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nemotron-customize · NVIDIA-NeMo | DecimalAI

AI/MLby NVIDIA-NeMo

nemotron-customize

Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints.

Never made a case worseMaintained

SkillScore

SkillScore59/100

Test results 59

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

9%59%of test cases passed +50 pts

Without skill2 of 22

With skill13 of 22

11 fixed2 passed either way9 still failing0 broke

Model 7/31/2026v1 · latest is v2

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 10/21/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

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 nemotron-customize
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("nemotron-customize")
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 nemotron-customize

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/nemotron-customize/SKILL.md https://app.decimal.ai/s/nemotron-customize/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/nemotron-customize/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

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

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

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/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/nemotron-customize/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add nemotron-customize

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("nemotron-customize")
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/nemotron-customize/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add nemotron-customize

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/nemotron-customize/SKILL.md https://app.decimal.ai/s/nemotron-customize/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/nemotron-customize/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/nemotron-customize/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/nemotron-customize.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 7/31/2026)
Source: NVIDIA-NeMo/Nemotron ↗
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

nemotron-customize

IMPORTANT: Read this file before answering any nemotron-customize, Nemotron customization, governed Curator flow, translation, tokenizer extension, synthetic-data, BYOB benchmark, SFT, PEFT, RL, conversion, optimization, checkpoint or existing/hosted-endpoint evaluation, or multi-step pipeline request. This applies whether the user names one step or asks you to compose several steps into a pipeline.

Evaluation requests count even when no training is involved: "evaluate", "benchmark", "smoke test", or "score" an existing/hosted endpoint, an API/model ID, or a deployed model all route to eval/model_eval. Read this skill for those too.

Purpose

Turn a model-customization request into a repo-native Nemotron step pipeline. Plan the DAG, validate artifact wiring, and create only the YAML/config files needed to run existing steps.

Use this skill only for inspecting, configuring, validating, running, or submitting existing Nemotron steps or multi-step training/customization pipelines. For frontend, dashboard, visualization, generic ML advice, billing/access, or unrelated coding tasks, stop with a short scope note and do not inspect the step catalog or edit files in that turn.

Inputs

Required:

The requested outcome and enough concrete values to run the selected steps: input data, model/checkpoint or endpoint, and output location.

Optional when the selected route needs them:

Language, backend, hardware/GPU count, metrics, task IDs, tokenizer, and step/config preferences.
Auth environment-variable names for hosted services. Accept names only; never request, inline, or commit secret values.

Resolve omitted values by consulting, in order: selected state/config files, explicit invocation arguments, established agent context, then the current user prompt. An explicit user correction overrides an older source. If a required value remains unknown, ask for it or return Blocked; do not guess it.

Prerequisites

A checkout of the Nemotron repo with src/nemotron/steps/ present; run from the repo root with uv available.
For remote execution, an env profile TOML (NEMOTRON_ENV_FILE or env*.toml) with a section matching the selected step.
Hosted-service credentials must already be exported through the environment variable expected by the selected step.
Persona MCQ also needs its configured endpoint variables and may require NGC_API_KEY for managed persona assets and HF_TOKEN for gated models.

Limitations

Does not invent new catalog steps. When no existing step, runner, recipe, CLI, or config can satisfy the request, it names the gap (Explorer mode) instead of fabricating a step.
Produces YAML/config for existing steps; new Python/shell is out of scope except in Explorer mode after the gap is approved.
Not for deployment-only/serving, frontend, dashboards, generic ML advice, or non-Nemotron tasks.
Does not guess concrete values (paths, model IDs, GPU counts, profiles); it asks or returns Blocked when they are missing.

Core Rule

Use bundled references first. The references/ folder is the first decision surface for routing, artifacts, patterns, hardware heuristics, and command shape. Use src/nemotron/steps/... only as a live verification/fallback source when you need exact current config fields, manifests, runner imports, or details missing from bundled references.

If sources disagree:

Checked live repo files win for exact execution.
Bundled references win for initial routing and planning.
Upstream docs/context packs are used only for exceptional code generation or library API details.

Before You Begin

Read this SKILL.md workflow and the relevant bundled reference before opening repo source files.
Route from references/CATALOG.md and references/ARTIFACTS.md before any broad repo exploration. Once a route is determined, verify only the selected live step/config/env files needed for the answer.
Do not emit commands with fake paths, placeholder model IDs, guessed task IDs, guessed batch profiles, or default auth variable names presented as facts. Ask for missing concrete values or return a Blocked handoff.
Use references/COMMANDS.md as the authoritative checklist before finalizing configs or execution commands.
For pipeline requests, plan before editing. Do not create or modify files until the DAG, artifact edges, required inputs, and validation checks are stated and approved.
For one-shot command requests, prefer a complete parameterized command in one response over exploratory prose, but only after required inputs are known. If the user already provides the needed values and asks for only a command, answer with the command first and keep explanation minimal.
Output discipline (keeps responses tight): emit one command block per step, include only flags the step actually defines, and add no speculative or invented flags. Keep narrative to a few lines — the command plus the required safety/profile callouts, not a tutorial. Do not restate reference content the user did not ask for.
Do not spawn subagents for one-shot command lookup. Use the bundled command reference directly; verify only the selected step if needed.

Safety

Keep Bash scoped to repo-safe commands such as uv run nemotron steps ..., targeted tests, git status/diff, and config validation. Never run environment dumps (env, printenv, broad export) or commands that expose secret values. For remote submissions, destructive changes, or expensive launches, confirm before execution.

When inspecting env/config files, avoid printing whole files that may contain secrets. Use targeted reads, report only section names and env-var names, and redact values for fields containing token, key, secret, password, credential, or auth.

Reference Map

Question	Read first	Live fallback / verification
Which step or category fits?	`references/CATALOG.md`	`uv run nemotron steps list/show`, then selected `step.toml`
Do artifacts chain?	`references/ARTIFACTS.md`	`src/nemotron/steps/types.toml`
What run shape should I emit?	`references/COMMANDS.md`	checked-in config YAML plus active profile TOML
Remote profile generation or selection	`references/COMMANDS.md`	active `NEMOTRON_ENV_FILE`, `env.toml`, or `env.*.toml`
What hardware/backend should I recommend?	`references/HARDWARE.md`	selected step `[[models]]` and `[[strategies]]`
Which cross-step guardrails apply?	`references/PATTERNS.md`	`src/nemotron/steps/patterns/<id>.md`
How do I run the full workflow?	`references/WORKFLOW.md`	selected step configs, `step.py`, and runners
Which upstream library API should generated code use?	`references/context/index.toml` -> matching pack	selected `step.py`, `_runners/`, upstream docs
New project scaffold, only when existing repo code cannot support the request	`references/act/PROJECT.md`	existing repo project/recipe shape
Per-stage code rules, only when existing repo code cannot support the request	`references/act/STAGE.md`	selected `step.py` and shared runner

Do not start by reading category READMEs or step.toml for ordinary decisions. Select candidates from bundled references, then verify exact live details before writing configs or final commands.

Routing

Use references/CATALOG.md as the authoritative home for step selection and route-specific fast paths. Use ARTIFACTS.md, PATTERNS.md, and HARDWARE.md only to resolve artifact, cross-step, or hardware constraints after the catalog narrows the route.

The catalog includes four specialized routing families that must not be collapsed into generic steps:

Use the six-step governed Curator flow when the request needs stable document identity, measured/approved filtering, audit evidence, holdout decontamination, or nested fixed-budget subsets.
Use sdg/persona_mcq for persona-grounded MCQ-shaped training data and byob/mcq for held-out MCQ benchmarks. Use byob/bfcl for executable function-calling benchmarks and their translation/evaluation lifecycle.
Use tokenizer_extension/* for tokenizer construction, fertility, embedding initialization, and cross-vocabulary BPB evaluation. Continue pretraining the resulting resized HF checkpoint with a pretraining step; tokenizer evaluation does not replace downstream model evaluation.
For eval/model_eval, choose launcher when NeMo Evaluator Launcher owns the deployment/executor flow and direct when the endpoint is already hosted and the selected Nemotron env profile should own scheduling.

Each step is independent and stitching steps together is your job. Compose any pipeline by artifact matching from the user's end goal: chain a step only when the next step consumes an artifact type nothing upstream already produces. Do not rely on fixed, named step combinations.

Instructions

Follow the flow that matches the request: a recommendation/plan, a single-step command, or a multi-step pipeline. In all cases, route from the bundled references first, gather required inputs, and verify the selected live step before presenting anything as runnable.

Recommendation Response

Use this shape for planning answers:

Decision, Why, Required inputs, Config/command, Avoid, and Next step. Call out the stack to avoid when the user's constraints make it a poor fit.

Whenever the answer includes a command that touches a hosted service or remote execution, also state, in the answer:

The auth env-var name and that its value must be exported in the environment, never inlined or committed (never print the value).
For --batch/--run, the env TOML profile prerequisite; if no profile exists, mark the command Blocked or give the local --dry-run shape.

Single-Step Command Flow

Confirm repo root has pyproject.toml and src/nemotron/steps/.
Read references/CATALOG.md and the selected section of references/COMMANDS.md.
Verify the selected live step with uv run nemotron steps show <step_id> when available, or the selected step.toml when the CLI is unavailable.
Read the requested checked-in config or user overlay before emitting the command.
For remote execution, read NEMOTRON_ENV_FILE or repo-root env*.toml and pick an actual section whose profile matches the step.
Emit the full command in one reply with the source tier: Verified, Repo-grounded, Reference-grounded, or Blocked.

Canonical command shapes live in references/COMMANDS.md.

Pipeline Workflow

For pipelines with two or more stages, use Orient -> Plan -> Act -> Verify. Read references/WORKFLOW.md for the phase checklist.

Orient from bundled references and user constraints.
Plan a DAG with artifact types, configs, patterns, and validation checks.
Wait for approval before writing configs or code.
Act with YAML/config-only changes whenever an existing step can satisfy the request.
Verify every generated YAML, artifact edge, command, and README command before reporting completion.

Catalog Mode

Use when the request maps to existing steps. Fast path:

references/CATALOG.md -> references/ARTIFACTS.md -> references/COMMANDS.md -> verify selected live manifest/config/profile -> add a new named config under the selected step's config/ directory.

Customization Surface

Always customize through the step catalog under src/nemotron/steps/. Never divert to alternate recipe CLIs such as src/nemotron/cli/commands/super3/ or .../nano3/, even for Super3/Nano3 work. If a request seems to need those, map it back to the equivalent catalog step (e.g. sft/megatron_bridge).
Make customizations as NEW config files inside the selected step's src/nemotron/steps/<cat>/<step>/config/ directory, for example src/nemotron/steps/sft/megatron_bridge/config/my_super3.yaml.
Never edit the checked-in default.yaml, tiny.yaml, other shipped configs, step.toml, step.py, or shared runners. Adding a new config file beside them is the expected and only customization write.
Base new configs on the checked-in default.yaml schema (read it, copy the needed fields), then override only what the request requires.

Explorer Mode

Use only after confirming no existing step, runner, recipe, CLI, or YAML config surface can satisfy the request. Full procedure lives in references/WORKFLOW.md.

Configuration Alignment

Surface these constraints before commands or config writes:

SFT packing pack_size, Megatron-Bridge seq_length, packed sequence size, tokenizer, and chat template must match.
The 128K/256K Super3 configs are dry-run topology references, not launch-ready stock configs; they require externally prepared alignment-aware packed shards and mixed-precision validation.
Prepared packed_parquet and binidx are tokenizer-locked; rebuild after tokenizer, chat-template, sequence-length, split, or blend changes.
Tokenizer-extension comparisons must keep corpus and vocabulary budget fixed. Use max_docs, not max_tokens, for BPB comparisons across vocabularies, and compare BPB rather than per-token perplexity.
Curator policy candidates are not approvals. Profile unfiltered input, record an explicit approval, then apply the policy; audit completeness only against a producer-emitted manifest and attribute loss only when a ledger exists.
Decontamination removes from training only and detects whole-document near-duplicates; do not claim it catches embedded benchmark questions.
Megatron-Bridge global batch size must be divisible by data-parallel size; start distributed validation with micro batch size 1.
TP/PP/CP/EP choices must fit GPU count, memory, topology, and model divisibility.
LoRA merge requires the exact base checkpoint/model and tokenizer used during adapter training.
Conversion/eval of Megatron checkpoints should point at a concrete iter_* checkpoint, not a parent run directory.
Hosted eval and translation configs store auth env-var names only, not values.

Operational Nuances

Smoke configs (tiny.yaml, tiny_chat.yaml) are wiring tests, not quality evidence.
${art:...} references belong in recipe-backed configs; standalone YAML uses plain paths.
Keep pretraining bin/idx data and blend.json from the same run/release.
Write customized configs as new files in the step's src/nemotron/steps/<cat>/<step>/config/ directory; never modify the checked-in default.yaml or other shipped configs.
For LoRA, preserve the exact base checkpoint and tokenizer/template metadata needed by later merge/eval.
For translation and hosted eval, mention auth environment variable names only, never values.
Direct evaluation runs to completion and writes per-task artifacts; launcher mode returns after acceptance, so poll its invocation to a terminal state before treating the evaluation as passed.

Boundaries

Do:

Always route through the step catalog under src/nemotron/steps/; never use alternate recipe CLIs (src/nemotron/cli/commands/super3|nano3/...).
Reuse repo CLIs, runners, recipes, steps, and checked-in configs first.
Customize by adding a new config under the step's config/ directory; base it on default.yaml rather than copying it blindly.
Validate artifact edges and cite patterns that changed the plan.
Ask about hardware/data/backend/output path when missing.
Surface tradeoffs such as AutoModel vs Megatron-Bridge and full SFT vs LoRA.

Do not:

Invent steps when a catalog step fits.
Skip Plan for pipelines with two or more stages.
Generate Python or shell when YAML is enough.
Add monitoring/W&B unless asked.
Assume GPU count, env profile, endpoint type, task ID, or auth value.
Generate Slurm/Airflow/Kubeflow wrappers unless the request explicitly needs deployment scaffolding.
Edit checked-in step files (default.yaml/tiny.yaml, other shipped configs, step.toml, step.py, runners); only add a new config beside them.
Restate all per-step rules in SKILL.md; use bundled references and source fallback.

Examples

Single-step routing (LoRA on a small box). User: "LoRA fine-tune a HF model on 2 GPUs." Route per CATALOG.md -> peft/automodel (HF base + small GPU count); do not offer Megatron-Bridge. Collect base model, JSONL data path, output dir, LoRA rank/alpha, then emit one uv run nemotron steps run peft/automodel -c <config> --dry-run ... command.

Multi-step pipeline (Super3 SFT). User: "data prep + SFT for Super3." This is two stages, so plan first: SFT on Super3 -> Megatron-Bridge, which consumes packed_parquet, so data_prep/sft_packing is required upstream. Present the DAG (sft_packing -> sft/megatron_bridge), align pack_size/seq_length/ tokenizer, wait for approval, then add new configs under src/nemotron/steps/<step>/config/<name>.yaml. Super3 needs a remote profile; state the env TOML prerequisite or mark Blocked.

Hosted-endpoint evaluation (no training). User: "benchmark my hosted model endpoint." Route to eval/model_eval. Use -c tiny_chat for a one-sample launcher smoke test, or -c direct when the endpoint is already hosted and the Nemotron profile must own the backend. Collect endpoint URL, model id, task IDs, matching tokenizer, durable output directory, and the auth env-var name (value exported, never inlined). See references/COMMANDS.md Evaluation Examples.

Troubleshooting

Situation	Action
Artifact types do not chain	Recheck `references/ARTIFACTS.md`; insert a converter or change the DAG before writing configs.
Remote profile or `--batch` is unclear	Read active env TOML; do not guess profile names.
Config key is unclear	Verify selected checked-in config, `step.py`, and shared runner before editing.
Strategy points to a missing context pack	Skip the pack, use catalog/pattern text, and flag the plan with `WARNING: <topic> docs unavailable`.
Hardware looks too small	Use `references/HARDWARE.md`; suggest smaller model, AutoModel, then LoRA before full Megatron-Bridge.
Two Act attempts fail	Stop, explain what was tried and failed, and ask how to proceed.
No existing repo path matches	Check `references/context/index.toml` and selected source fallback; use Explorer mode only after naming the gap.

When it activates

Automatically, when a task matches. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize

About this skill

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v6 — 10/21/2026

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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

Source

Imported

NVIDIA-NeMo / Nemotron

skills/nemotron-customize/SKILL.md

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License Apache-2.0Imported 10/21/2026Version v2Bundled 6 files

Illustrative Want to see what nemotron-customize 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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nemotron-customize

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