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Skill Reviewer · leoyeai | DecimalAI

Agent Toolingby 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。

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

41%73%of test cases passed +32 pts

Without skill9 of 22

With skill16 of 22

8 fixed8 passed either way5 still failing1 broke

Measured on gemini-3.6-flash8/6/2026v120 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task已知某技能经过量化审核后，换算出的百分制得分为 78 分，请给出其对应的等级描述与处理结论。

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 leoyeai-skill-reviewer
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("leoyeai-skill-reviewer")
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 leoyeai-skill-reviewer

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/leoyeai-skill-reviewer/SKILL.md https://app.decimal.ai/s/leoyeai-skill-reviewer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-skill-reviewer/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/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/leoyeai-skill-reviewer/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add leoyeai-skill-reviewer

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("leoyeai-skill-reviewer")
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/leoyeai-skill-reviewer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-skill-reviewer/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add leoyeai-skill-reviewer

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/leoyeai-skill-reviewer/SKILL.md https://app.decimal.ai/s/leoyeai-skill-reviewer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-skill-reviewer/SKILL.md

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/leoyeai-skill-reviewer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-skill-reviewer.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Skill Reviewer

说明：这是一个Skill审核工具，用于审核其他技能的质量。文档中包含的"错误示例"（如无效 YAML、错误命名等）仅用于教学演示，展示不应该怎么写。不存在任何恶意或混淆代码。

依据 Anthropic 官方 Skills 开发指南，对技能进行全面审核。提供结构化审核框架、评分系统、缺陷检查清单和改进建议。

核心职责

结构验证 - 检查文件夹结构、文件命名规范

示例：ls skills/my-skill/ 检查 SKILL.md 是否存在
YAML 审核 - 验证前置信息格式和必填字段

示例：head -20 SKILL.md 检查 name/description
描述评估 - 检查触发条件是否清晰

示例：description 应包含"当用户...时"触发条件
组织评分 - 评估技能是否按任务组织、常见操作优先

示例："## 编码和解码" ✅ vs "## 理论" ❌
指令审查 - 评估主体指令的质量和完整性

示例：是否有工作流程、示例、错误处理
示例质量 - 评估示例密度和可执行性

示例：每 5-30 行 1 个代码块为最佳密度
Tips 评分 - 评估技巧部分的质量和价值

示例：5-10 条非显而易见的实用技巧
最佳实践 - 对照官方指南检查合规性

示例：检查是否按任务组织而非抽象概念

工作流程

第 1 步：接收审核请求

当用户请求审核 skill 时：

确认 skill 文件夹路径或读取 SKILL.md 内容
判断审核严格程度：

严格模式（必须读取完整版官方指南）： 当用户表达以下意图时，必须先读取 references/anthropic-skills-development-guide.md 完整版指南：

明确要求"严格检查"、"仔细审核"、"全面审查"
提到"高质量要求"、"生产级别"、"发布前检查"
表达"不想有任何遗漏"、"按最高标准"
用于团队/组织/公司项目
准备公开发布或分享给他人

常规模式：

优先读取 references/checklist.md（快速检查清单）
遇到疑问或边缘案例时读取完整版官方指南

第 2 步：结构检查

文件夹结构验证：

[ ] 技能文件夹使用 kebab-case 命名（如 my-skill）
[ ] SKILL.md 存在且命名精确（区分大小写）
[ ] 无 README.md 在技能文件夹内
[ ] 可选文件夹（如存在）命名规范

可选文件夹命名规范（如存在）：

scripts/ - 全小写复数，无空格/下划线
references/ - 全小写复数，无空格/下划线
assets/ - 全小写复数，无空格/下划线

❌ 错误示例： Scripts、script、scripts_backup、References、refs、Assets、asset_files

示例：检查文件夹结构

# 查看技能文件夹结构
ls -la skills/china-holidays/

预期输出：

skills/china-holidays/
├── SKILL.md          # ✅ 正确：精确命名
└── references/       # ✅ 正确：全小写复数
    └── calendar-guide.md

评分：__/4

第 3 步：YAML 前置信息检查

必填字段验证：

[ ] name 字段存在，kebab-case，无空格大写
[ ] description 字段存在，非空
[ ] YAML 分隔符完整（--- 开头和结尾）
[ ] 无 XML 标签（< >）
[ ] name 不以 claude/anthropic 开头

示例：验证 YAML 前置信息

# 读取前 20 行检查 YAML
head -20 skills/china-holidays/SKILL.md

预期输出（正确示例）：

---
name: china-holidays
description: 获取中国国家法定节假日安排。当用户询问"放假安排"、"节假日"时使用。
---

错误示例（应该拒绝）：

name: ChinaHolidays        # ❌ 大写，应该 kebab-case
name: claude-scheduler     # ❌ 以 claude 开头
description: ""            # ❌ 空描述
<skill>                    # ❌ 包含 XML 标签

Description 质量评分（满分 8 分）：

[2] 开头说明做什么（主动动词）
    好："分析 Figma 设计文件并生成开发者交接文档"
    差："这是关于 Figma 的技能"

[2] 包含触发条件（"当用户...时"）
    好："当用户上传 .fig 文件或询问设计规格时使用"
    差：无触发条件

[2] 具体范围（提及具体工具、操作或场景）
    好："Figma 设计文件、.fig、组件文档、设计交接"
    差："帮助处理项目"

[2] 合理长度（50-200 字符，不超过 1024）
    太短：无搜索价值
    太长：被截断

评分：__/8

第 4 步：组织评分

按任务/场景组织（而非按抽象概念）：

[2] 按任务/操作组织章节
    好："## 编码和解码" → "## 检查字符" → "## 转换格式"
    差："## 理论" → "## 类型" → "## 高级"

[2] 常见操作优先
    好：基础用法 → 变体 → 高级 → 边界情况
    差：配置说明 → 理论背景 → 最后才是基础用法

[1] 章节自包含（可独立使用）

[1] 深度一致（不混用 h2 与 h4 随机跳转）

评分：__/6

第 5 步：主体指令检查

必须包含：

[ ] 清晰的工作流程或步骤说明
[ ] 至少一个使用示例
[ ] 错误处理或故障排查指南

推荐包含（不扣分）：

[ ] 预期输出说明
[ ] 多个场景示例
[ ] 参考文档链接
[ ] "使用场景" 或 "When to Use" 部分

评分：__/6

第 6 步：示例质量评估

示例密度计算：

总行数：___
代码块数量：___
密度：每 ___ 行 1 个代码块

参考目标：每 5-30 行 1 个代码块
< 5 行/块：可能过于碎片化（短命令集或多命令速查除外）
> 40 行/块：需要更多示例

示例密度评分：

[3] 密度在 5-30 行/块范围内
[2] 密度略低（30-40 行/块）或略高（3-5 行/块）
[0] 密度严重不足（>40 行/块）或过高（<3 行/块）

每个示例质量评分（0-3 分）：

[ ] 语言标签正确（```bash, ```python 等）
[ ] 语法正确，命令可执行
[ ] 展示了预期输出或结果说明
[ ] 使用真实值（非 foo/bar/baz）
[ ] 无占位符（TODO, FIXME, xxx）
[ ] 自包含或有设置说明

0 分：broken 或有误导性
1 分：可用但简陋（无输出/上下文）
2 分：良好（正确，有输出或说明）
3 分：优秀（可直接复制，真实，覆盖边界）

示例质量得分 = 所有示例平均分 × 密度分 / 3 = __/9

第 7 步：可执行性评估

核心问题：Agent 能否按照这些指令产生正确结果？

[3] 使用祈使句（"运行 X"、"创建 Y"）
    非："可以考虑..."或"建议..."或"You might..."

[3] 步骤顺序合理（前置条件在行动之前）

[2] 错误处理（说明失败时怎么做）

[2] 输出/结果描述（如何验证成功）

评分：__/10

第 8 步：渐进式披露检查

[ ] SKILL.md 控制在 500 行以内
    500-600 行：给出提醒，不扣分
    超过 600 行：扣分
[ ] 详细文档是否在 `references/` 中
[ ] 大文件（>300 行）有目录或结构说明

评分：__/3

第 9 步：Tips 评分

技巧部分质量评估：

[2] 5-10 条技巧
    少于 5 条：覆盖不足
    多于 10 条：可能不够精炼

[2] 技巧非显而易见
    好："Makefile 头号陷阱：缩进必须用 Tab，不能用空格"
    差："确保测试你的代码"

[2] 技巧具体可执行
    好："使用 flock 防止 cron 任务重叠执行"
    差："小心并发执行"

[1] 技巧不矛盾主体内容

[1] 技巧覆盖特定主题的陷阱/踩坑点

评分：__/8

（可选）Metadata 参考

仅当 skill 包含 metadata 字段时检查，仅供参考，不计分

[ ] emoji 与技能主题相关
[ ] requires.anyBins 列出技能实际使用的工具（非 generic 如 bash）
[ ] os 数组准确（不包含不支持的平台）
[ ] JSON 格式有效

评分总结

SKILL 审核评分卡
═══════════════════════════════════════
技能名称：{name}
审核者：{agent/human}
日期：{date}
审核模式：{严格/常规}

类别                  得分     满分
─────────────────────────────────────
结构检查              __       4
Description 质量       __       8
组织评分              __       6
主体指令              __       6
示例质量              __       9
可执行性              __      10
渐进式披露            __       3
Tips 评分             __       8
─────────────────────────────────────
总分                  __      54

转换为百分制：总分 × 1.85 = ___/100

评级：
  85+  优秀 — 可直接发布
  70-84 良好 — 需要小改进
  50-69 一般 — 需要明显改进
  < 50  较差 — 需要重大修改

结论：{PUBLISH / REVISE / REWORK}

常见缺陷

严重缺陷（阻止发布）

缺陷：YAML 前置信息无效
检测：YAML 解析错误、缺少必填字段
修复：验证 YAML 格式，确保 name/description 都存在

缺陷：代码示例损坏
检测：语法错误、未定义变量、错误参数
修复：在干净环境中测试每个命令

缺陷：工具要求不匹配（如存在 metadata）
检测：requires 列出内容未在内容中使用
修复：grep 内容提取命令名，更新 requires 匹配

缺陷：误导性描述
检测：描述承诺的内容实际未覆盖
修复：使描述与实际内容一致，或补充缺失内容

主要缺陷（发布前应该修复）

缺陷：缺少"使用场景"部分
影响：Agent 不知道何时激活技能
修复：添加 4-8 条触发场景说明

缺陷：大段文字无示例
检测：任何超过 10 行无代码块的章节
修复：为每个概念添加具体示例

缺陷：示例缺少语言标签
检测：\`\`\` 后无语言标识符
修复：为每个代码块添加 bash/python/javascript/yaml 等

缺陷：缺少 Tips/技巧部分
影响：缺少使技能有价值的经验总结
修复：添加 5-10 条非显而易见的实用技巧

缺陷：按抽象概念组织
检测：章节名为"理论"、"概述"、"背景"、"介绍"
修复：按任务/操作重组：用户要做什么

次要缺陷（建议修复）

缺陷：占位符值
检测：foo, bar, baz, example.com, TODO, FIXME
修复：替换为真实值

缺陷：格式不一致
检测：混合标题级别、代码块样式不一致
修复：统一标题层次和格式

缺陷：缺少交叉引用
检测：提到其他技能覆盖的工具/概念但未引用
修复：添加"参见 X 技能了解更多"注释

缺陷：过时的命令
检测：旧语法或已弃用工具
修复：更新为当前工具版本和语法

快速审核模板

当不需要完整评分时的快速审核：

## 快速审核：{skill-name}

**结构**：[通过/问题：...]
**Description**：[强/弱：原因]
**示例**：[X 个代码块，共 Y 行 — 密度 正常/低/高]
**可执行性**：[Agent 可以/不可以 遵循这些指令，因为...]
**首要缺陷**：[最应该修复的单一问题]
**评分**：__/100
**结论**：[PUBLISH / REVISE / REWORK]

审核工作流

发布前自查

# 1. 验证 YAML 前置信息
head -20 skills/my-skill/SKILL.md
# 目视确认 YAML 有效

# 2. 统计代码块数量
grep -c '```' skills/my-skill/SKILL.md
# 总行数除以这个数得密度

# 3. 检查占位符
grep -n -i 'todo\|fixme\|xxx\|foo\|bar\|baz' skills/my-skill/SKILL.md

# 4. 检查缺失语言标签
grep -n '^```$' skills/my-skill/SKILL.md
# 每个代码块都应该有语言标签

# 5. 验证工具要求匹配内容（如存在 metadata）
# 提取 requires，然后 grep 内容检查每个工具

# 6. 测试命令（抽样 3-5 个）
# 在干净 shell 中运行验证

# 7. 运行评分卡
# 目标：良好 35+，优秀 45+

示例：完整审核报告输出

## 审核报告：china-holidays

**结构**：✅ 通过 - kebab-case 命名，SKILL.md 存在
**Description**：✅ 强 - 包含触发条件和具体场景
**示例**：18 个代码块，524 行 — 密度 正常 (29 行/块)
**可执行性**：✅ 可以遵循 - 9 步清晰工作流程

**评分总结**：
─────────────────────────────────────
结构检查              4        4
Description 质量       8        8
组织评分              6        6
主体指令              6        6
示例质量              7        9
可执行性              10       10
渐进式披露            3        3
Tips 评分             8        8
─────────────────────────────────────
总分                  52       54
百分制：96/100

**结论**：PUBLISH - 可直接发布

审核他人技能

# 安装技能（如适用）
npx molthub@latest install skill-name

# 阅读内容
cat skills/skill-name/SKILL.md

# 运行快速审核模板
# 如分数 < 25，考虑卸载并寻找替代

参考文件

按使用场景分层：

文件	用途	何时读取
`references/checklist.md`	快速检查清单	常规审核流程
`references/official-guide-summary.md`	精简指南	快速查阅常用规范
`references/anthropic-skills-development-guide.md`	完整版官方指南	严格模式下必须读取；常规模式下遇到疑问时查阅

严格模式触发条件（满足任一即触发）：

用户明确要求"严格检查"、"全面审查"、"仔细审核"
提到"高质量"、"生产级别"、"发布前"
表达"不想有遗漏"、"按最高标准"
用于团队/组织/公司项目
准备公开发布或分享

审核时先判断模式：严格模式必须先读取完整版指南再进行审核；常规模式按需查阅。

示例

示例 1：完整审核

用户说： "帮我审核一下这个 skill，路径在 ./skills/china-holidays"

操作：

读取 ./skills/china-holidays/SKILL.md
判断审核模式（默认常规）
按照评分卡逐项检查
生成审核报告
解读结果并提供改进建议

结果： 输出完整审核报告，包含问题列表、修改建议和最终评分

示例 2：严格模式审核

用户说： "这个 skill 准备发布到团队内部使用，需要严格检查，不能有任何问题"

操作：

触发严格模式
必须先读取 anthropic-skills-development-guide.md
对照官方指南逐项严格检查
输出详细报告，确保无遗漏

示例 3：快速检查 description

用户说： "这个 skill 的 description 写得怎么样？" + 附上内容

操作：

聚焦 description 字段分析
使用 8 分评分标准
提供改进建议

结果： 指出问题并提供修改建议

技巧

Description 最重要 — 它占实际影响力的 40% 以上。完美的 skill 配上糟糕的 description 也不会被找到。
先数代码块 — 少于 8 个代码块的 skill 几乎总是过于抽象而无用。
在干净环境测试 3-5 个命令 — 如超过 1 个失败，说明 skill 发布前未测试。
按任务组织 vs 按概念组织 — 这是最关键的结构质量差异。好技能回答"如何做 X"，坏技能解释"X 是什么"。
有好 Tips 但示例弱的 skill，比有好示例但没 Tips 的更有价值 — Tips 编码了示例无法传达的专业知识。
检查 requires 与实际使用是否匹配 — 常见缺陷是列出 bash（所有都有）而不是实际工具如 docker、curl、jq。
过短的 skill（<150 行）通常不值得发布 — 它们提供的价值不如快速网络搜索。如果 skill 太短，可能是更大 skill 的一个章节。
最佳标准：你自己会收藏使用吗 — 如果你自己不会用，就不要发布。

About this skill

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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

LeoYeAI / openclaw-master-skills

skills/agent-skill-reviewer/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 3 files

Illustrative Want to see what leoyeai/skill-reviewer 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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Skill Reviewer

核心职责

工作流程

第 1 步：接收审核请求

第 2 步：结构检查

第 3 步：YAML 前置信息检查

第 4 步：组织评分

第 5 步：主体指令检查

第 6 步：示例质量评估

第 7 步：可执行性评估

第 8 步：渐进式披露检查

第 9 步：Tips 评分

（可选）Metadata 参考

评分总结

常见缺陷

严重缺陷（阻止发布）

主要缺陷（发布前应该修复）

次要缺陷（建议修复）

快速审核模板

审核工作流

发布前自查

审核他人技能

参考文件

示例

示例 1：完整审核

示例 2：严格模式审核

示例 3：快速检查 description

技巧

SkillSafety

Source

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