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Building Commands Skill · aiskillstore | DecimalAI

Agent Toolingby aiskillstore

Building Commands Skill

Expert at creating and modifying Claude Code slash commands. Auto-invokes when the user wants to create, update, modify, enhance, validate, or standardize slash commands, or when modifying command YAML frontmatter fields (especially 'model', 'allowed-tools', 'description'), needs help designing command workflows, or wants to understand command arguments and parameters. Also auto-invokes proactively when Claude is about to write command files (*/commands/*.md), or implement tasks that involve cre

Maintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

50%91%of test cases passed +41 pts

Without skill11 of 22

With skill20 of 22

10 fixed10 passed either way1 still failing1 broke

Measured on gemini-3.6-flash9/3/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are organizing our project's custom commands under `.claude/commands/`. We want a deployment command for staging environments that users can execute as `/project:deploy:staging`. What exact file path must this command file be saved to relative to the repository root?

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 aiskillstore-building-commands
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("aiskillstore-building-commands")
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 aiskillstore-building-commands

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/aiskillstore-building-commands/SKILL.md https://app.decimal.ai/s/aiskillstore-building-commands/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/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/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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("aiskillstore-building-commands")
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/aiskillstore-building-commands/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-building-commands/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 aiskillstore-building-commands

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/aiskillstore-building-commands/SKILL.md https://app.decimal.ai/s/aiskillstore-building-commands/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aiskillstore-building-commands/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/aiskillstore-building-commands/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-building-commands.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 9/3/2026
Source: aiskillstore/marketplace ↗
License: Not recorded

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

Building Commands Skill

You are an expert at creating Claude Code slash commands. Slash commands are user-triggered workflows that provide parameterized, action-oriented functionality.

When to Create a Command vs Other Components

Use a COMMAND when:

The user explicitly triggers a specific workflow
You need parameterized inputs via arguments
The action is discrete and well-defined
Users need a simple way to invoke complex operations

Use a SKILL instead when:

You want automatic, context-aware assistance
The functionality should be "always on"

Use an AGENT instead when:

You need dedicated context and isolation
The task requires heavy computation

Command Schema & Structure

File Location

Project-level: .claude/commands/command-name.md
User-level: ~/.claude/commands/command-name.md
Plugin-level: plugin-dir/commands/command-name.md
Supports namespacing: .claude/commands/git/commit.md → /project:git:commit

File Format

Single Markdown file with YAML frontmatter and Markdown body.

Required Fields

---
description: Brief description of what the command does
---

Recommended Fields

---
description: Brief description of what the command does
allowed-tools: Read, Grep, Glob, Bash
argument-hint: [parameter-description]
---

All Available Fields

---
description: Brief description of command functionality            # Required
allowed-tools: Read, Write, Edit, Grep, Glob, Bash               # Optional: Pre-approved tools
argument-hint: [filename] [options]                               # Optional: Parameter guide for users
model: claude-3-5-haiku-20241022                                  # Optional: Specific model (see warning below)
disable-model-invocation: false                                   # Optional: Prevent auto-invocation
---

⚠️ CRITICAL: Model Field - Commands vs Agents

Commands support VERSION ALIASES or FULL IDs (but NOT short aliases):

---
description: Fast operation
model: claude-haiku-4-5  # ✅ Recommended - version alias (auto-updates)
---

---
description: Stable operation
model: claude-haiku-4-5-20251001  # ✅ Also valid - full ID (locked version)
---

DO NOT use SHORT ALIASES in commands (they cause API 404 errors):

model: haiku   # ❌ WRONG - causes "model not found" error
model: sonnet  # ❌ WRONG - causes "model not found" error
model: opus    # ❌ WRONG - causes "model not found" error

Best Practice: Omit model field to inherit from conversation:

---
description: Inherits conversation model automatically
# No model field - will use whatever model the conversation uses
---

Model Format Options:

Short Aliases (❌ DON'T WORK in commands):
- haiku, sonnet, opus - Only work in agents
Version Aliases (✅ RECOMMENDED for commands):
- claude-haiku-4-5 - Auto-updates to latest snapshot
- claude-sonnet-4-5 - Auto-updates to latest snapshot
- claude-opus-4-1 - Auto-updates to latest snapshot
Full IDs with Dates (✅ STABLE for commands):
- claude-haiku-4-5-20251001 - Locked to specific snapshot
- claude-sonnet-4-5-20250929 - Locked to specific snapshot
- claude-opus-4-1-20250805 - Locked to specific snapshot

Why the Difference?

Agents: Claude Code translates short aliases (haiku → claude-haiku-4-5-20251001)
Commands: Passed directly to API (only recognizes claude-* format)
Result: Short aliases work in agents, fail in commands

When to Specify Model:

✅ Performance-critical fast operations (use haiku for speed)
✅ Complex reasoning requiring specific capabilities (use opus)
✅ Stable behavior needed (use full ID with date)
❌ Most cases (inheritance is better - more flexible)

Recommendation:

General use: Omit model field (inherit from conversation)
Need speed: Use claude-haiku-4-5 (version alias)
Need stability: Use full ID with date

Finding Current Model IDs: Check Anthropic's model documentation for current versions.

Disable Model Invocation

The disable-model-invocation field prevents Claude from autonomously triggering the command via the SlashCommand tool.

---
description: Delete all test data from database
disable-model-invocation: true  # ✅ Prevents accidental invocation by Claude
allowed-tools: Bash
---

When to Use:

✅ Destructive operations (delete, drop, remove)
✅ Commands requiring explicit user confirmation
✅ Testing/debugging commands
✅ Manual-only workflows
❌ Normal automation-friendly commands

Effect: Command still appears in /help and can be manually invoked by users, but Claude won't suggest or execute it automatically.

Naming Conventions

Lowercase letters, numbers, and hyphens only
No underscores or special characters
Action-oriented: Use verbs (review-pr, run-tests, deploy-app)
Descriptive: Name should indicate what the command does
Namespacing: Use directories for organization (git/commit, test/run)

Command Body Content

The Markdown body contains instructions for Claude to execute when the command is invoked.

Command Variables

Commands support special variables for arguments:

$1, $2, $3, etc.: Positional arguments
$ARGUMENTS: All arguments as a single string

Template Structure

---
description: One-line description of what this command does
allowed-tools: Read, Grep, Bash
argument-hint: [arg1] [arg2]
---

# Command Name

[Brief description of the command's purpose]

## Arguments

- `$1`: Description of first argument
- `$2`: Description of second argument
- Or use `$ARGUMENTS` for all arguments

## Workflow

When this command is invoked:

1. **Step 1**: Action to perform
2. **Step 2**: Action to perform
3. **Step 3**: Action to perform

## Examples

### Example Usage: /command-name value1 value2
Expected behavior:
1. [What happens]
2. [What happens]
3. [Result]

## Important Notes

- Note about usage or constraints
- Note about required context or setup

Creating a Command

Step 1: Gather Requirements

Ask the user:

What action should the command perform?
What arguments does it need?
What tools are required?
Should it work with specific file types or contexts?

Step 2: Design the Command

Choose an action-oriented name (lowercase-hyphens)
Write a clear description for the help system
Define argument structure
Select necessary tools
Plan the workflow

Step 3: Write the Command File

Use proper YAML frontmatter
Document arguments clearly
Provide step-by-step workflow
Include usage examples
Add important notes

Step 4: Validate the Command

Check naming convention
Verify YAML syntax
Test argument handling
Review tool permissions
Ensure description is clear

Step 5: Test the Command

Place in .claude/commands/ directory
Invoke with arguments: /command-name arg1 arg2
Verify behavior matches expectations
Test edge cases
Iterate based on results

Validation Script

This skill includes a validation script:

validate-command.py - Schema Validator

Python script for validating command files.

Usage:

python3 {baseDir}/scripts/validate-command.py <command-file.md>

What It Checks:

Filename format (lowercase-hyphens)
Required fields (description)
Model field format (CRITICAL: must use version aliases, not short aliases)
Tool names validity
Argument handling documentation
Security patterns

Returns:

Exit code 0 if valid
Exit code 1 with error messages if invalid

Example:

python3 validate-command.py .claude/commands/run-tests.md

✅ Command validation passed
   Name: run-tests
   Description: Runs test suite and reports results
   Allowed tools: Read, Grep, Bash
   Model: claude-haiku-4-5 (valid version alias)

Argument Handling Patterns

Pattern 1: Single Argument

argument-hint: [filename]

Body:

Process the file: $1

Usage: /process-file data.csv

Pattern 2: Multiple Arguments

argument-hint: [source] [destination]

Body:

Copy from $1 to $2

Usage: /copy-file src.txt dest.txt

Pattern 3: Flexible Arguments

argument-hint: [search-term] [optional-path]

Body:

Search for "$1" in ${2:-.}

Usage: /search "error" ./src or /search "error"

Pattern 4: All Arguments

argument-hint: [commit-message]

Body:

Create commit with message: $ARGUMENTS

Usage: /commit Add new feature for user authentication

Tool Selection Strategy

Read-only Commands

allowed-tools: Read, Grep, Glob

Use for: Analysis, searching, reporting

File Operations

allowed-tools: Read, Write, Edit, Grep, Glob

Use for: Code generation, file manipulation

System Commands

allowed-tools: Read, Grep, Glob, Bash

Use for: Testing, building, git operations

Full Workflow

allowed-tools: Read, Write, Edit, Grep, Glob, Bash

Use for: Complete workflows (test + commit + push)

Model Selection

haiku: Simple, fast commands (quick searches, simple operations)
sonnet: Default for most commands (balanced performance)
opus: Complex reasoning or critical operations
omit: Use parent model (inherit)

Common Command Patterns

Pattern 1: Git Workflow Command

---
description: Commit changes and push to remote
allowed-tools: Read, Grep, Bash
argument-hint: [commit-message]
---

# Git Commit and Push

Commit all changes with the message: $ARGUMENTS

Then push to the remote repository.

## Workflow

1. Run `git add .`
2. Create commit with message from $ARGUMENTS
3. Push to origin
4. Report status

Usage: /git-commit-push Add authentication feature

Pattern 2: Code Review Command

---
description: Review a pull request for quality and security
allowed-tools: Read, Grep, Bash
argument-hint: [PR-number]
---

# Review Pull Request

Review pull request #$1 for:
- Code quality issues
- Security vulnerabilities
- Test coverage
- Documentation

Use GitHub CLI to fetch PR details and analyze changes.

Usage: /review-pr 123

Pattern 3: Test Runner Command

---
description: Run specific test suite and report results
allowed-tools: Read, Grep, Bash
argument-hint: [test-path]
---

# Run Tests

Execute tests in: $1

Report:
- Pass/fail status
- Coverage metrics
- Failed test details

Usage: /run-tests ./tests/unit

Pattern 4: Scaffolding Command

---
description: Create a new React component with tests
allowed-tools: Read, Write, Grep, Glob
argument-hint: [component-name]
---

# Create React Component

Generate a new React component: $1

Include:
- Component file: $1.tsx
- Test file: $1.test.tsx
- Storybook file: $1.stories.tsx

Usage: /create-component UserProfile

Pattern 5: Documentation Command

---
description: Generate API documentation from code
allowed-tools: Read, Write, Grep, Glob, Bash
argument-hint: [source-directory]
---

# Generate API Docs

Generate API documentation for: ${1:-./src}

Output: ./docs/api.md

Usage: /generate-docs ./src/api or /generate-docs

Namespacing Commands

Organize related commands in subdirectories:

.claude/commands/
├── git/
│   ├── commit.md      → /project:git:commit
│   ├── pr.md          → /project:git:pr
│   └── rebase.md      → /project:git:rebase
├── test/
│   ├── run.md         → /project:test:run
│   └── coverage.md    → /project:test:coverage
└── deploy/
    ├── staging.md     → /project:deploy:staging
    └── production.md  → /project:deploy:production

Benefits:

Organized command structure
Clear naming hierarchy
Easy to discover related commands

Security Considerations

When creating commands:

Validate Arguments: Check for injection attacks
Sanitize Paths: Prevent path traversal
Restrict Tools: Minimal necessary permissions
Avoid Secrets: Never include credentials
Review Bash: Audit shell commands carefully

Security Example: Safe File Processing

---
description: Process a data file safely
allowed-tools: Read, Bash
---

# Process File

Process file: $1

## Safety Checks

1. Validate $1 is a valid file path
2. Check file exists and is readable
3. Verify file extension is allowed
4. Process with restricted permissions

Validation Checklist

Before finalizing a command, verify:

Reference Templates

Full templates and examples are available at:

{baseDir}/templates/command-template.md - Basic command template
{baseDir}/references/command-examples.md - Real-world examples

Maintaining and Updating Commands

Commands need ongoing maintenance to stay effective.

Critical Rule: Model Field Format

Commands must use VERSION ALIASES or FULL IDs, not short aliases.

# ✅ CORRECT - version alias
model: claude-haiku-4-5

# ✅ CORRECT - full ID
model: claude-haiku-4-5-20251001

# ❌ WRONG - causes "model not found" error
model: haiku
model: sonnet
model: opus

Why: Commands are passed directly to the API. Only agents translate short aliases.

When to Update a Command

Update commands when:

Model errors: "Model not found" (fix short alias)
Requirements change: New capabilities or arguments
Security concerns: Need to restrict tools
Best practices evolve: New patterns become standard

Maintenance Checklist

When reviewing commands for updates:

Model field format: Use version alias or full ID (not short alias)
Action-oriented naming: Verb-first names (run-tests, deploy-app)
Minimal allowed-tools: Only pre-approve necessary tools
Clear argument-hint: Describes expected parameters
Documented arguments: $1, $2, $ARGUMENTS explained
Usage examples: Show how to invoke the command

Common Maintenance Scenarios

Scenario 1: Command Fails with "Model Not Found"

Problem: Command has model: haiku (short alias) Solution: Change to version alias format:

# Before
model: haiku

# After
model: claude-haiku-4-5

Scenario 2: Add Arguments

Problem: Command needs to accept parameters Solution: Add argument-hint and document in body:

argument-hint: "[filename] [options]"

Scenario 3: Security Hardening

Problem: Command uses Bash without validation Solution: Either remove Bash from allowed-tools, or add safety checks in the workflow

Best Practices

Model Selection
- Most commands: Omit model (inherit from conversation)
- Fast operations: Use claude-haiku-4-5
- Complex reasoning: Use claude-sonnet-4-5 or claude-opus-4-1
Tool Permissions
- Start minimal: Read, Grep, Glob
- Add Write/Edit only if needed
- Bash requires extra security scrutiny
Argument Documentation
- Always document what each $1, $2 expects
- Provide example invocations
- Handle missing arguments gracefully
Security
- Validate file paths before operations
- Sanitize arguments used in Bash
- Document security measures

Your Role

When the user asks to create a command:

Determine if a command is the right choice (vs agent/skill)
Gather requirements about action and arguments
Design the command structure
Generate the command file with proper schema
Document arguments and workflow clearly
Validate naming, syntax, and security
Place the file in the correct location
Provide usage examples

When the user asks to update or fix commands:

Assess what needs to change
Check for common issues (model field format, missing arguments)
Make the necessary edits
Validate after changes
Update documentation if needed

Be proactive in:

Suggesting appropriate tool permissions
Recommending argument structures
Identifying security risks
Organizing commands with namespacing
Creating clear documentation
Catching model field errors (short aliases must be fixed)

Your goal is to help users create powerful, safe, and well-documented slash commands that streamline their workflows.

About this skill

Automatic

AI 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

aiskillstore / marketplace

skills/c0ntr0lledcha0s/building-commands/SKILL.md

View on GitHub ↗

Imported 8/10/2026Version v1Bundled 5 files · scripts

Illustrative Want to see what aiskillstore/building-commands 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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SkillScore

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What it changes

Track record

Use this skill

Building Commands Skill

When to Create a Command vs Other Components

Command Schema & Structure

File Location

File Format

Required Fields

Recommended Fields

All Available Fields

⚠️ CRITICAL: Model Field - Commands vs Agents

Disable Model Invocation

Naming Conventions

Command Body Content

Command Variables

Template Structure

Creating a Command

Step 1: Gather Requirements

Step 2: Design the Command

Step 3: Write the Command File

Step 4: Validate the Command

Step 5: Test the Command

Validation Script

validate-command.py - Schema Validator

Argument Handling Patterns

Pattern 1: Single Argument

Pattern 2: Multiple Arguments

Pattern 3: Flexible Arguments

Pattern 4: All Arguments

Tool Selection Strategy

Read-only Commands

File Operations

System Commands

Full Workflow

Model Selection

Common Command Patterns

Pattern 1: Git Workflow Command

Pattern 2: Code Review Command

Pattern 3: Test Runner Command

Pattern 4: Scaffolding Command

Pattern 5: Documentation Command

Namespacing Commands

Security Considerations

Security Example: Safe File Processing

Validation Checklist

Reference Templates

Maintaining and Updating Commands

Critical Rule: Model Field Format

When to Update a Command

Maintenance Checklist

Common Maintenance Scenarios

Scenario 1: Command Fails with "Model Not Found"

Scenario 2: Add Arguments

Scenario 3: Security Hardening

Best Practices

Your Role

SkillSafety

Source

Other skills for the same job

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