ~/.dbt/profiles.yml. Reconcile manually: rename this project, replace the existing block, or confirm it already points at the right warehouse.
git init && git add . && git commit -m "Initial commit" (if the directory is not already a git repo).
Create a GitHub repo and push; set the secrets called out by the sync skill (ENTROPY_DATA_API_KEY, platform creds).
Fill in the data contract schema in models/output_ports/v1/<CONTRACT_FILE>.
Any deferred items surfaced by the sync skill's report (e.g. git connections to register after first CI publish).

If there is nothing in Part 2, write a single line: No further action required.

Constraints

Do not run dbt init — it generates an example layout that does not match the Entropy Data conventions. Use the templates here.
Do not commit secrets. profiles.yml is in .gitignore; only profiles.yml.example is checked in.
Do not invent credentials. Every secret in profiles.yml.example should be a <fill in> placeholder.
Idempotent on greenfield only. If dbt_project.yml exists, route the user to entropy-data-sync; do not overwrite.
Do not run git init, git commit, or any push — surface those as next steps for the user instead.

When it activates

Automatically, when a task matches. Trigger when the user asks to start a new data product, scaffold a new dbt project, or "create a data pr

About this skill

Automatic

Data Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

hashgraph-online / awesome-codex-plugins

plugins/entropy-data/dataproduct-builder-dbt/skills/dataproduct-bootstrap/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/10/2026Version v1Bundled 5 files

Illustrative Want to see what hashgraph-online/dataproduct-bootstrap would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

hashgraph-online/dataproduct-implementGiven an Entropy Data data product URL or id, fetch its data contracts (output port ODCS files written next to the SQL under models/output_ports/v<N>/, input port ODCS files cached next to their dbt source under models/input_ports/), translate the schema into dbt models, and ensure the project has the publishing layer (ODPS, OpenLineage, GitHub Actions). Trigger when the user asks to "implement the data product <url>", "build the dbt pipeline for this data product", or "scaffold dbt models from Close match+64 pts pass-rate lift

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SkillScore

Safety

What it changes

Track record

Use this skill

Bootstrap a new dbt data product

What this skill produces

How to run this skill

Plan announcement (before Step 1)

Step 1 — Pre-checks

Step 2 — Gather parameters

Step 2a — If DATA_PRODUCT was loaded from Entropy Data

Step 2b — If no draft was loaded (new product)

Step 3 — Pick the dbt adapter and profile block

Step 4 — Scaffold the dbt project

Check the user's existing ~/.dbt/profiles.yml

Step 5 — Hand off to entropy-data-sync

Step 6 — Final report

Constraints

When it activates

SkillSafety

Source

Other skills for the same job

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Step 2a — If `DATA_PRODUCT` was loaded from Entropy Data

Check the user's existing `~/.dbt/profiles.yml`