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clean-code-guard · sickn33 | DecimalAI

Code Reviewby sickn33

clean-code-guard

Review generated or changed production code with Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks.

Maintained

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

64%73%of test cases passed +9 pts

Without skill14 of 22

With skill16 of 22

4 fixed12 passed either way4 still failing2 broke

Measured on gemini-3.6-flash8/8/2026v1 · latest is v2

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskThe user explicitly insists on keeping an 8-parameter constructor on a configuration class `DatabaseConfig`. Write the override comment in the source code.

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 sickn33-clean-code-guard
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("sickn33-clean-code-guard")
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 sickn33-clean-code-guard

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/sickn33-clean-code-guard/SKILL.md https://app.decimal.ai/s/sickn33-clean-code-guard/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sickn33-clean-code-guard/SKILL.md

This gets SKILL.md only. The skill's 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/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/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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("sickn33-clean-code-guard")
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/sickn33-clean-code-guard/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sickn33-clean-code-guard/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 7 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 sickn33-clean-code-guard

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/sickn33-clean-code-guard/SKILL.md https://app.decimal.ai/s/sickn33-clean-code-guard/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sickn33-clean-code-guard/SKILL.md

This gets SKILL.md only. The skill's 7 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/sickn33-clean-code-guard/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sickn33-clean-code-guard.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/8/2026)
Source: sickn33/antigravity-awesome-skills ↗
License: MIT

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

clean-code-guard

You are reviewing generated or changed code before it ships. Apply the rules below as a guard pass after the first implementation pass — and once this skill is active, keep applying it to every later code change in the same session, re-running the self-check before delivery after each edit rather than reverting to unguarded output because the skill loaded earlier. If the user explicitly invokes this skill before writing code, use the same rules while writing and still run the self-check before delivery.

When to Use

Use this skill when reviewing generated or changed code before it ships. Activate it reactively after an agent writes, edits, or refactors production code — especially after a first implementation pass. Re-run the guard pass before delivery after each edit.

Compatibility

This is a portable instruction skill. It requires no MCP server, network access, API key, shell command, local executable, or bundled script. It can be used in any runtime that supports SKILL.md plus directly linked references/ files. This bundle contains no runtime helper or host metadata file.

This skill does not replace project linters, formatters, type checkers, or test runners. Use the project's own tools for mechanical verification; use this skill for the judgement layer around code quality and review.

How to use this skill

This skill has three modes — pick based on the user's request.

Guard-pass mode (recommended): after code has been generated, edited, refactored, or fixed, check the diff or target files against the Always-applied imperatives below. Fix violations before presenting, committing, or merging the work.

Live mode (explicit): when the user invokes this skill before a risky code edit, apply the same imperatives while writing, then run the Self-check before delivery checklist. If you violate any rule, fix it before showing the user.

Review mode (triggered when the user asks you to review, audit, critique, or rate code): walk references/review-checklist.md against the target file(s) and produce a structured findings report. Do not edit code in review mode unless asked.

Across all three modes, the rule bodies live in references/. Read the relevant reference file when:

You hit a rule you don't fully remember the reasoning for.
The user pushes back on a rule and you need the source citation.
You're in review mode and need the full checklist.
The code under review touches a specific principle (e.g., subclassing → references/solid.md; deduplication → references/dry-kiss-yagni.md).

The reference files are:

references/naming-and-functions.md — names, function size, parameters, command/query separation.
references/comments-and-formatting.md — when to comment, when to delete, matching neighbor style.
references/solid.md — SRP, OCP, LSP, ISP, DIP with the modern phrasings and detection smells.
references/dry-kiss-yagni.md — knowledge vs code duplication, Sandi Metz's re-inline rule, McCabe complexity, Fowler's YAGNI cost categories.
references/ai-failure-modes.md — the 15 systematic ways LLMs produce bad code. Read this one first if you are an AI agent reading this skill. It is the highest-leverage file in the skill.
references/review-checklist.md — structured walk-through for review mode.
references/sources.md — central bibliography for source URLs. Read it only when you need to verify or cite an external source.

Examples

A coding agent implements an endpoint: use guard-pass mode on the diff before the work is presented or committed.
User asks "review this PR" or "should I merge this?": use review mode and report findings from references/review-checklist.md; do not edit unless asked.
User asks "implement this endpoint using clean-code-guard": use live mode while writing, then run the self-check before delivery.
User asks "refactor this function, same behavior": preserve observable behavior exactly and treat any bug fix as a separate change.

Success criteria

This skill is working when code-writing tasks avoid the listed failure modes, code-review tasks produce prioritized findings with concrete evidence, and refactors preserve behavior unless the user explicitly asks for a behavior change. It should stay silent for conceptual, CI, git workflow, prose, data analysis, and test-running tasks covered by the scope exclusions below.

Why this skill exists

LLM-generated code has measurable, systematic failure modes that generic "follow clean code" instructions do not catch. Examples backed by published research:

GitClear reports increased copy/paste and cloning in its observational code-change dataset; this does not establish that AI caused each change.
Spracklen et al. measured package hallucinations across 16 models; the paper reports at least 5.2% for commercial and 21.7% for open-source models in its tested setting.
LLMs often wrap risky operations in broad catch-all handlers that swallow errors (Karpathy).
AI agents "declare success despite failing tests" by returning hardcoded fixture values (Fowler, Patterns for Reducing Friction).
Do not infer a particular function-size or complexity increase without checking the underlying metric and study population.

The classic principles (Clean Code, SOLID, DRY/KISS/YAGNI) are still the foundation — but this skill adds the AI-specific layer most rule packs miss.

Always-applied imperatives

These are the rules to follow on every code change. They are imperative, not suggestions.

Functions and names

Names reveal intent. Never use data, data2, result, result_final, item, temp, value, obj, info, helper, manager, utils, or handle_*/process_*/do_* without a qualifier. A name must answer why it exists and what it does. (Clean Code Ch. 2)
Functions stay small. Target ≤20 lines, one level of abstraction, one thing. If you can extract a function with a name that doesn't restate the body, the parent was doing more than one thing. (Clean Code Ch. 3)
Four arguments is the hard ceiling. At five, stop and introduce a request/config object (record, struct, DTO, or equivalent). Never use boolean flag arguments — split into two functions instead.
No output arguments. A function either returns a value (query) or has a side effect (command). Never both. Command names use verbs; query names use nouns or getter-style names. (CQS)

Comments and structure

Comments explain why, never what. Delete any comment that paraphrases the line below it. Delete step-number scaffolding comments. Delete commented-out code — version control exists. (Clean Code Ch. 4)
Match the file's existing style. Read the file you're editing and at least one neighbor before writing. Mirror the casing, import order, error handling, logging, and HTTP/DB client choices. Do not introduce a second pattern.

SOLID

One actor per module. A class should be answerable to one stakeholder group (Accounting, Auth, Reporting). If two unrelated subsystems both reach into the same class, split it. (SRP, Uncle Bob 2014)
Extension via new code, not edits. If adding a new variant requires another type-tag branch in an existing function, refactor to a registry, strategy, or polymorphic dispatch first. (OCP)
No subclass refuses its parent's contract. Never override a method to signal "not implemented" or "unsupported operation." Never strengthen preconditions or weaken postconditions in an override. If you need to do that, the inheritance is wrong. (LSP)
Abstractions live with the client, not the implementation. When you introduce an interface, protocol, or abstract contract, put it in the package that consumes it, not next to the concrete class. (DIP)

DRY, KISS, YAGNI

Delete duplicated knowledge, not duplicated text. Two functions that look alike but encode different rules are not a DRY violation. One rule expressed in code + docs + schema is. (Pragmatic Programmer, "DRY")
The wrong abstraction is worse than duplication. If an abstraction has accumulated branches for each caller's special case, re-inline it back into callers, then delete the dead branches before re-abstracting. (Sandi Metz, "The Wrong Abstraction")
Complexity ceiling: cyclomatic ≤10, nesting depth ≤5. Refactor before exceeding. (McCabe 1976)
No speculative anything. No optional parameter, config flag, env var, feature toggle, interface, factory, or base class without a present-day caller. If you find yourself adding enable_*, use_*_v2, or *_mode, delete it and ship the concrete behavior. (Fowler, "Yagni")

AI-specific guardrails — the highest-leverage section

Never swallow errors with broad catch-all handling. Catch only the specific error type you can recover from. If you cannot recover, let the error propagate. Returning null/none/empty success from a catch handler is forbidden unless the function contract documents that behavior. (Karpathy)
Guard the boundary; trust the contract. At a trust boundary — external input, request/API payloads, deserialized or cross-process data, anything from an untrusted source — validate, even when the happy path looks fine. Inside the boundary, do not add null checks or runtime type checks for values whose declared type or caller contract already excludes that case. The test for a guard is not "could this theoretically be wrong" but "can untrusted data reach here." (arXiv 2409.19182)
Verify every import and external call. Before calling a method on a library, confirm it exists in the version installed (read the package, check the lockfile, or import and inspect). Do not generate code based on what the API "should" look like. (USENIX Security '25)
No hardcoded "success" returns or mock fixtures in production code. Never return {"status": "ok", ...} or canned data from a function whose spec says it does real work. If you cannot implement, fail explicitly with the language's unimplemented or unsupported-operation mechanism and say so. Never disable, skip, or weaken a test to make it pass. (Fowler, Claude Code issue #6984)
Re-derive, do not copy from similar. When tempted to copy a function and modify it, stop. Re-derive from the spec. Off-by-one and wrong-null-semantic bugs almost always enter through copy-from-similar. (arXiv 2411.01414)
Enumerate boundary cases before writing them. For any range, off-by-one, null/empty/one/many, even/odd, or unicode/byte boundary, write the case list in a comment first. Cover each case in code before moving on.
Strip dead code before delivery. Run a linter or grep pass for unused imports, unused symbols, unreachable branches, and "just in case" exports. Remove them. A function that nothing calls today does not get to live for "someday."
Read before write. Before writing in an unfamiliar repo, read the file you'll edit, one neighbor, and any project rules file (CLAUDE.md, AGENTS.md, README's "conventions" section). Use the project's existing helpers, error types, and logging.
No new dependency for what a few lines cover. Before adding a package, check the standard library, the already-installed dependencies, and whether a few lines of local code do the job. A new dependency is permanent maintenance and supply-chain surface; add one only when it owns real complexity you should not re-implement (cryptography, parsing, time zones — illustrative, not exhaustive), never to save ten lines. See references/dry-kiss-yagni.md.

The floor — never cut these for simplicity

Rule 16 trusts the contract inside the boundary; the items below stay even while you strip speculation (14), defensive guards (16), and dead code (21). Removing one of these is a behavior change, not a cleanup — keep it, or flag it and ask.

Validation and sanitization at every trust boundary — external input, request/API payloads, deserialized or cross-process data.
Error handling that prevents data loss.
Security measures — authorization, output escaping, parameterized queries, secret handling.
Behavior the user explicitly requested. Idly mentioned ≠ requested, but do not drop what was asked for.

Refactoring discipline

Preserve observable behavior when refactoring. When the user asks you to clean up, simplify, or refactor existing code, do not change the contract — same inputs produce the same outputs, same exceptions raised, same side effects, same ordering guarantees. If you spot a bug while refactoring, flag it separately and ask before changing it. Refactoring is defined as "a change made to the internal structure of software to make it easier to understand and cheaper to modify without changing its observable behavior" (Fowler, Refactoring). Bug fixes and refactors are two operations — never bundle them in a single change.

Self-check before delivery

Before you show the user the code you wrote or edited:

Walk imperatives 1–24 against your diff. Fix every violation.
For new functions, count: lines ≤ 20? params ≤ 4? complexity feels ≤ 10? names reveal intent?
For new comments, ask: does this explain why? If it explains what, delete it.
For new error handling: is the caught error type specific? Does the handler do something other than silently return?
For new abstractions (interface, factory, base class, registry): is there a second concrete user today? If no, inline it.
Did you read the file you edited and at least one neighbor? Did your style match?
Is there any hardcoded "ok" return or fixture data? If yes, replace with real implementation or an explicit unimplemented/unsupported-operation failure.
If this is a refactor: did you change observable behavior? If yes, you bundled a bug fix — split it out and ask the user.

If you cannot answer yes to every check, fix before shipping.

After the guard pass, surface it so the user can see it ran (guard-pass and live modes — review mode reports through its own findings format). List each fix as <file>[:<line>] — <what changed>, omitting the line number if it is unstable, then close with one line: clean-code-guard: <N> fixed, <M> flagged for author — or clean-code-guard: clean if nothing triggered. Report only changes you actually made; never estimate a quality score or percentage — no baseline exists, so such a number would be invented. This reports the pass; it does not block presenting or committing.

When the user pushes back on a rule

Refer them to the source name in the relevant references/ file and use references/sources.md only when the URL is needed. The rules are defensible — they come from primary sources (Uncle Bob, Fowler, Hunt & Thomas, McCabe, Metz) and from published 2024–2026 research on LLM code generation. If the user has a context-specific reason to override (e.g., a constructor genuinely needs 8 params for a config DTO), document the exception in a code comment that names the principle being overridden, the reason, and a revisit trigger — the condition under which it should be reconsidered. An exception comment with no revisit trigger is itself a finding on the next pass: a tradeoff with no exit is just deferred debt.

Troubleshooting

If the task is conceptual rather than code-producing, do not apply this skill; answer the concept directly.
If review mode starts producing style-only feedback, use references/review-checklist.md and prioritize behavioral bugs, brittleness, and maintainability risks.
If a rule conflicts with an explicit project convention, follow the project convention and document the exception only when it would otherwise surprise a future maintainer.
If the skill feels too broad, use the scope exclusions below first; do not add runtime-specific rules to this general guard skill.

What this skill does not do

Run linters or static analysis. Those are tool-level concerns; this skill is about what to write and what to look for.
Enforce language-specific formatter or linter preferences. Defer to the project's style tooling.
Replace tests. Passing tests does not prove maintainability, and maintainable-looking code can still be incorrect.

Limitations

These numeric cutoffs and style imperatives are review heuristics, not universal correctness criteria or research-established safety limits. Respect project contracts and documented API conventions. An uncalled exported entry point may be used externally; verify consumers before deleting it. Preserve license and provenance comments. Do not execute an untrusted package merely to inspect its API; prefer static source and versioned documentation.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/8/2026

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

sickn33 / antigravity-awesome-skills

skills/clean-code-guard/SKILL.md

View on GitHub ↗

License MITImported 11/8/2026Version v2Bundled 7 files

Illustrative Want to see what sickn33/clean-code-guard 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.

amelnagdy/clean-code-guardReview generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can aNear-identical description+32 pts pass-rate lift

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SkillScore

Safety

What it changes

Track record

Use this skill

clean-code-guard

When to Use

Compatibility

How to use this skill

Examples

Success criteria

Why this skill exists

Always-applied imperatives

Functions and names

Comments and structure

SOLID

DRY, KISS, YAGNI

AI-specific guardrails — the highest-leverage section

The floor — never cut these for simplicity

Refactoring discipline

Self-check before delivery

When the user pushes back on a rule

Troubleshooting

What this skill does not do

Limitations

SkillSafety

Source

Other skills for the same job

Related skills

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