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Building Skills Skill · aiskillstore | DecimalAI

Agent Toolingby aiskillstore

Building Skills Skill

Expert at creating and modifying Claude Code skills. Auto-invokes when the user wants to create, update, modify, enhance, validate, or standardize skills, or when modifying skill YAML frontmatter fields (especially 'allowed-tools', 'description'), needs help designing skill architecture, or wants to understand when to use skills vs agents. Also auto-invokes proactively when Claude is about to write skill files (*/skills/*/SKILL.md), create skill directory structures, or implement tasks that invo

Maintained

SkillScore

SkillScore96/100

Test results 96

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

52%96%of test cases passed +43 pts

Without skill12 of 23

With skill22 of 23

11 fixed11 passed either way0 still failing1 broke

Measured on gemini-3.6-flash9/3/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are creating a skill for reading, searching, and inspecting system architecture diagrams and markdown documentation files without modifying any files or running shell commands. Which minimal pre-approved tools should be assigned in `allowed-tools`?

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-skills
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-skills")
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-skills

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

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-building-skills/SKILL.md

This gets SKILL.md only. The skill's 3 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-skills

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-skills")
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-skills/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-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/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-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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-skills")
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-skills/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-building-skills/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 3 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-skills

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

Copy SKILL.md

Save it in your project, as

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

This gets SKILL.md only. The skill's 3 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-skills/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-building-skills.json

This gets SKILL.md only. The skill's 3 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 Skills Skill

You are an expert at creating Claude Code skills. Skills are "always-on" expertise modules that Claude automatically invokes when relevant, providing context-aware assistance without explicit user invocation.

When to Create a Skill vs Other Components

Use a SKILL when:

You want automatic, context-aware assistance
The expertise should be "always on" and auto-invoked by Claude
You need progressive disclosure of context (Claude discovers resources as needed)
The functionality should feel like an integrated part of Claude's capabilities
You're providing domain expertise or specialized knowledge

Use an AGENT instead when:

You want explicit invocation with dedicated context
The task requires isolation and heavy computation
You need manual control over when it runs

Use a COMMAND instead when:

The user explicitly triggers a specific workflow
You need parameterized inputs via command arguments

Key Differences: Skills vs Agents

Aspect	Skills	Agents
Invocation	Automatic (Claude decides)	Explicit (user/Claude calls)
Context	Progressive disclosure	Full context on invocation
Structure	Directory with resources	Single markdown file
Best For	Always-on expertise	Specialized delegated tasks
Permissions	`allowed-tools` for pre-approval	Standard permission flow

Skill Schema & Structure

Directory Location

Project-level: .claude/skills/skill-name/
User-level: ~/.claude/skills/skill-name/
Plugin-level: plugin-dir/skills/skill-name/

Directory Structure

skill-name/
├── SKILL.md           # Required: Main skill definition
├── scripts/           # Optional: Executable scripts
│   ├── helper.py
│   └── process.sh
├── references/        # Optional: Documentation files
│   ├── api-guide.md
│   └── examples.md
└── assets/           # Optional: Templates and resources
    └── template.json

SKILL.md Format

Markdown file with YAML frontmatter and Markdown body.

Required Fields

---
name: skill-name           # Unique identifier (lowercase-hyphens, max 64 chars)
description: Brief description of WHAT the skill does and WHEN Claude should use it (max 1024 chars)
---

Optional Fields

---
version: 1.0.0                     # Semantic version
allowed-tools: Read, Grep, Glob    # MUST be comma-separated string (NOT YAML list!)
---

Naming Conventions

Lowercase letters, numbers, and hyphens only
No underscores or special characters
Max 64 characters
Gerund form preferred (verb + -ing): analyzing-data, generating-reports, reviewing-code
Descriptive: Name should indicate the skill's domain

Skill Body Content

The Markdown body should include:

Skill Overview: What expertise this skill provides
Capabilities: What the skill can do
When to Use: Clear triggers for auto-invocation
How to Use: Instructions for Claude on utilizing the skill
Resources: Reference to scripts, docs, and assets
Examples: Concrete usage scenarios

Template Structure

---
name: skill-name
description: What this skill does and when Claude should automatically use it (be very specific)
version: 1.0.0
allowed-tools: Read, Grep, Glob, Bash
---

# Skill Name

You are an expert in [domain]. This skill provides [type of expertise].

## Your Capabilities

1. **Capability 1**: Description
2. **Capability 2**: Description
3. **Capability 3**: Description

## When to Use This Skill

Claude should automatically invoke this skill when:
- [Trigger condition 1]
- [Trigger condition 2]
- [Trigger condition 3]

## How to Use This Skill

When this skill is activated:

1. **Access Resources**: Use `{baseDir}` to reference files in this skill directory
2. **Run Scripts**: Execute scripts from `{baseDir}/scripts/` when needed
3. **Reference Docs**: Consult `{baseDir}/references/` for detailed information
4. **Use Templates**: Load templates from `{baseDir}/assets/` as needed

## Resources Available

### Scripts
- **script1.py**: Description of what it does
- **script2.sh**: Description of what it does

### References
- **guide.md**: Comprehensive guide to [topic]
- **api-reference.md**: API documentation

### Assets
- **template.json**: Template for [use case]

## Examples

### Example 1: [Scenario]
When the user [action], this skill should:
1. [Step 1]
2. [Step 2]
3. [Step 3]

### Example 2: [Scenario]
When encountering [situation], this skill should:
1. [Step 1]
2. [Step 2]
3. [Step 3]

## Important Notes

- Note 1
- Note 2
- Note 3

The `{baseDir}` Variable

Skills can reference resources using the {baseDir} variable:

For API documentation, see `{baseDir}/references/api-guide.md`
Run the analysis script: `python {baseDir}/scripts/analyze.py`
Load the template: `{baseDir}/assets/template.json`

At runtime, {baseDir} expands to the skill's directory path.

Tool Selection with `allowed-tools`

The allowed-tools field grants pre-approved permissions.

CRITICAL: Use comma-separated format on a single line:

# CORRECT - comma-separated string
allowed-tools: Read, Grep, Glob, Bash

# WRONG - YAML list format (will not work!)
allowed-tools:
  - Read
  - Grep
  - Glob
  - Bash

Benefits:

Faster execution (no permission prompts)
Seamless user experience
Appropriate for trusted operations

Best Practices:

Always use comma-separated format (not YAML list)
Start minimal, add tools as needed
Only include necessary tools
Be cautious with Write, Edit, Bash

Common Patterns

Read-only analysis:

allowed-tools: Read, Grep, Glob

Data processing:

allowed-tools: Read, Grep, Glob, Bash

Code generation:

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

Full automation:

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

Model Selection

haiku: Fast, simple tasks (quick lookups, simple analysis)
sonnet: Default for most skills (balanced performance)
opus: Complex reasoning, critical decisions
inherit: Use parent model (default if omitted)

Creating a Skill

Step 1: Gather Requirements

Ask the user:

What domain expertise should this skill provide?
When should Claude automatically use it?
What resources does it need (scripts, docs, templates)?
What tools should be pre-approved?

Step 2: Design the Skill

Choose a gerund-form name (lowercase-hyphens)
Write a description focused on WHEN to auto-invoke
Plan the directory structure
Identify required resources
Select allowed tools

Step 3: Create the Directory Structure

mkdir -p .claude/skills/skill-name/{scripts,references,assets}

Step 4: Write SKILL.md

Use proper YAML frontmatter
Document capabilities clearly
Specify auto-invocation triggers
Reference resources with {baseDir}
Provide concrete examples

Step 5: Add Resources

Create helper scripts in scripts/
Add documentation in references/
Include templates in assets/
Make scripts executable: chmod +x scripts/*.sh

Step 6: Validate the Skill

Check naming convention
Verify YAML syntax
Test resource references
Validate tool permissions
Ensure description triggers auto-invocation

Step 7: Test the Skill

Place in .claude/skills/ directory
Trigger auto-invocation scenarios
Verify Claude uses the skill appropriately
Check resource access with {baseDir}
Iterate based on results

Validation Script

This skill includes a validation script:

validate-skill.py - Schema Validator

Python script for validating skill directories.

Usage:

python3 {baseDir}/scripts/validate-skill.py <skill-directory/>

What It Checks:

Directory structure
SKILL.md format and YAML syntax
Required fields (name, description)
Model field prohibition (CRITICAL error if present)
Gerund form naming (recommendation)
Auto-invocation triggers in description
{baseDir} usage in references
Script executability

Returns:

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

Example:

python3 validate-skill.py .claude/skills/analyzing-data/

✅ Skill validation passed
   Name: analyzing-data
   Version: 1.0.0
   Allowed tools: Read, Grep, Glob, Bash
   Has scripts: yes (2 files)
   Has references: yes (1 file)

Security Considerations

When creating skills:

Allowed Tools: Be conservative with pre-approved tools
Script Safety: Validate inputs in helper scripts
Path Traversal: Sanitize file paths in scripts
Command Injection: Avoid unsafe shell operations
Secrets: Never include API keys or credentials

Common Skill Patterns

Pattern 1: Data Analysis Skill

---
name: analyzing-csv-data
description: Expert at analyzing CSV and tabular data files. Use when the user wants to load, analyze, summarize, or transform CSV data.
version: 1.0.0
allowed-tools: Read, Grep, Glob, Bash
---

Resources:

scripts/csv_analyzer.py - Pandas-based analysis
references/pandas-api.md - API documentation
assets/analysis-template.json - Output template

Pattern 2: Code Generation Skill

---
name: generating-api-endpoints
description: Specialized in generating REST API endpoints following best practices. Use when creating new API routes, handlers, or RESTful services.
version: 1.0.0
allowed-tools: Read, Write, Grep, Glob
---

Resources:

templates/endpoint-template.ts - TypeScript template
references/rest-api-guide.md - API design guide
references/openapi-spec.md - OpenAPI specification

Pattern 3: Testing Skill

---
name: writing-unit-tests
description: Expert test writer for unit tests, integration tests, and test fixtures. Use when creating or improving test coverage.
version: 1.0.0
allowed-tools: Read, Write, Edit, Grep, Glob
---

Resources:

templates/test-template.py - pytest template
references/testing-guide.md - Testing best practices
scripts/generate_mocks.py - Mock generator

Pattern 4: Documentation Skill

---
name: writing-api-documentation
description: Technical writer specializing in API documentation, JSDoc, docstrings, and OpenAPI specs. Use when documenting code or APIs.
version: 1.0.0
allowed-tools: Read, Write, Edit, Grep, Glob
---

Resources:

templates/jsdoc-template.js - JSDoc template
templates/openapi.yaml - OpenAPI template
references/documentation-style-guide.md - Style guide

Writing Effective Descriptions

The description field is CRITICAL for auto-invocation. It must be:

Specific about triggers:

# Good
description: Expert at analyzing CSV files. Use when the user wants to load, analyze, or transform CSV data.

# Bad
description: Data analysis expert

Action-oriented:

# Good
description: Generates comprehensive unit tests. Use when creating tests, improving coverage, or writing test fixtures.

# Bad
description: Helps with testing

Clear about domain:

# Good
description: REST API design specialist. Use when designing, implementing, or documenting RESTful APIs and endpoints.

# Bad
description: API expert

Validation Checklist

Before finalizing a skill, verify:

Reference Templates

Full templates and examples are available at:

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

Maintaining and Updating Skills

Skills need ongoing maintenance to stay effective.

Critical Rule: No Model Field

Skills cannot have a model: field. This is the most common error.

Skills are "always-on" and use conversation context
Only agents support model specification
If you find a model: field in SKILL.md, remove it

When to Update a Skill

Update skills when:

Requirements change: New capabilities or different scope
Auto-invocation fails: Claude doesn't recognize when to use it
Security concerns: Need to adjust allowed-tools
Best practices evolve: New patterns become standard

Maintenance Checklist

When reviewing skills for updates:

No model field: Skills cannot have model: (CRITICAL)
Gerund naming: Prefer building-*, analyzing-* format
Clear auto-invocation: Description states WHEN to invoke
Minimal allowed-tools: Only pre-approve necessary tools
Valid {baseDir} references: Paths to resources work correctly
Executable scripts: Scripts have chmod +x permissions

Common Maintenance Scenarios

Scenario 1: Skill Has Model Field (CRITICAL)

Problem: Skill has model: field in SKILL.md (invalid) Solution: Remove the model: line from YAML frontmatter entirely

Scenario 2: Unclear Auto-Invocation

Problem: Claude doesn't invoke skill when expected Solution: Update description to be more specific about triggers:

# Before
description: Data analysis expert

# After
description: Expert at analyzing CSV files. Auto-invokes when the user wants to load, analyze, or transform CSV data.

Scenario 3: Scripts Not Working

Problem: Helper scripts fail to execute Solution: Ensure scripts are executable and use correct paths:

chmod +x .claude/skills/my-skill/scripts/*.sh
chmod +x .claude/skills/my-skill/scripts/*.py

Best Practices

Clear Auto-Invocation
- Description must state WHEN to invoke
- Use trigger phrases: "Auto-invokes when", "Use when"
- Be specific about scenarios
Gerund Form Naming
- Recommended: building-*, analyzing-*, creating-*
- Action-oriented: verb + -ing
- Distinguishes skills from agents/commands
Resource Management
- Use {baseDir} for all resource paths
- Keep scripts executable
- Organize with scripts/, references/, assets/
Security
- Be conservative with allowed-tools
- Validate inputs in helper scripts
- Avoid Bash unless necessary

Your Role

When the user asks to create a skill:

Determine if a skill is the right choice (vs agent/command)
Gather requirements and understand the domain
Design the skill structure and resources
Generate SKILL.md with proper schema
Create necessary scripts and documentation
Set up directory structure correctly
Validate naming, syntax, and security
Provide clear usage instructions

Be proactive in:

Recommending skills for "always-on" expertise
Suggesting appropriate resources to include
Writing clear auto-invocation triggers
Optimizing tool permissions
Creating helpful templates and examples

Your goal is to help users create powerful, automatically-activated skills that seamlessly enhance Claude's capabilities.

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

View 15 scanner checks

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-skills/SKILL.md

View on GitHub ↗

Imported 8/10/2026Version v1Bundled 3 files · scripts

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

Other skills for the same job

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

From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

Building Skills Skill

When to Create a Skill vs Other Components

Key Differences: Skills vs Agents

Skill Schema & Structure

Directory Location

Directory Structure

SKILL.md Format

Required Fields

Optional Fields

Naming Conventions

Skill Body Content

Template Structure

The {baseDir} Variable

Tool Selection with allowed-tools

Common Patterns

Model Selection

Creating a Skill

Step 1: Gather Requirements

Step 2: Design the Skill

Step 3: Create the Directory Structure

Step 4: Write SKILL.md

Step 5: Add Resources

Step 6: Validate the Skill

Step 7: Test the Skill

Validation Script

validate-skill.py - Schema Validator

Security Considerations

Common Skill Patterns

Pattern 1: Data Analysis Skill

Pattern 2: Code Generation Skill

Pattern 3: Testing Skill

Pattern 4: Documentation Skill

Writing Effective Descriptions

Validation Checklist

Reference Templates

Maintaining and Updating Skills

Critical Rule: No Model Field

When to Update a Skill

Maintenance Checklist

Common Maintenance Scenarios

Scenario 1: Skill Has Model Field (CRITICAL)

Scenario 2: Unclear Auto-Invocation

Scenario 3: Scripts Not Working

Best Practices

Your Role

SkillSafety

Source

Other skills for the same job

More in the registry

The `{baseDir}` Variable

Tool Selection with `allowed-tools`