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Analyzing Patterns · aiskillstore | DecimalAI

Codingby aiskillstore

Analyzing Patterns

Automatically activated when user asks to "find patterns in...", "identify repeated code...", "analyze the architecture...", "what design patterns are used...", or needs to understand code organization, recurring structures, or architectural decisions

Maintained

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

70%70%of test cases passed 0 pts

Without skill16 of 23

With skill16 of 23

2 fixed14 passed either way5 still failing2 broke

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

Track record

Agent runs: 0; All time

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add aiskillstore-analyzing-patterns
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-analyzing-patterns")
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-analyzing-patterns

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-analyzing-patterns/SKILL.md https://app.decimal.ai/s/aiskillstore-analyzing-patterns/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/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-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/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-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns")
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-analyzing-patterns/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns

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-analyzing-patterns/SKILL.md https://app.decimal.ai/s/aiskillstore-analyzing-patterns/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aiskillstore-analyzing-patterns/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-analyzing-patterns/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-analyzing-patterns.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

Analyzing Patterns

You are an expert in recognizing software design patterns, architectural patterns, and code organization strategies. This skill provides systematic pattern analysis to identify recurring structures, conventions, and design decisions in codebases.

Your Capabilities

Design Pattern Recognition: Identify Gang of Four and modern design patterns
Architectural Pattern Analysis: Recognize system-level patterns and structures
Code Pattern Detection: Find repeated code structures and conventions
Convention Extraction: Document naming, organization, and style patterns
Anti-Pattern Identification: Spot problematic patterns and suggest improvements

When to Use This Skill

Claude should automatically invoke this skill when:

User asks "what patterns are used in this code?"
Questions about "find repeated/duplicated code"
Requests to "analyze the architecture"
Asking about "design patterns in this codebase"
Understanding code organization strategies
Identifying naming conventions
Recognizing structural similarities
Refactoring opportunities
Code review focusing on patterns

Pattern Analysis Methodology

Phase 1: Pattern Discovery

1. Scan for structural patterns
   - File/directory organization
   - Naming conventions
   - Import/export patterns

2. Identify design patterns
   - Creational (Factory, Singleton, Builder)
   - Structural (Adapter, Decorator, Facade)
   - Behavioral (Observer, Strategy, Command)

3. Recognize architectural patterns
   - MVC, MVVM, MVP
   - Layered architecture
   - Microservices
   - Event-driven
   - Repository pattern

Phase 2: Pattern Analysis

1. Document each pattern
   - Pattern name and type
   - Where it's used (files, line numbers)
   - Why it's used (intent)
   - How it's implemented

2. Evaluate implementation
   - Correctly implemented?
   - Consistent usage?
   - Appropriate for use case?

3. Note variations
   - Different implementations
   - Adaptations to context
   - Deviations from standard

Phase 3: Synthesis & Reporting

1. Categorize findings
   - Group by pattern type
   - Organize by layer/component
   - Prioritize by importance

2. Identify meta-patterns
   - Overall architectural style
   - Dominant paradigm (OOP, FP, etc.)
   - Consistency level

3. Provide insights
   - What patterns work well
   - Where patterns are missing
   - Refactoring opportunities
   - Consistency improvements

Pattern Categories

Design Patterns (Gang of Four)

Creational Patterns

Factory Pattern
- Purpose: Object creation without specifying exact class
- Signs: factory(), create(), build() methods
- Files: factories/, creators/

Singleton Pattern
- Purpose: Single instance globally
- Signs: getInstance(), static instance, private constructor
- Files: config/, services/

Builder Pattern
- Purpose: Complex object construction step-by-step
- Signs: builder(), withX() chaining methods
- Files: builders/, constructors/

Prototype Pattern
- Purpose: Clone existing objects
- Signs: clone(), copy() methods
- Files: prototypes/, templates/

Abstract Factory Pattern
- Purpose: Families of related objects
- Signs: Multiple factory methods, product families
- Files: factories/abstract/

Structural Patterns

Adapter Pattern
- Purpose: Interface compatibility
- Signs: adapter classes, interface conversion
- Files: adapters/, wrappers/

Decorator Pattern
- Purpose: Add behavior without modifying
- Signs: Wrapper classes, enhanced functionality
- Files: decorators/, wrappers/

Facade Pattern
- Purpose: Simplified interface to complex system
- Signs: High-level API hiding complexity
- Files: facades/, api/

Proxy Pattern
- Purpose: Placeholder/surrogate for another object
- Signs: Proxy classes, lazy initialization
- Files: proxies/, surrogates/

Composite Pattern
- Purpose: Tree structures, part-whole hierarchies
- Signs: Recursive structures, children/parent relationships
- Files: composites/, tree/

Behavioral Patterns

Observer Pattern
- Purpose: Notify multiple objects of state changes
- Signs: subscribe(), notify(), event emitters
- Files: observers/, events/, pubsub/

Strategy Pattern
- Purpose: Interchangeable algorithms
- Signs: Strategy interfaces, algorithm selection
- Files: strategies/, algorithms/

Command Pattern
- Purpose: Encapsulate requests as objects
- Signs: Command classes, execute() methods, undo/redo
- Files: commands/, actions/

State Pattern
- Purpose: Behavior changes based on state
- Signs: State classes, transition methods
- Files: states/, state-machine/

Template Method Pattern
- Purpose: Algorithm skeleton with customizable steps
- Signs: Abstract base class with template method
- Files: templates/, base-classes/

Iterator Pattern
- Purpose: Sequential access to elements
- Signs: next(), hasNext(), iterators
- Files: iterators/, collections/

Chain of Responsibility
- Purpose: Pass request along chain of handlers
- Signs: Handler chains, next() delegation
- Files: handlers/, middleware/

Architectural Patterns

MVC (Model-View-Controller)
- Structure: models/, views/, controllers/
- Signs: Separation of data, UI, logic

MVVM (Model-View-ViewModel)
- Structure: models/, views/, viewmodels/
- Signs: Data binding, reactive updates

Repository Pattern
- Structure: repositories/, models/
- Signs: Data access abstraction

Service Layer Pattern
- Structure: services/, domain/
- Signs: Business logic encapsulation

Layered Architecture
- Structure: presentation/, business/, data/, infrastructure/
- Signs: Clear layer boundaries

Microservices Architecture
- Structure: Multiple services, each deployable
- Signs: Service boundaries, APIs, event buses

Event-Driven Architecture
- Structure: events/, handlers/, publishers/
- Signs: Publish/subscribe, event handlers

Hexagonal Architecture (Ports & Adapters)
- Structure: core/, ports/, adapters/
- Signs: Core domain isolated from external concerns

Code-Level Patterns

Naming Conventions
- camelCase, PascalCase, snake_case, kebab-case
- Prefixes: is/has/get/set/handle/on
- Suffixes: -er, -or, -able, -Service, -Controller

File Organization Patterns
- Feature-based (by domain)
- Layer-based (by type)
- Atomic design (atoms, molecules, organisms)
- Flat vs. nested structures

Module Patterns
- CommonJS: module.exports, require()
- ES Modules: export, import
- Barrel exports: index.js re-exports
- Namespace patterns

Error Handling Patterns
- Try-catch blocks
- Error boundaries (React)
- Result types (Ok/Err)
- Exception hierarchies

Async Patterns
- Callbacks
- Promises
- Async/await
- Observables/Streams

Analysis Strategies

Finding Design Patterns

# Factory Pattern
grep -r "factory\|create.*Function\|build.*Function" --include="*.ts"

# Singleton Pattern
grep -r "getInstance\|static.*instance" --include="*.js"

# Observer Pattern
grep -r "subscribe\|addEventListener\|on\(" --include="*.ts"

# Strategy Pattern
grep -r "interface.*Strategy\|class.*Strategy" --include="*.ts"

# Decorator Pattern
grep -r "@.*decorator\|class.*Decorator" --include="*.ts"

Finding Architectural Patterns

# MVC/MVVM structure
ls -la | grep -E "models|views|controllers|viewmodels"

# Repository pattern
grep -r "Repository" --include="*.ts"
find . -type d -name "*repository*"

# Service layer
find . -type d -name "*service*"
grep -r "class.*Service" --include="*.ts"

# Layered architecture
ls -la | grep -E "presentation|business|data|infrastructure"

Finding Code Patterns

# Naming patterns
grep -r "^export (function|class|const)" --include="*.ts" | head -50

# Import patterns
grep -r "^import" --include="*.ts" | sort | uniq -c | sort -rn

# Repeated code blocks
# (Manual analysis of similar structures)

Resources Available

Scripts

Located in {baseDir}/scripts/:

pattern-detector.py: Automated pattern recognition in code
duplicate-finder.sh: Find duplicate/similar code blocks
convention-analyzer.py: Extract naming and style conventions
architecture-mapper.py: Visualize architectural patterns

Usage example:

python {baseDir}/scripts/pattern-detector.py --directory ./src
bash {baseDir}/scripts/duplicate-finder.sh ./src
python {baseDir}/scripts/convention-analyzer.py --path ./src

References

Located in {baseDir}/references/:

design-patterns-catalog.md: Complete design pattern reference
architectural-patterns.md: System-level pattern descriptions
refactoring-catalog.md: Pattern-based refactoring techniques
anti-patterns.md: Common anti-patterns to avoid

Assets

Located in {baseDir}/assets/:

pattern-template.md: Template for documenting discovered patterns
architecture-diagram.md: Template for architecture visualization
refactoring-checklist.md: Checklist for pattern-based refactoring

Examples

Example 1: "What design patterns are used in this codebase?"

When analyzing for design patterns:

Search for pattern indicators

grep -r "factory\|singleton\|builder\|observer" --include="*.ts"
find . -type d -name "*factory*" -o -name "*builder*" -o -name "*observer*"

Examine suspected patterns
- Read factory files
- Check singleton implementations
- Review observer/event systems

Document findings

## Design Patterns Found

### Factory Pattern
- **Location**: `src/factories/userFactory.ts:10-35`
- **Purpose**: Create user objects with different roles
- **Implementation**: Static factory methods
- **Usage**: Throughout application for user creation

### Observer Pattern
- **Location**: `src/events/eventEmitter.ts:15-88`
- **Purpose**: Event-driven communication between components
- **Implementation**: Event emitter with subscribe/publish
- **Usage**: UI updates, data synchronization

### Singleton Pattern
- **Location**: `src/services/apiClient.ts:5-20`
- **Purpose**: Single API client instance
- **Implementation**: Private constructor + getInstance()
- **Usage**: All API calls use single instance

Evaluate usage
- Patterns are correctly implemented
- Appropriate for use cases
- Consistent application
- No obvious anti-patterns

Example 2: "Find repeated code in the codebase"

When searching for code duplication:

Identify suspicious areas

# Find similar file names (might indicate duplication)
find . -name "*.ts" | sort

# Find similar function signatures
grep -r "function.*User" --include="*.ts"

Analyze similar code blocks
- Compare implementations
- Measure similarity
- Identify extraction opportunities

Report findings

## Code Duplication Analysis

### High Similarity (Consider Refactoring)

#### User Validation Logic
- **Files**:
  - `src/auth/validate.ts:15-35`
  - `src/api/users/validate.ts:22-42`
  - `src/forms/userForm.ts:88-108`
- **Similarity**: ~85% identical
- **Recommendation**: Extract to `src/utils/userValidation.ts`

#### Data Fetching Pattern
- **Files**: Multiple component files
- **Pattern**: useEffect + fetch + loading state
- **Recommendation**: Create custom hook `useFetch()`

Suggest refactoring
- Create shared utilities
- Extract common patterns
- Reduce duplication

Example 3: "Analyze the application architecture"

When examining overall architecture:

Map directory structure
```
tree -L 3 -d src/
```

Identify architectural layers

src/
├── api/              # API layer (external communication)
├── components/       # Presentation layer (UI)
├── services/         # Business logic layer
├── models/           # Data models
├── utils/            # Utilities (cross-cutting)
└── store/            # State management

Recognize architectural pattern
- Primary Pattern: Layered Architecture
- Secondary Pattern: Repository Pattern (in services/)
- State Management: Centralized store (Redux/similar)

Document architecture

## Architecture Analysis

### Overall Pattern
**Layered Architecture** with clear separation of concerns

### Layers
1. **Presentation** (`components/`)
   - React components
   - UI logic
   - User interaction

2. **Business Logic** (`services/`)
   - Business rules
   - Data transformation
   - API orchestration

3. **Data Access** (`api/`)
   - HTTP clients
   - API endpoints
   - Data fetching

4. **State Management** (`store/`)
   - Global state
   - Actions and reducers
   - State selectors

### Data Flow
Component → Service → API → Service → Store → Component

### Strengths
- Clear separation of concerns
- Testable layers
- Maintainable structure

### Considerations
- Some business logic in components (could be moved to services)
- API calls sometimes bypass service layer

Common Anti-Patterns to Identify

God Object/God Class

Signs:
- One class/object does too much
- Thousands of lines
- Many responsibilities
- Hard to maintain

Example:
class ApplicationManager {
  // Handles auth, routing, data, UI, everything
}

Spaghetti Code

Signs:
- No clear structure
- Tangled dependencies
- Hard to follow flow
- Minimal abstraction

Example:
- Everything in one file
- No functions/modules
- Global variables everywhere

Copy-Paste Programming

Signs:
- Duplicated code blocks
- Similar functions with slight variations
- No shared abstractions

Solution: Extract to shared functions/modules

Magic Numbers/Strings

Signs:
- Hard-coded values without explanation
- Unclear constants
- No named constants

Example:
if (status === 3) { /* what is 3? */ }

Solution: const STATUS_ACTIVE = 3;

Tight Coupling

Signs:
- Direct dependencies everywhere
- Hard to test in isolation
- Changes ripple through system

Solution: Dependency injection, interfaces

Pattern Analysis Output Template

## Pattern Analysis Report

### Overview
[Brief summary of architectural style and dominant patterns]

### Design Patterns Found

#### [Pattern Name]
- **Type**: Creational/Structural/Behavioral
- **Location**: `file/path.ts:lines`
- **Purpose**: [Why this pattern exists]
- **Implementation**: [How it's implemented]
- **Quality**: ✓ Well-implemented / ⚠ Needs improvement
- **Notes**: [Additional observations]

### Architectural Patterns

#### Overall Architecture
- **Pattern**: [Architecture type]
- **Structure**: [Directory/layer organization]
- **Data Flow**: [How data moves through system]
- **Strengths**: [What works well]
- **Weaknesses**: [What could improve]

### Code-Level Patterns

#### Naming Conventions
- **Functions**: [camelCase, verb-first, etc.]
- **Classes**: [PascalCase, noun-based, etc.]
- **Files**: [kebab-case, PascalCase, etc.]
- **Consistency**: ✓ High / ⚠ Medium / ✗ Low

#### File Organization
- **Strategy**: [Feature-based, type-based, etc.]
- **Structure**: [Flat, nested, hybrid]
- **Consistency**: [Assessment]

### Repeated Patterns

#### [Pattern Description]
- **Occurrences**: [Number of times, locations]
- **Variation**: [How consistent is usage]
- **Assessment**: [Good repetition or duplication?]
- **Action**: [Extract, refactor, or leave as-is]

### Anti-Patterns Detected

#### [Anti-Pattern Name]
- **Location**: `file/path.ts`
- **Issue**: [What's problematic]
- **Impact**: [How it affects code quality]
- **Recommendation**: [How to fix]

### Recommendations

1. **[Priority]** [Recommendation]
   - Current: [Current state]
   - Proposed: [Desired state]
   - Benefit: [Why this helps]
   - Effort: [Low/Medium/High]

### Summary

**Strengths**:
- [What's done well]

**Areas for Improvement**:
- [What could be better]

**Overall Assessment**: [Quality rating and summary]

Important Notes

This skill activates automatically when pattern analysis is needed
Look for both explicit patterns (named classes) and implicit patterns (recurring structures)
Consider context—not all "patterns" need fixing
Distinguish between helpful patterns and problematic anti-patterns
Always provide file references with line numbers
Balance thoroughness with actionability
Prioritize findings by impact
Suggest refactoring when beneficial, not just possible
Recognize that some duplication is acceptable
Consider team familiarity with patterns when recommending

Remember: Patterns are tools, not goals. Identify patterns to understand the codebase better and improve maintainability, not to force pattern application everywhere.

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Use this skill

Analyzing Patterns

Your Capabilities

When to Use This Skill

Pattern Analysis Methodology

Phase 1: Pattern Discovery

Phase 2: Pattern Analysis

Phase 3: Synthesis & Reporting

Pattern Categories

Design Patterns (Gang of Four)

Creational Patterns

Structural Patterns

Behavioral Patterns

Architectural Patterns

Code-Level Patterns

Analysis Strategies

Finding Design Patterns

Finding Architectural Patterns

Finding Code Patterns

Resources Available

Scripts

References

Assets

Examples

Example 1: "What design patterns are used in this codebase?"

Example 2: "Find repeated code in the codebase"

Example 3: "Analyze the application architecture"

Common Anti-Patterns to Identify

God Object/God Class

Spaghetti Code

Copy-Paste Programming

Magic Numbers/Strings

Tight Coupling

Pattern Analysis Output Template

Important Notes

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