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Skill Quality Check 🔍 · webkong | DecimalAI

Agent Toolingby webkong

Skill Quality Check 🔍

Quality audit for AI Agent Skills. Use before installing or after writing any SKILL.md. Scores 5 dimensions with actionable improvements. Works for skills written for Claude, Cursor, Codex, and any AI agent. Keywords: audit, skill, quality, review, score, assess, best practices, vet.

SkillScore

SkillScore74/100

Test results 74

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

22%74%of test cases passed +52 pts

Without skill5 of 23

With skill17 of 23

13 fixed4 passed either way5 still failing1 broke

Measured on gemini-3.6-flash8/6/2026v122 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat is the numerical range of score adjustments applied in Section V (Performance Impact) of the skill audit report?

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 webkong-skill-quality-check
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("webkong-skill-quality-check")
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 webkong-skill-quality-check

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/webkong-skill-quality-check/SKILL.md https://app.decimal.ai/s/webkong-skill-quality-check/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/webkong-skill-quality-check/SKILL.md

This gets SKILL.md only. The skill's 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/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/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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("webkong-skill-quality-check")
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/webkong-skill-quality-check/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/webkong-skill-quality-check/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 2 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 webkong-skill-quality-check

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/webkong-skill-quality-check/SKILL.md https://app.decimal.ai/s/webkong-skill-quality-check/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/webkong-skill-quality-check/SKILL.md

This gets SKILL.md only. The skill's 2 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/webkong-skill-quality-check/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/webkong-skill-quality-check.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
Source: webkong/skill-quality-check ↗
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

Skill Quality Check 🔍

Universal quality assessment framework for AI Agent Skills. Evaluates any SKILL.md file across 5 dimensions, outputting a quantified score and actionable improvement suggestions. Designed to work with skills built for Claude, Cursor, Codex, OpenClaw, or any AI agent.

When to Use

Before installing a new Skill from any source
After writing your own Skill (self-check)
Comparing quality of similar Skills
Evaluating Skills for ClawHub/SkillHub submission
As companion to Skill Creator — learn to write, then learn to audit

Audit Protocol

Step 1: Locate and Read the Target Skill

Find the SKILL.md file:

# Path priority (in order):
1. User-specified path
2. <skills-dir>/<skill-name>/SKILL.md

   # Common locations by platform:
   #   OpenClaw:     ~/.openclaw/skills/<skill-name>/SKILL.md
   #   QClaw:        ~/.qclaw/skills/<skill-name>/SKILL.md
   #   Claude Code:  ~/.claude/skills/<skill-name>/SKILL.md
   #   Cursor:       ~/.cursor/skills/<skill-name>/SKILL.md
   #   Codex:        ~/.codex/skills/<skill-name>/SKILL.md
3. <repo>/skills/<skill-name>/SKILL.md
4. <repo>/<skill-name>/SKILL.md

# If installing from GitHub without a local copy, fetch via curl:
curl -s "https://raw.githubusercontent.com/<owner>/<repo>/main/skills/<skill>/SKILL.md"

Then scan the directory for supporting files:

skill-name/
├── SKILL.md       ✅ required
├── scripts/       ✅ optional (lazy-loaded)
├── references/   ✅ optional (lazy-loaded)
└── assets/        ✅ optional (lazy-loaded)

Step 2: YAML Frontmatter Review

SKILL.md must have YAML frontmatter with only these fields:

---
name: <skill-name>      ✅ required
description: >          ✅ required
# Fields below are NOT recommended in frontmatter:
# ❌ version           → package metadata
# ❌ author            → non-standard
# ❌ license           → non-essential
# ❌ compatibility     → most Skills don't need it
# ❌ tags              → non-standard
---

Review checklist:

Does name and description exist?
Is description under 150 characters (trigger-level content must be concise)?
Does description include trigger keywords ("when to use")?
Are there extra fields wasting Level 1 tokens?

Step 3: Description Quality Assessment

Description is Level 1 content — the AI uses it to decide whether to trigger the Skill. It is a trigger, not a manual.

✅ Good Description:

TDD test-driven development workflow. Use when writing new features,
adding tests, or debugging. Keywords: test-driven, TDD, red-green-refactor.

❌ Bad Description:

This is a comprehensive guide to Test-Driven Development using the
red-green-refactor cycle. First, write a failing test that describes
the behavior you want. Then write the minimum code to make it pass...

(Too long — contains Level 2 content that belongs in SKILL.md body)

Scoring rubric (each dimension 0-10):

#	Dimension	Question
1	Trigger Accuracy	Does it clearly state when to use this Skill?
2	Conciseness	Under 150 chars? No explanatory filler?
3	Keyword Coverage	Does it include trigger keywords (e.g. TDD, debug, pdf)?
4	Non-Redundancy	Does it avoid restating what AI already knows?

Step 4: SKILL.md Body Quality Assessment

Five assessment dimensions (0-10 each):

4.1 Progressive Disclosure

Does it follow the three-layer loading principle?

Layer	Content	When Loaded
Level 1	name + description	Always in context
Level 2	SKILL.md body	On skill trigger
Level 3	scripts/ + references/ + assets/	On execution, never in context

Review checklist:

Trigger conditions → should be in Description (Level 1)
Execution steps, tool instructions → SKILL.md body (Level 2)
Detailed docs, scripts, templates → references/scripts (Level 3)
SKILL.md body under 500 lines?

4.2 Role Setting

Does the Skill open with a clear role or context definition?

✅ Good example:

# PDF Processing Skill

You are a professional document preparation assistant specializing in
PDF creation and editing workflows...

4.3 Examples

Are there sufficient, relevant, and diverse examples?

Claude recommends 3-5 examples that are:

Relevant: tied to real use cases
Diverse: cover edge cases
Structured: wrapped in XML tags

Review checklist:

Input/output example pairs present?
Core use cases covered?
Edge cases shown?

4.4 Instruction Clarity

Are instructions clear, actionable, and unambiguous?

Review checklist:

Steps listed with numbered lists?
Conditional branches explained?
Error/exception handling covered?
Output format specified (e.g. JSON structure)?

Step 5: Resource Layer Assessment

Are bundled resources used appropriately?

Resource	When to Use	Review Question
scripts/	Deterministic/repeated code execution	Is there repetitive code that should be a script?
references/	Detailed docs, API specs, domain knowledge	Is there >10k chars of docs not in references/?
assets/	Templates, images, fonts for output	Are there files that should be assets, not inline content?

Review checklist:

Long docs in SKILL.md body that should be in references/?
Repeated code snippets that should be scripts?
Scripts have correct paths and dependency notes?

Step 6: Performance Impact Assessment

6.1 Level 1 Token Cost

Formula:

Level 1 cost ≈ len(description) / 4 tokens
(English: ~4 chars ≈ 1 token)

Benchmarks:

Excellent: < 50 tokens
Good: 50-100 tokens
Too long: > 150 tokens → needs trimming

6.2 Level 2 Volume

Review checklist:

SKILL.md body over 500 lines (~5000 tokens)?
Repetitive content that can be trimmed?
AI-common-knowledge content that should be deleted?

6.3 Mis-trigger Risk

High-risk signals:

Multiple Skills with overlapping Description keywords
Vague Descriptions (e.g. "general-purpose assistant")
Too many installed Skills (>10) increases mis-trigger risk

Step 7: Comprehensive Scoring

Aggregate all dimension scores into the final report.

SKILL AUDIT REPORT
═══════════════════════════════════════════════════════════════
Skill: [skill-name]
Source: [local path / GitHub URL / ClawHub]
Audited: [date]
───────────────────────────────────────────────────────────────
I.   YAML FRONTMATTER COMPLIANCE       [X/10]
     ✅ [passed items]
     ❌ [issues]

II.  DESCRIPTION QUALITY               [X/40]
     Trigger Accuracy        [X/10]
     Conciseness             [X/10]
     Keyword Coverage        [X/10]
     Non-Redundancy          [X/10]

III. BODY QUALITY                      [X/40]
     Progressive Disclosure  [X/10]
     Role Setting            [X/10]
     Examples                [X/10]
     Instruction Clarity     [X/10]

IV.  RESOURCE LAYERING                 [X/10]
     scripts/ Usage           [X/5]
     references/ Usage       [X/5]

V.   PERFORMANCE IMPACT                [-5 to +2]
     Level 1 Cost            [penalty/bonus]
     Level 2 Volume          [penalty/bonus]
     Mis-trigger Risk        [penalty/bonus]
───────────────────────────────────────────────────────────────
OVERALL SCORE: X / 100
───────────────────────────────────────────────────────────────
Grade:
  🟢 Excellent (85-100)  — Worth installing, top quality
  🟡 Good (70-84)        — Usable, has room for improvement
  🔴 Acceptable (50-69) — Usable but needs optimization
  ⚫ Poor (<50)          — Not recommended
───────────────────────────────────────────────────────────────
VI.  IMPROVEMENT RECOMMENDATIONS (priority order)

  🔴 P0 (must fix):
     - [specific issue and fix]

  🟡 P1 (strongly recommended):
     - [specific issue and fix]

  🟢 P2 (optional):
     - [nice-to-have improvements]
═══════════════════════════════════════════════════════════════

Scoring Reference

Score	Grade	Meaning	Action
85-100	🟢 Excellent	Meets all best practices	Install directly
70-84	🟡 Good	Meets most standards, minor issues	Install, address P1 items
50-69	🔴 Acceptable	Functional but有明显缺陷	Fork and fix, or wait for update
<50	⚫ Poor	Fails best practices	Do not install, find alternatives

Common Issue Diagnosis

Symptom	Cause	Fix
Description too long	Frontmatter >150 tokens	Move details to body, keep only trigger keywords
Body too long	SKILL.md >500 lines	Split into references/
No examples	Text-only instructions	Add 3-5 XML-wrapped example pairs
Vague role	No clear Skill boundary	Add role-setting paragraph
AI-common-knowledge filler	Explaining what AI already knows	Delete, keep only project-specific context
Not layered	Docs in body	Move to references/
Mis-triggers	Overlapping or vague keywords	Differentiate Descriptions

Skill Quality Check vs. Skill Vetter

Use both in sequence: Vet for safety first, then audit for quality.

Quick Audit Commands

# Fetch SKILL.md from GitHub
curl -s "https://raw.githubusercontent.com/<owner>/<repo>/main/skills/<skill>/SKILL.md"

# Check frontmatter
grep -A 5 "^---" SKILL.md | head -10

# Estimate Level 2 volume (lines → ~10 tokens/line)
wc -l SKILL.md

Output Requirements

Every audit report must include:

Overall score (X/100) with grade label
Five dimension subscores (radar chart optional)
Improvement recommendations (P0/P1/P2 priority)
Clear "install or not" conclusion

Do not say "this Skill is pretty good" — deliver a specific score, specific issues, and specific fixes.

Good Skills deserve thorough auditing. Bad Skills deserve honest feedback. 🔍🦀

Examples

Example 1: Perfect Description (Score 10/10)

Input:

name: tdd-skill
description: >
  TDD test-driven development workflow. Use when writing new features,
  adding tests, or fixing bugs. Keywords: test-driven, TDD, red-green-refactor,
  pytest, unit test.

Audit Result:

Trigger Accuracy 10/10 — explicitly states when to use
Conciseness 10/10 — well under 150 chars
Keyword Coverage 10/10 — all key triggers present
Non-Redundancy 10/10 — no AI-common-knowledge filler
Description Score: 40/40

Example 2: Manual-Style Description (Score 3/10)

Input:

name: tdd-skill
description: >
  This is a comprehensive guide to Test-Driven Development using
  the red-green-refactor cycle. First, you write a failing test that
  describes the behavior you want. Then write the minimum code to make
  it pass. Then refactor while keeping tests green. This approach
  ensures high test coverage and better code quality...

Audit Result:

Trigger Accuracy 5/10 — mentions TDD but buried in explanation
Conciseness 1/10 — 280+ chars, reads like a manual
Keyword Coverage 5/10 — "TDD" present but no concise trigger list
Non-Redundancy 1/10 — explains the TDD cycle (Level 2 content in Level 1)
Description Score: 12/40

P0 Recommendation:

Rewrite Description to be under 150 chars. Move the cycle explanation to SKILL.md body.

Example 3: Good Role Setting (Score 9/10)

Input:

# PDF Processing Skill

You are a professional document preparation assistant specializing in
PDF creation, editing, and conversion workflows. You have deep knowledge
of PDF structure, reportlab, pypdf, and weasyprint.

Audit Result:

Role clarity 9/10 — clear persona and domain
Skill boundary 9/10 —明确的职责范围
Context specificity 9/10 — project-specific tools named

Minor improvement (P2): Could add one sentence about what this Skill does NOT cover (e.g. OCR, scanned PDFs).

Example 4: Poor Role Setting (Score 2/10)

Input:

# My Skill

This skill helps you get things done. Use it when you need help.
It provides instructions and guidelines for various tasks.

Audit Result:

Role clarity 2/10 — "assistant" is too generic
Skill boundary 1/10 — "various tasks" defines nothing
Context specificity 1/10 — no project-specific information

P0 Recommendation:

Replace generic language with specific domain context. Define what the Skill does and does not cover.

Example 5: Well-Layered Skill (Score 8/10)

Directory structure:

awesome-skill/
├── SKILL.md              80 lines  (Level 2: execution flow only)
├── references/
│   ├── api-spec.md       450 lines (Level 3: detailed API docs)
│   └── troubleshooting.md 120 lines (Level 3: edge cases)
└── scripts/
    └── validate.sh        (Level 3: deterministic execution)

Audit Result:

Progressive Disclosure 9/10 — clear layer separation
Body size 9/10 — 80 lines is ideal (not bloated)
Resource usage 8/10 — all heavy content in references/
Resource Layering Score: 8.5/10

Minor improvement (P2): Could add a brief Layer 1 summary in Description listing which references/ files are most relevant.

Example 6: Bloated SKILL.md (Score 2/10)

Symptom: SKILL.md has 620 lines including a 300-line API reference pasted directly in the body.

Audit Result:

Progressive Disclosure 1/10 — Level 3 content in Level 2
Body size 1/10 — 620 lines far exceeds 500-line guideline
Conciseness 1/10 — 300-line API doc belongs in references/

P0 Recommendation:

Move the API reference to references/api-spec.md. SKILL.md body should be execution flow only (under 500 lines).

Example 7: Mis-Trigger Risk (Score -3 Performance Impact)

Scenario: User has 12 Skills installed. Two of them have "debug" in their Description:

Skill	Description trigger keyword
systematic-debugging	"debugging, error, bug"
general-helper	"debug, logs, errors, general assistance"

Audit Result:

Mis-trigger Risk: -3 penalty
The overlap means "debug" alone can't reliably select the right Skill

P1 Recommendation:

Differentiate: systematic-debugging should use "systematic-debugging, root-cause" (more specific); general-helper should remove "debug" entirely or move it lower in priority.

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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

webkong / skill-quality-check

SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 2 files

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

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SkillScore

Safety

What it changes

Track record

Use this skill

Skill Quality Check 🔍

When to Use

Audit Protocol

Step 1: Locate and Read the Target Skill

Step 2: YAML Frontmatter Review

Step 3: Description Quality Assessment

Step 4: SKILL.md Body Quality Assessment

4.1 Progressive Disclosure

4.2 Role Setting

4.3 Examples

4.4 Instruction Clarity

Step 5: Resource Layer Assessment

Step 6: Performance Impact Assessment

6.1 Level 1 Token Cost

6.2 Level 2 Volume

6.3 Mis-trigger Risk

Step 7: Comprehensive Scoring

Scoring Reference

Common Issue Diagnosis

Skill Quality Check vs. Skill Vetter

Quick Audit Commands

Output Requirements

Examples

Example 1: Perfect Description (Score 10/10)

Example 2: Manual-Style Description (Score 3/10)

Example 3: Good Role Setting (Score 9/10)

Example 4: Poor Role Setting (Score 2/10)

Example 5: Well-Layered Skill (Score 8/10)

Example 6: Bloated SKILL.md (Score 2/10)

Example 7: Mis-Trigger Risk (Score -3 Performance Impact)

SkillSafety

Source

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