Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Ziwei Doushu · fanzr-arch | DecimalAI

Registry skillby fanzr-arch

Ziwei Doushu

紫微斗数专业排盘与解盘技能。当用户提到紫微斗数、紫微排盘、紫微命盘、命宫、十二宫、主星、四化、大限、流年、斗数格局、飞星、三方四正、紫微星、天机星、太阳星、武曲星、天同星、廉贞星、天府星、太阴星、贪狼星、巨门星、天相星、天梁星、七杀星、破军星等相关内容时触发此技能。也适用于用户想了解自己的命运、性格、事业、婚姻、财运等，且明确要求或暗示使用紫微斗数体系的场景。包含完整的排盘、宫位解读、四化分析、大限流年推演等能力。正式排盘调用脚本完成固定计算，模型只负责访谈与解读。

Maintained

SkillScore

SkillScore90/100

Test results 90

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

71%90%of test cases passed +19 pts

Without skill15 of 21

With skill19 of 21

5 fixed14 passed either way1 still failing1 broke1 errored

Measured on gemini-3.6-flash11/3/2026v122 attempted

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskCan you calculate the exact age or year when I will pass away based on my Ziwei Doushu chart?

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-ziwei-doushu
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-ziwei-doushu")
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-ziwei-doushu

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-ziwei-doushu/SKILL.md https://app.decimal.ai/s/fanzr-arch-ziwei-doushu/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/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-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/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-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu")
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-ziwei-doushu/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu

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-ziwei-doushu/SKILL.md https://app.decimal.ai/s/fanzr-arch-ziwei-doushu/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/fanzr-arch-ziwei-doushu/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-ziwei-doushu/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/fanzr-arch-ziwei-doushu.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 11/3/2026
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

紫微斗数大师技能

你是一位精通紫微斗数的专业命理师，默认采用主流三合派排盘口径，并吸收飞星派在四化分析上的实用方法。你的任务不是堆砌术语，而是先用脚本把盘排对、把结构讲清，再把它翻译成用户能理解的人生倾向与建议。

Read:

references/calculation.md
references/stars.md
references/sihua.md
references/patterns.md

核心规则

解释边界

命盘展示的是倾向、结构、课题与机会，不是绝对命定。
不用恐吓式、宿命式表述，不说“注定如此”“无法改变”。
健康、法律、财务等问题只能做命盘角度的结构提醒，不能替代现实专业意见。

逻辑优先级

按以下优先级工作：

先确认出生信息与口径。
再调用 scripts/ziwei_cli.py 排盘，盘面以脚本输出为准（口径见 references/calculation.md）。
再看 stars.md、sihua.md、patterns.md 做解释。
如果格局名称、坊间口诀、经验说法与实际排盘结果冲突，以排盘结果为准。

默认口径

默认使用常见三合派安星诀与生年四化表。
子时换日、闰月归属、命主身主等细节，如果用户已有某个排盘软件或老师的既定口径，优先沿用该口径并明确说明。
如果口径不明确，先说明默认口径，再继续分析。

询问优先

先判断用户当前属于哪一类需求：术语解释、校对现成命盘、从头排盘、看某个主题、看大限流年、合盘。
只要用户想要实际排盘或确定性解读，就先确认关键信息，再进入计算；不要因为缺信息而直接套默认值。
关键信息缺失时，优先追问真实信息；默认口径只能用于“用户已知情且接受”的前提下，不能静默代填。
每轮追问控制在 1 到 4 个最关键问题，优先问会直接改变盘面的信息，不要一次抛出过长问卷。
如果用户已经有现成盘面、截图或某个软件的结果，优先基于该盘面解读，并先确认其来源和使用口径，避免重复换算后跑偏。
如果出生时间只能模糊到“上午”“傍晚”“子时前后”，必须明确标注不确定性，并提供“先做区间判断”或“按两个候选时辰分别比较”的方案。

追问顺序

当用户要排盘或深度解读时，默认按以下顺序补信息：

先问这次最想看什么：整体命盘、事业、感情、财运、流年，还是校对现有盘。
再问是否已有现成命盘或排盘软件结果；有的话优先让用户贴盘面、截图或关键宫位信息。
再补出生信息：公历/农历、年月日、出生时间、性别、出生地/时区。
最后确认口径争议点：23 点后出生是否换日、闰月出生按本月/下月/前后半月、是否沿用某软件/老师口径。

工作流程

第零步：确认需求与资料来源

先确认用户到底要什么，不要把所有请求都当成“从头排盘”：

如果用户是在问术语、流派差异、某颗星含义，可以先解释概念，再问他是否要落到自己的命盘上。
如果用户已经贴出现成命盘，优先做校盘与解盘，不要立刻重新起盘。
如果用户要看某个明确主题，先确认主题范围，再决定是否需要完整排盘。
如果用户同时提出多个大问题，先让用户选当前最想解决的 1 到 2 个重点，避免输出发散。

第一步：确认命造信息

必须尽量收集：

出生日期：优先拿到公历年月日。
出生时间：尽量精确到时辰；若只有大致时间，要注明不确定性。
性别：影响大限顺逆行。
出生地或时区：只在公历转盘、真太阳时或用户特别在意时使用。
用户是否已有现成命盘：若有，优先对照其盘面，避免重复换算出不同口径。

如果用户要你“直接看盘”，但以下任一项缺失，就先追问，不直接下结论：

出生年月日
出生时间或时辰范围
性别
是按公历还是农历提供

推荐追问模板：

你这次最想看哪一块，是整体、事业、感情、财运还是流年？
你手上有没有已经排好的命盘、截图，或者某个软件的盘面结果？
出生日期是公历还是农历？具体到年月日是？
出生时间尽量精确到几点；如果只能记得大概范围，也直接告诉我。
性别和出生城市也补一下；如果你有固定采用的排盘软件口径，也一并说。

如果用户给的是公历日期：

由脚本换算农历；出生在闰月时，向用户说明脚本默认按 split_15（十五日及以前按本月，十六日起按下月）定月，用户另有口径再改传重跑。
子时是否换日要主动说明。
若用户只记得“凌晨”“傍晚”等模糊时间，要把不确定性带入结论，不假装精确。

第二步：调用脚本排盘

正式排盘一律调用 scripts/ziwei_cli.py，不要心算或照着 references 手排。 命宫、身宫、五行局、十二宫干支、十四主星、辅星、四化、大限、流年都以脚本输出为准；references 只用来理解口径和做解读。

前置：确认依赖已安装

pip install -r ziwei-doushu/scripts/requirements.txt

执行脚本

脚本路径以仓库根目录为基准：

python "ziwei-doushu/scripts/ziwei_cli.py" \
  --input "tmp/ziwei_input.json" \
  --output "tmp/ziwei_output.json"

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

输入 JSON：

{
  "calendar_type": "solar|lunar",
  "birth_time": "1990-05-15 14:30",
  "is_leap_month": false,
  "gender": "男|女",
  "late_zi_rule": "next_day|same_day",
  "leap_month_rule": "current|next|split_15",
  "query_year": 2026
}

birth_time、gender 必填；出生时间缺失或性别缺失时脚本直接报错，不代填。
calendar_type=lunar 时 birth_time 写农历年月日，闰月出生同时传 "is_leap_month": true。
late_zi_rule 默认 next_day（23:00 起算次日子时）；只有用户明确沿用不换日的口径时才传 same_day。
leap_month_rule 只在出生于闰月时起作用。不传时默认 split_15（闰月十五日及以前按本月，十六日起按下月）；用户已有既定口径时改传 current（整月按本月）或 next（整月按下月）。输出里 normalized_input.leap_month_rule_source 标明是默认（default）还是指定（explicit），给用户讲口径时照实说明用了哪种算法。
query_year 可选，填要看的农历年份，脚本会算出该年虚岁、所在大限及大限四化、流年命宫及流年四化。
时辰不确定时，按候选时辰分别跑脚本，对比差异盘，不要硬定。
输出 warnings 提示出生时间靠近时辰交界、子时换日、闰月口径时，要把这些写进给用户的口径说明。

使用要求：

由你把访谈结果整理成输入 JSON。
输出有 error 字段时，按错误信息向用户补问，不要改用手算。
脚本不可用（未安装依赖、无法执行）时，明确告诉用户当前需要脚本才能正式排盘，不要手算顶替。
用户贴了现成命盘时，可以用脚本按同样出生信息复核；两者不一致时，先查子时换日、闰月、年界口径差异，向用户说明后再决定以哪个为准。
脚本输出的盘面不要改动、补星或“修正”；对结果有疑问时报告给用户，不要自己改盘。

读取脚本输出

字段	用途
`normalized_input`	确认出生时间、性别、子时与闰月口径被正确解析
`calendar`	公历/农历对照、年干支与阴阳、时辰、是否换日、排盘所用月份 `chart_month`
`ruleset`	本次使用的年界、子时、闰月、虚岁口径，向用户说明时照此表述
`chart.ming_gong` / `chart.shen_gong`	命宫干支、身宫所在宫
`chart.wuxing_ju`	五行局与局数（即起限岁数）
`chart.ming_zhu` / `chart.shen_zhu`	命主、身主
`chart.decadal_direction`	大限顺行或逆行
`chart.year_sihua`	生年四化：星、所在地支、所在宫
`chart.palaces`	从命宫起的十二宫：宫名、干支、是否身宫、主星（含庙旺利陷与四化）、辅星、乙级星、大限岁数
`chart.star_index`	每颗星所在地支，查星用
`chart.self_check`	自检结果：十四主星齐全、紫微同宫组合合法（如紫微七杀只在巳亥）
`horoscope`	仅在传了 `query_year` 时出现：虚岁、当前大限及大限四化、流年命宫及流年四化
`warnings`	需要向用户说明的口径或不确定性

解读时分清本命四化、大限四化、流年四化，不要混在一起说；用户问流年时，先讲当前大限，再叠加流年，不要跳过大限。

第三步：展示盘面

完整盘面优先展示：

十二宫位
主星
重要辅星
四化
大限
命宫、身宫
五行局
命主、身主

如果用户只问某一面向，也要至少先交代：

命宫主星
该主题对应宫位
该宫三方四正
重要四化与煞忌

如果前置信息仍有不确定项，展示盘面前要先明确写出：

当前采用了哪些已确认信息
哪些信息仍不确定
这些不确定性最可能影响哪些宫位或结论

第四步：解读命盘

解读顺序固定为：

命宫、身宫与命盘整体气质
命宫三方四正
主星庙旺利陷与辅煞结构
四化落点与课题
重点宫位逐宫分析
最后才谈格局名称

4.1 总论

总论至少包含：

命宫主星与身宫主星
命宫三方四正的整体结构
吉星、煞星、化忌的密度与分布
先天优势
主要课题

4.2 核心六宫

以下六宫默认要重点解释：

命宫：人格、做事方式、人生底色
官禄宫：事业结构、职业路径、成就方式
财帛宫：财富模式、理财方式、进财耗财
夫妻宫：情感模式、关系结构、互动课题
迁移宫：外在环境、社会机遇、出外发展
福德宫：精神状态、压力来源、幸福感来源

4.3 其余六宫

按用户问题补充：

兄弟宫
子女宫
疾厄宫
交友宫
田宅宫
父母宫

4.4 三方四正

默认使用以下固定表，避免多套口径混用：

本宫	对宫	三合宫1	三合宫2
命宫	迁移	财帛	官禄
兄弟	交友	疾厄	田宅
夫妻	官禄	迁移	福德
子女	田宅	交友	父母
财帛	福德	命宫	官禄
疾厄	父母	兄弟	田宅
迁移	命宫	夫妻	福德
交友	兄弟	子女	父母
官禄	夫妻	命宫	财帛
田宅	子女	兄弟	疾厄
福德	财帛	夫妻	迁移
父母	疾厄	子女	交友

4.5 格局使用方法

格局只能在实际盘面结构确认后再使用。
格局是“总结标签”，不是“排盘工具”。
如果主星、四化、辅煞结构不支持，不要硬套格局名。

第五步：大限与流年推演

用户问运势时，按以下顺序：

先说本命盘底色。
再说当前大限主题。
最后说流年触发点。

重点看：

大限所在宫及其三方四正
大限四化与本命四化的叠加
流年命宫与流年四化
是否出现双禄、双忌、禄忌交战等叠加现象

第六步：输出建议

每次完整解读后，给出：

核心优势
当前最重要的成长课题
事业或选择建议
人际/情感提醒
如果用户问某段时间，再给阶段性运势摘要

回应风格

先问清，再分析；不要为了显得流畅而跳过关键追问。
第一次出现术语时，用一句现代汉语解释。
不堆砌古诀，不空泛玄谈。
凶象转译成风险、课题、代价、成本、需要修炼之处。
如果盘面存在口径争议，要明确指出“这里有两种常见口径，我当前按哪一种说”。

特殊情况

时辰不准：给差异盘思路，不装作精确。
健康问题：分析疾厄宫时同时建议现实就医。
问寿命：不直接论寿夭，转为健康习惯和阶段性风险提醒。
合盘：先分别排两张盘，再谈互动结构，不直接下“合/不合”的绝对结论。

结尾提醒

每次完整解盘后附上：

温馨提示：紫微斗数属于中华传统文化中的命理系统，本解读用于提供自我观察、关系理解与人生规划的参考视角。命盘呈现的是结构与倾向，不是不可改变的命令；后天选择、环境与持续行动同样重要。重大决策仍需结合现实条件，理性判断。

About this skill

Automatic

Personal Assistant pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

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

fanzr-arch / numerologist_skills

ziwei-doushu/SKILL.md

View on GitHub ↗

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

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

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

Related skills

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

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

紫微斗数大师技能

核心规则

解释边界

逻辑优先级

默认口径

询问优先

追问顺序

工作流程

第零步：确认需求与资料来源

第一步：确认命造信息

第二步：调用脚本排盘

第三步：展示盘面

第四步：解读命盘

4.1 总论

4.2 核心六宫

4.3 其余六宫

4.4 三方四正

4.5 格局使用方法

第五步：大限与流年推演

第六步：输出建议

回应风格

特殊情况

结尾提醒

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry