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skill-checker · wtfitsme-design | DecimalAI

Agent Toolingby wtfitsme-design

skill-checker

Reviews a SKILL.md file and evaluates whether it is well-constructed, using best practices distilled from real-world skill analysis.

Never made a case worse

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

59%91%of test cases passed +32 pts

Without skill13 of 22

With skill20 of 22

7 fixed13 passed either way2 still failing0 broke

Measured on gemini-3.6-flash8/4/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task对纯文本分析类无外部依赖技能执行 P0-3 专项评估判定： ```markdown name: prose-critique description: Critiques prose style. Triggers: 'review my prose'. NOT for proofreading code. ## Workflow Analyze rhetoric and structural clarity. Uses no tools, APIs, or external data. ```

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 wtfitsme-design-skill-checker
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("wtfitsme-design-skill-checker")
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 wtfitsme-design-skill-checker

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/wtfitsme-design-skill-checker/SKILL.md https://app.decimal.ai/s/wtfitsme-design-skill-checker/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/wtfitsme-design-skill-checker/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/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/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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("wtfitsme-design-skill-checker")
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/wtfitsme-design-skill-checker/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/wtfitsme-design-skill-checker/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 1 other file is 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 wtfitsme-design-skill-checker

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/wtfitsme-design-skill-checker/SKILL.md https://app.decimal.ai/s/wtfitsme-design-skill-checker/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/wtfitsme-design-skill-checker/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/wtfitsme-design-skill-checker/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/wtfitsme-design-skill-checker.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/4/2026
Source: wtfitsme-design/skill-checker ↗
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-checker

根据对7个真实开源 skill 案例的纵横深挖，提炼出的检查标准，评估一个 skill 的构建质量。

When to Use

用户分享了 SKILL.md 内容，请求检查/review/验证
用户问"这个 skill 写得好吗""有没有问题"
用户完成 skill 构建，想发布前做最后把关

When NOT to Use

用户想创建新 skill（换用专门的创建类 skill）
用户想执行某个 skill 的功能
用户只是问 skill 的概念问题

Workflow

Step 1 · 获取待检查的 skill

如果用户没有直接提供 SKILL.md 内容，询问：

"请把你的 SKILL.md 内容粘贴过来，或者告诉我文件路径。"

如果给了路径，用 Read 工具读取。

Step 2 · 确认检查模式

默认：完整模式（P0 + P1 + P2 全面检查，12条）

如果用户明确说"快速检查""只看 P0""时间紧"，则切换到快速模式（只检查 P0 核心3条，约30秒）。否则直接执行完整检查。

Step 3 · 执行检查

按所选模式，逐条检查并判定：✅ 通过 / ⚠️ 需改进 / ❌ 缺失

Step 4 · 输出报告

见"输出格式"节。

检查标准

P0 · 核心项（快速模式只检查这3条）

P0-1 · description 质量 检查 description 是否同时包含：

做什么（功能描述）
何时用（触发场景/触发词）
何时别用（边界，防误触发）

判定：

✅ 三项齐全，触发词具体（如写了"用户会遇到的具体问题信号"）
⚠️ 缺"何时别用"，或触发词过于泛泛（如只写"helps with X"）
❌ description 只有功能描述，没有触发场景；或极度模糊

P0-2 · 职责单一性 检查这个 skill 是否只干一件能干净归类的事。

判定：

✅ 明确聚焦单一职责，有"NOT for…"边界声明
⚠️ 职责略宽，但尚可接受；或缺少"Not for"但实际内容单一
❌ 试图同时做多件不相关的事（如"规划+写作+发布+分析"全干）

P0-3 · 失败分支 检查 skill 是否有失败处理机制：当依赖的工具/API/数据源/外部服务失败时，是否写明了处理方式。

判定：

✅ 明确写死失败分支（"失败即停""禁止兜底""失败时报错等用户"）
⚠️ 有模糊的错误提示，但没有禁止兜底行为
❌ 只写正常路径，完全没有失败处理（最危险：模型会偷偷降级）
N/A：该 skill 不依赖外部工具/数据（如纯方法论类），此项跳过

P1 · 重要项（完整模式追加）

P1-1 · 配置/规则的具体性 检查 skill 内部的配置、规则、约束是否具体可执行（非口号）。

判定：

✅ 给了具体标准/维度/正反例（如"核心用户判断依据复购频次/抗周期性/成瘾性"）
⚠️ 规则存在但过于抽象（如"保持高质量""专业风格"）
❌ 配置/规则是口号级别，模型无法执行

P1-2 · 产出可判定性 检查是否有明确的验收标准，让产出的好坏可以被判断。

判定：

✅ 有量化评分/等级/验收清单（如"目标10/10""S/A/B/C/D等级""官方源≥30%"）
⚠️ 有定性描述的输出要求，但无量化标准
❌ 只说"给出建议"，没有任何标准说明什么是好的输出

P1-3 · 正文长度 检查 SKILL.md 正文行数是否在合理范围。

判定：

✅ <500行（官方红线），或虽较长但有 references/ 分文件承载细节
⚠️ 300-500行，存在重复内容或可拆分到 references 的部分（内部建议：超过300行就开始考虑拆分）
❌ >500行，且正文有大量重复步骤/例子堆砌（如 radar 的874行）

P1-4 · 渐进式披露 检查是否合理使用了 references/ 目录分担细节。

判定：

✅ 正文做导航，细节拆进 references/；或明确标注"何时读哪个文件"
⚠️ 有 references/ 但正文仍有大量细节可以下沉
❌ 所有内容全堆在 SKILL.md，没有分文件
N/A：skill 本身较简单（<150行），不需要 references/

P2 · 加分项（完整模式追加）

P2-1 · 样例质量 检查是否有具体的好/坏输入输出样例。

判定：

✅ 有具体样例（好输入+好输出，或正反例对比）
⚠️ 有抽象描述但没有具体例子
❌ 完全没有样例

正反例参考（用于校准判定标准）：

❌ 没有样例的 description（P2-1 = ❌）：

description: "Helps you write better code."

✅ 有具体样例的 description（P2-1 = ✅）：

description: "Reviews Rust code for ownership and borrow-checker issues.
Triggers: 'my Rust code has lifetime errors', 'borrow checker keeps rejecting this'.
NOT for: general code style, Python/JS code."

同理，P2-1 = ✅ 的条件：skill 正文内有"输入示例 → 输出示例"对，或明确的正反例对比表（如 insight 的3段对话demo、radar 的"10分/1分选题"对比）。仅有"样例可以提高效果"类的说明，判定为 ⚠️。

P2-2 · allowed-tools 设置（工具执行型 skill 必查）如果 skill 会调用外部工具/执行命令，检查 frontmatter 是否有 allowed-tools 限定。

判定：

✅ 有 allowed-tools，且只列了该 skill 实际需要的工具
⚠️ 有 allowed-tools 但范围过宽
❌ 调用外部工具但没有 allowed-tools（权限未收敛）
N/A：纯方法论/纯建议类 skill，不执行外部操作

P2-3 · 破坏性操作保护（工具执行型 skill 必查）如果 skill 会执行写入/删除/发布等不可逆操作，检查是否有前置确认机制。

判定：

✅ 写明"发布/删除/覆盖前必须用户确认""不许同一轮确认+执行"
⚠️ 有提示但不够明确，模型可能跳过确认
❌ 有破坏性操作但没有确认机制
N/A：该 skill 不执行破坏性操作

P2-4 · 方法论 skill 专项（方法论封装型必查）如果 skill 封装的是一套方法论/框架，检查：

是否有 prompt_templates（框架是骨架，模板是肉）
是否有 Ethical boundary（防滑向操纵）
是否主动声明能力边界（"给的是方向，不是定论"）

判定：

✅ 三项具备
⚠️ 部分具备
❌ 都没有（尤其洞察/营销/决策类 skill 的风险）
N/A：非方法论封装类 skill

P2-5 · 迭代友好性 检查是否有 last-updated 字段、版本号、或 CHANGELOG。

判定：

✅ 有版本记录机制
⚠️ 有 last-updated 但无版本/变更记录
❌ 完全没有，迭代后无法追踪

输出格式

快速模式报告

## Skill 快速检查报告

**Skill 名称**：{name}
**检查模式**：快速（P0 核心项）

| # | 检查项 | 结果 | 问题/建议 |
|---|---|---|---|
| P0-1 | description 质量 | ✅/⚠️/❌ | {一句话} |
| P0-2 | 职责单一性 | ✅/⚠️/❌ | {一句话} |
| P0-3 | 失败分支 | ✅/⚠️/N/A | {一句话} |

**快速结论**：{✅ 核心没问题，可继续 / ⚠️ 有X处需注意 / ❌ 有X处关键缺陷，建议修复后再用}

> 需要完整检查？告诉我"完整检查"。

完整模式报告

## Skill 完整检查报告

**Skill 名称**：{name}
**检查模式**：完整（P0 + P1 + P2）

### P0 · 核心项
| # | 检查项 | 结果 | 问题/建议 |
|---|---|---|---|
| P0-1 | description 质量 | | |
| P0-2 | 职责单一性 | | |
| P0-3 | 失败分支 | | |

### P1 · 重要项
| # | 检查项 | 结果 | 问题/建议 |
|---|---|---|---|
| P1-1 | 配置/规则具体性 | | |
| P1-2 | 产出可判定性 | | |
| P1-3 | 正文长度 | | |
| P1-4 | 渐进式披露 | | |

### P2 · 加分项
| # | 检查项 | 结果 | 问题/建议 |
|---|---|---|---|
| P2-1 | 样例质量 | | |
| P2-2 | allowed-tools | | |
| P2-3 | 破坏性操作保护 | | |
| P2-4 | 方法论专项 | | |
| P2-5 | 迭代友好性 | | |

### 综合评分

| 层级 | 通过/总数 | 状态 |
|---|---|---|
| P0 核心 | X/3 | ✅/⚠️/❌ |
| P1 重要 | X/4 | ✅/⚠️/❌ |
| P2 加分 | X/Y（适用项）| ✅/⚠️/❌ |

**总体评级**：
- ✅ **可发布**：P0 全通过，P1 无❌
- ⚠️ **建议改进后发布**：P0 全通过，P1 有⚠️
- 🔧 **需修复**：P0 有⚠️或 P1 有❌
- ❌ **不建议使用**：P0 有❌

### 优先修复清单
{列出所有❌和重要⚠️，按 P0→P1→P2 排序，每条给具体改法}

Rationalizations to Reject

检查过程中，不要因为以下理由放水：

"description 虽然泛，但我能理解它的意思" → 模型触发靠的是文字匹配，不是你的理解
"失败情况很少见，不用写" → 静默失败是最伤信任的失败，必须写死
"这个 skill 很简单，不需要 examples" → 简单不是省略样例的理由
"作者是专业的，规则肯定没问题" → 专业不代表具体可执行，照标准检查

来源

本 skill 的检查标准提炼自以下案例的纵深分析： wechat-topic-radar（失败分支/配置具体性）、typefully（allowed-tools/破坏性操作）、daymade deep-research（质量门/诚实失败）、Jeffallan（渐进式披露/validator）、market-insight（方法论封装/伦理边界）、trailofbits（反偷懒清单/前置检查分级）、ask-questions（When NOT to Use/职责单一）

以上项目均为开源项目，分析仅供学习参考，设计思路归原作者所有。

References

references/sample-report.md — 读完检查标准想看实际报告长什么样时加载。包含一个"有典型问题的 mini-SKILL.md"和对应的完整检查报告输出，可用于校准判定尺度。

CHANGELOG

v1.1.0 — 2026-06-12

Step 2 改为默认完整模式，移除每次必问的 AskUserQuestion（减少一轮交互）
P2-1 判定后新增正反例参考，展示有/无样例的 description 对比和 ✅/⚠️ 分界线
新增 references/sample-report.md：真实 mini-SKILL.md 输入 + 完整检查报告输出样例
修正 When NOT to Use 中的笔误 criar-skill（葡萄牙语），改为通用表述
P1-3 行数标准从 "<300行" 对齐官方红线 "<500行"，保留 300 作内部建议注释

v1.0.0 — 2026-06-11

初始发布
P0/P1/P2 三层检查标准，12个检查项
两档模式：快速（P0）/ 完整（P0+P1+P2）
Rationalizations to Reject 反放水清单
P2-1 加描述正反例（好/坏 description 片段）

When it activates

Automatically, when a task matches. Use when the user asks to check, review, or validate a skill they have built or are building. Triggers: '帮我检查这个skill', '我写的skill有没有问题', '我写的skill好不好', 'review my SKILL.md', 'validate my skill', '检查一下我的skill'. Two modes: quick (P0 only, ~30 seconds) or full (P0+P1+P2, comprehensive). NOT for executing skills, NOT for creating new skills from scratch.

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 — 8/1/2026

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

wtfitsme-design / skill-checker

SKILL.md

View on GitHub ↗

License MITImported 8/1/2026Version v1Bundled 1 file

Illustrative Want to see what wtfitsme-design/skill-checker 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.

leoyeai/skill-reviewer审核/审查 Skill 代码质量的专业工具。当用户说"检查 skill"、"审核 skill"、"review {名称} skill"、"skill 写得怎么样"、"帮我看看这个 skill 有什么问题"时使用。依据 Anthropic 官方指南进行结构验证、YAML 前置信息检查、描述质量评估、指令完整性审查，并输出详细的问题报告和改进建议。Close match+32 pts pass-rate lift

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Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

skill-checker

When to Use

When NOT to Use

Workflow

Step 1 · 获取待检查的 skill

Step 2 · 确认检查模式

Step 3 · 执行检查

Step 4 · 输出报告

检查标准

P0 · 核心项（快速模式只检查这3条）

P1 · 重要项（完整模式追加）

P2 · 加分项（完整模式追加）

输出格式

快速模式报告

完整模式报告

Rationalizations to Reject

来源

References

CHANGELOG

v1.1.0 — 2026-06-12

v1.0.0 — 2026-06-11

When it activates

SkillSafety

Source

Other skills for the same job

Related skills

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