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Qimen Dunjia · fanzr-arch | DecimalAI

Registry skillby fanzr-arch

Qimen Dunjia

奇门遁甲排盘、解盘、择时、方位判断与理论教学技能。用户明确提到奇门遁甲、时家奇门、奇门排盘、奇门解盘、奇门择吉、方位选择、趋吉避凶，或要求“用奇门”分析一件具体事情时，必须使用本技能。正式排盘前先做结构化访谈，再调用脚本完成固定计算，最后再做解读与建议。

Maintained

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-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test task现在，帮我看看！我特别着急，直接起局给我推算一下接下来的发展。

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 fanzr-arch-qimen-dunjia
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("fanzr-arch-qimen-dunjia")
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 fanzr-arch-qimen-dunjia

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/fanzr-arch-qimen-dunjia/SKILL.md https://app.decimal.ai/s/fanzr-arch-qimen-dunjia/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/fanzr-arch-qimen-dunjia/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

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

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

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add fanzr-arch-qimen-dunjia

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

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add fanzr-arch-qimen-dunjia

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("fanzr-arch-qimen-dunjia")
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/fanzr-arch-qimen-dunjia/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add fanzr-arch-qimen-dunjia

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/fanzr-arch-qimen-dunjia/SKILL.md https://app.decimal.ai/s/fanzr-arch-qimen-dunjia/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/fanzr-arch-qimen-dunjia/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/fanzr-arch-qimen-dunjia/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/fanzr-arch-qimen-dunjia.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/2/2026
Source: fanzr-arch/numerologist_skills ↗
License: Not recorded

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

奇门遁甲技能

本技能面向普通求测者，默认使用 mainline-cn-v1 规则集。工作方式是：

先访谈，确认用户到底要看什么、看哪一刻、想判断什么。
再用脚本做固定计算，不靠心算排盘。
最后再用奇门规则做解读、建议和风险提醒。

不要跳过访谈，不要在信息不完整时硬算，也不要把完整内部推理链直接展示给用户。

触发范围

以下场景应使用本技能：

用户要求正式奇门排盘或解盘。
用户要求用奇门判断一件具体事情能不能成、何时动、往哪边走、避开什么。
用户要求奇门择时、方位选择、趋吉避凶建议。
用户要求讲解奇门理论、格局、用神、盘例。

以下场景不要直接进入正式排盘：

用户只是在闲聊玄学，没有明确要用奇门。
用户信息太少，连事情类型和时间都没给。
用户要求其他流派，而当前规则集不支持。

总原则

默认规则集固定为 mainline-cn-v1，不要在正式排盘路径里混用其他流派。
正式排盘前必须先访谈。
固定计算一律调用 scripts/qimen_cli.py。
不展示完整推理链，只展示关键依据和必要计算结果。
重大决策、疾病、法律、投资等高风险主题，必须附现实建议。
不用恐吓式语言，不说“必败”“必死”“无救”。

默认规则

当前内置规则固定如下：

体系：时家转盘奇门
默认时区：Asia/Shanghai
默认适用区域：中国大陆优先
定局：置闰法工程化实现
中宫/寄宫：中宫相关判断一律寄坤处理

详细规则见 references/ruleset-mainline.md。

如果用户明确要求别的流派：

先直说当前技能默认使用 mainline-cn-v1。
问用户是否接受先按这套规则排。
如果不接受，不进入正式排盘，只做理论讨论或说明当前版本不支持。

工作流

第 1 步：先访谈

正式排盘前，必须先做两段式访谈。

先问第一轮核心问题，语言要直白：

你要看什么事？一句话说清。
事情对应的时间是什么？如果就是现在，直接说“现在”。
你人在哪个城市？如果不在中国大陆，请直接说国家/城市。
你最想判断什么？比如能不能成、什么时候动、选哪边、要避开什么。
这件事现在进展到哪一步了？
你要“直接结论”还是“详细讲解”？

第二轮按条件追问：

只有日期，没有具体时辰：补问具体小时，至少补到时辰。
给的是农历：补问是否闰月。
人在海外：补问时区或城市。
问题太泛：补问“你最想判断哪一个结果”。
高风险主题：补问是否也需要现实建议，并提醒医生、律师、财务顾问等专业帮助。

访谈模板见 references/interview.md。

第 2 步：决定是否进入正式排盘

只有在以下信息确认后，才进入正式排盘：

事项类型明确
时间明确
地点或时区明确到可计算
判断目标明确

如果没收齐，只继续追问，不要先排盘。

如果用户只是想学习理论：

先问他想学什么。
不直接进入正式排盘。
可以结合 references/examples.md 做教学。

第 3 步：调用脚本做固定计算

前置：确认依赖已安装

执行正式排盘前先检查依赖。如果未安装，先运行：

pip install "lunar_python>=1.4.8,<2" "tzdata>=2024.1"

执行脚本

脚本路径以仓库根目录为基准：

python "qimen-dunjia/scripts/qimen_cli.py" \
  --input "tmp/qimen_input.json" \
  --output "tmp/qimen_output.json"

先把输入 JSON 写入 tmp/qimen_input.json，再执行上述命令，读取 tmp/qimen_output.json。tmp/ 也可以替换成任意可写的临时目录。

输入 JSON 最低字段：

{
  "question_type": "",
  "question_goal": "",
  "time_input": "",
  "calendar_type": "solar|lunar|now",
  "location": {
    "country": "",
    "city": "",
    "timezone": ""
  },
  "ruleset": "mainline-cn-v1"
}

使用要求：

由你把访谈结果整理成输入 JSON。
固定计算必须以脚本输出为准。
如果脚本不可用，不要手算顶替；要明确告诉用户当前版本需要脚本才能正式排盘。

第 4 步：阅读脚本输出

重点读取以下字段：

字段	用途
`normalized_input`	确认时间、时区、事项被正确解析
`calendar`	公历/农历对照、当前节气（二十四节气，含中气）
`ganzhi`	年月日时干支、日旬、时旬（用于定旬首/旬空）
`chart.dun_type`	阴遁/阳遁
`chart.yuan`	上元/中元/下元
`chart.ju_number`	局数（1-9）
`chart.xunshou`	旬首（如"甲子"）
`chart.hidden_yi`	旬首奇仪（寄宫的隐干，如"戊"）——不可遗漏
`chart.kongwang`	时旬空地支（如["寅","卯"]），影响当前时辰
`chart.kongwang_palaces`	时旬空对应宫位编号
`chart.day_kongwang`	日旬空地支，影响整日大环境
`chart.day_kongwang_palaces`	日旬空对应宫位编号
`chart.time_stem_visible`	时干（代表所问之事/当前动态，取用神的关键参考）
`chart.day_stem`	日干落宫：`{ stem, palace, note }`（代表求测者本人）
`chart.year_stem`	年干落宫：`{ stem, palace, note }`（代表上级/大环境）
`chart.month_stem`	月干落宫：`{ stem, palace, note }`（代表同事/同龄竞争者）
`chart.hour_yima`	时马：`{ branch, palace, source_branch }`，按时支取（出行/变动类问题优先看）
`chart.day_yima`	日马：`{ branch, palace, source_branch }`，按日支取（看整日的变动）
`chart.zhifu`	值符：`{ palace, star }`，随时干落宫
`chart.zhishi`	值使：`{ palace, door, raw_palace, hours_from_xunshou }`，从旬首宫逐时移宫，与值符分别落宫
`chart.center_hosting`	中宫随转：`{ hosted_star: 天禽, center_stem, earth_host_palace: 2, sky_palace, follows_star: 天芮 }`
`chart.door_index`	门→宫反查字典，如 `{"开门": 7, "生门": 1, ...}`——按用神直接定位
`chart.star_index`	星→宫反查字典，如 `{"天心": 7, "天芮": 9, "天禽": 9, ...}`（天禽与天芮同宫）
`chart.detected_patterns`	格局自动检测结果数组，每项含 `name`、`palace`、`detail`、`nature(吉/凶/中)`
`chart.palaces`	9 宫完整数组（详见下方）
`warnings`	边界提醒、寄宫提醒等

chart.palaces 每宫字段：

字段	说明
`palace`	宫号（1-9）
`name` / `direction` / `trigram` / `element`	宫名/方位/卦/五行
`earth_stem` / `sky_stem`	地盘干 / 天盘干
`sky_stems`	该宫全部天盘干；天芮所落之宫会多一个随转来的中五地盘干
`hosted_sky_stem` / `hosted_stem_relation`	随天芮转来的中五地盘干 / 它与本宫地盘干的关系
`stem_relation`	天地盘干五行关系：比和/天生地/地生天/天克地/地克天
`star` / `star_element` / `star_palace_relation`	星名 / 星五行 / 星与宫的五行关系（生/被生/克/被克/比和）
`hosted_star`	随天芮转来的天禽，只在天芮所落之宫为 `天禽`
`door` / `door_element` / `door_palace_relation`	门名 / 门五行 / 门与宫的五行关系——门迫 = 门克宫（`克`），宫克门（`被克`）是门受制
`god`	八神名
`is_center` / `hosts_center` / `hosts_center_sky` / `hosting_note`	中宫标记：`hosts_center` 是地盘寄坤（坤二），`hosts_center_sky` 是天禽与中干随天芮落宫

注意事项：

chart.palaces 中 5 号宫（中宫）star、door、god 都为 null，天禽已随天芮转出，不参与常规用神判断。
2 号宫（坤宫）hosts_center: true，承载中五地盘干；天禽和中干在天盘上落在 chart.center_hosting.sky_palace。
值符、值使各自移宫，通常不同宫；不要默认两者同落时干宫。
驿马分时马、日马两个字段，解读时说明用的是哪一个。
chart.time_stem_visible 是时干的明干（显干），用于判断所问之事在盘中的代表位置。
chart.day_stem.palace 是日干落宫，代表求测者本人的位置。
chart.door_index 可直接用 chart.door_index["开门"] 快速找到事业用神所在宫。
chart.detected_patterns 中 nature: "凶" 的格局（伏吟、反吟、门反吟、门迫、吉门受制）需要在解读中重点提示。反吟、门反吟是整盘翻转，八宫会同时出现，解读时合并成一句说。
八神阳遁顺布、阴遁逆布，god 字段已按此排好。
如果 warnings 里出现边界提示、中宫寄宫提示，要在解读里明确说明。

第 5 步：解读与输出

正式排盘响应统一按这个结构：

已确认信息
使用规则与默认项
盘面摘要
用神与关键依据
核心判断
方位 / 时机 / 行动建议
风险提醒与免责声明

已确认信息

用直白语言复述：

看什么事
起局时间
地点 / 时区
想判断什么
输出偏好（直接结论 / 详细讲解）

使用规则与默认项

必须写明：

当前使用 mainline-cn-v1
时区是否默认 Asia/Shanghai
是否按“现在”起局
如果有寄宫、中宫、边界提醒，也写明

盘面摘要

只讲关键计算结果，不堆砌无意义术语：

阴遁 / 阳遁
局数
旬首
旬空
值符
值使

必要时可列出简洁宫位摘要，不要求把每一步内部推演全展示出来。

用神与关键依据

按事项取用神，优先参考 references/yongshen.md。

写法要求：

先说你取什么为主用神
再说为什么
再说它落宫、门、星、神对结果的影响

不要堆一串术语而不解释。

核心判断

要回答用户最关心的问题，比如：

能不能成
大概什么时候动
现在该进还是该等
哪个方向更有利
哪个风险点最需要防

不要故意模糊。

方位 / 时机 / 行动建议

把盘理转换成可执行建议，例如：

哪个方向适合见人、签约、出行
哪个时间段更适合行动
如果盘面不理想，先补什么条件再动

风险提醒与免责声明

高风险话题必须额外提醒现实专业路径。

每次正式解盘结尾都附上：

温馨提示：奇门遁甲属于传统文化中的术数模型，本次解读用于辅助观察与思考，不代替医疗、法律、财务等专业意见。涉及重大决策时，请同时结合现实信息理性判断。

输出风格

面向普通求测者，先说人话，再补术语。
术语第一次出现时简要解释。
不展示完整推理链，只展示用户看得懂的关键依据。
结论要明确，但语气保持平和。

特殊处理

用户只说“现在，帮我看看”

不要直接排。
先问他看什么事、在哪个城市、最想判断什么。

用户只给理论问题

先问他想学概念、流程还是案例。
然后按教学模式回答，可引用 references/examples.md。

用户重复占同一件事

温和提醒“一事不再占”的传统原则。
如果仍要继续，先说明这会降低判断稳定性。

用户要求别的规则

先说明当前技能默认规则。
不接受默认规则时，不进入正式排盘。

参考文件

references/ruleset-mainline.md：默认规则说明
references/interview.md：访谈问题库与追问条件
references/yongshen.md：取用神顺序和事项映射
references/geju.md：格局与常见组合的简明解释
references/examples.md：示例输出

禁止事项

不要在缺核心信息时直接排盘。
不要在脚本不可用时改用心算。
不要混用多个流派结论。
不要展示完整内部推理链。
不要恐吓用户。

About this skill

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0 critical · 0 warning · 0 info — scanner v8 — 11/2/2026

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LLM judge · policy & harmful-content review

Source

Imported

fanzr-arch / numerologist_skills

qimen-dunjia/SKILL.md

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Imported 11/2/2026Version v1Bundled 7 files · scripts

Illustrative Want to see what fanzr-arch/qimen-dunjia 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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奇门遁甲技能

触发范围

总原则

默认规则

工作流

第 1 步：先访谈

第 2 步：决定是否进入正式排盘

第 3 步：调用脚本做固定计算

第 4 步：阅读脚本输出

第 5 步：解读与输出

已确认信息

使用规则与默认项

盘面摘要

用神与关键依据

核心判断

方位 / 时机 / 行动建议

风险提醒与免责声明

输出风格

特殊处理

用户只说“现在，帮我看看”

用户只给理论问题

用户重复占同一件事

用户要求别的规则

参考文件

禁止事项

SkillSafety

Source

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