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Knowledge Base Management — 知识库全生命周期管理 · chubbyguan | DecimalAI

Documentationby chubbyguan

Knowledge Base Management — 知识库全生命周期管理

Obsidian 知识库全生命周期管理：三层架构、素材入库(ABC分级)、健康检查、GBrain/GraphRAG/LLM Wiki 三件套集成、目录整理

Maintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%86%of test cases passed +64 pts

Without skill5 of 22

With skill19 of 22

14 fixed5 passed either way3 still failing0 broke

Measured on gemini-3.6-flash9/25/2026v319 comparable

Track record

Agent runs: 0; All time
Forks: 2

See one task, with and without the skill

One test taskWe are writing a script to auto-fill missing `summary` metadata across all markdown files in our wiki by taking the first heading. Which folder tree contains ~260 auto-generated entity notes that should be explicitly excluded from this summary fix?

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 chubbyguan-knowledge-base-management
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("chubbyguan-knowledge-base-management")
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 chubbyguan-knowledge-base-management

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/chubbyguan-knowledge-base-management/SKILL.md https://app.decimal.ai/s/chubbyguan-knowledge-base-management/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/chubbyguan-knowledge-base-management/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

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

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

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add chubbyguan-knowledge-base-management

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

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add chubbyguan-knowledge-base-management

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("chubbyguan-knowledge-base-management")
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/chubbyguan-knowledge-base-management/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add chubbyguan-knowledge-base-management

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/chubbyguan-knowledge-base-management/SKILL.md https://app.decimal.ai/s/chubbyguan-knowledge-base-management/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/chubbyguan-knowledge-base-management/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/chubbyguan-knowledge-base-management/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/chubbyguan-knowledge-base-management.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3, tested 9/25/2026
Source: chubbyguan/chubbyskills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Knowledge Base Management — 知识库全生命周期管理

核心架构

三层架构

素材库/  → Layer 1: 不可变原始素材（AI 只读）
wiki/    → Layer 2: AI 编译的结构化知识（AI 维护）
产出/    → Layer 3: 按需生成的视图（不持久化）

核心原则：素材库是事实源（不可变），Wiki 是 AI 编译的投影（必须溯源），产出是按需生成的视图（不持久化）。

Vault 配置

vault 路径通过环境变量配置，不写死在脚本里：

export VAULT_DIR="$HOME/Documents/your-vault"   # 你的 Obsidian vault 根目录

完整仓库可以统一配置采集、搜索和资料包：

python3 tools/chubby.py init --vault "$VAULT_DIR"

这里的路径是知识库根目录。采集进入根目录的 00_Inbox，默认索引为 .chubby/index.sqlite。独立安装本 skill 后，tools/vault_index.py 和 tools/evidence_brief.py 直接使用 VAULT_DIR 或 --vault，不读取仓库中的 chubby.yaml。

健康检查可自行加入调度器；安装本 skill 不会创建 cron。

1. 素材入库 (Ingest)

新素材处理流水线

用户丢素材 →
  ① 判断类型 → 存入素材库/对应子目录
  ② 提取关键信息 → 决定是否建 wiki 页面
  ③ 如果值得：建选题（打 SHARP 分）或更新项目页
  ④ 更新 wiki/index.md 索引

素材分级 (ABC Grading)

等级	标准	处理
A	核心方法论/框架/系统	优先编译为选题
B	有洞察但不够系统	备选，作为 A 的补充
C	水货/重复/与定位无关	归档，不主动编译

公众号文章处理

来源一：可选的 wechat-article-exporter 外部同步 如果已自行部署该工具和调度器，可把公众号文章同步到 素材库/公众号文章/<公众号名>/。本 skill 不内置该服务或定时任务。

来源二：手机保存的文章导入 通过手机保存的文章先落到一个待处理目录（按日期），再由一个归类脚本搬入素材库并按领域筛选优质文章做 A+B 深度处理。

归类脚本（如 sync_notes_to_kb.py）与各人的目录结构强相关，本仓库不内置，按下面的「工作流」自建即可。

工作流：

新文章入库 →
  ① 脚本自动归类到 素材库/公众号文章/<公众号名>/
  ② URL 去重（跳过已存在的文章）
  ③ 领域关键词筛选（AI/半导体/消费/电商/品牌/投资/Agent/Skill）
     跳过纯营销、与用户领域无关的文章
  ④ 命中关键词的精选文章 → A+B 双轨处理（每日上限 5 篇）
     A层：观点提取+分类+3条Takeaway → wiki/📡 外部输入/公众号/<公众号名>/<主题>/
     B层：问题链+伴读引导 → 同上
  ⑤ 更新 wiki/index.md（外部输入 — 最新 A+B 处理 表格）

关键原则：

聚焦高价值主题（匹配用户兴趣），不追求全量覆盖
每个 wiki 页面必须用 source frontmatter 列出引用素材路径
深度处理上限 5 篇/天，分批消化
文章路径格式：[[../../../../素材库/公众号文章/<公众号>/<文件名>]]

2. 健康检查 (Audit)

自动检查项

手动运行脚本或配置自己的调度器后，检查：

断链（broken wikilinks）
缺 source 字段
frontmatter 缺失
待沉淀概念
低链接密度

脚本：scripts/vault_health_check.py（本仓库提供，零依赖，纯标准库）

# 指定 vault 路径
python3 scripts/vault_health_check.py "$HOME/Documents/your-vault"

# 或读 VAULT_DIR 环境变量
export VAULT_DIR="$HOME/Documents/your-vault"
python3 scripts/vault_health_check.py --json

# 把缺 source 字段也视为问题（强溯源场景）
python3 scripts/vault_health_check.py --require-source

检查项：断链、缺 frontmatter/source/summary、孤立笔记、空目录、内容重复、文件名含空格。存在断链时退出码为 1，方便接入 cron / CI 告警。

清理标准

操作	规则
去重	SHA256 或 diff 确认相同后删除副本
归位	散落文件按类型移到素材库/对应子目录
归档	历史版本移到 `wiki/🗄️ 归档/`，不删除
空目录	直接删除。含 redirect README 的 legacy 目录也算空目录
全库审计	执行第 6 节「全库审计与批量归档」
目录审查	执行第 5 节「目录审查与增量清理」

文件命名规范

✅ 选题-AI-Agent-未来方向.md    # 连字符连接，无空格
❌ 选题-DemisHassabis Agent.md  # 含空格，wikilink 断裂

迁移提示页格式

当文档内容迁移到新位置时，旧文件不删除，改为迁移提示：

---
title: ⚠️ 已迁移 - 原文件名（旧版 vX.0）
type: note
tags: [已废弃, 旧版, 已迁移]
created: YYYY-MM-DD
summary: 已被 [[新位置/新文件名|新版本]] 取代。
---

关键规则：

frontmatter: ⚠️ 已迁移 标题 + 已废弃 标签
说明为什么旧版不再适用（1-3条）
明确的新链接
不要保留旧的正文内容

3. 工具集成 (Tools)

🧠 增量索引与统一搜索

完整仓库的统一入口：

python3 tools/chubby.py init --vault "$VAULT_DIR"
python3 tools/chubby.py ingest "替换为真实素材链接" --no-enrich
python3 tools/chubby.py search "AI Agent"
python3 tools/chubby.py search "内容策略" --mode lite

成功采集后，以及统一搜索前，自动同步索引。init --vault 指向根目录；旧的 ingest --vault 参数仍表示具体入库目录。

独立安装本 skill 后，在 skill 目录内使用自带工具：

export VAULT_DIR="$HOME/Documents/your-vault"
python3 tools/vault_index.py index "$VAULT_DIR"
python3 tools/vault_index.py search "AI Agent"
python3 tools/vault_index.py semantic "内容策略"
python3 tools/vault_index.py search "品牌" --platform wechat
python3 tools/vault_index.py recent --limit 10
python3 tools/vault_index.py read "10_Sources/x/example.md" --vault "$VAULT_DIR"
python3 tools/vault_index.py stats

独立 vault_index.py 搜索读取已有索引，笔记变化后先再运行 index。后续命令需要保持 VAULT_DIR，或显式传入同一个数据库：

python3 tools/vault_index.py --db /path/to/index.sqlite index "$VAULT_DIR"
python3 tools/vault_index.py --db /path/to/index.sqlite search "内容策略"

SQLite FTS5 可用时自动启用；不可用时使用 LIKE 搜索。默认 semantic-lite 只做本地词语和字符组合排序，不调用模型 API。

索引规则：

默认位置为 $VAULT_DIR/.chubby/index.sqlite；VAULT_INDEX_DB 或显式 --db 可以覆盖。
index 默认增量同步。未变笔记的向量保留，实际 embedding 输入变化时使旧向量失效，删除笔记时移除对应索引和向量。
新默认索引不存在时，旧 $VAULT_DIR/.chubby/vault_index.sqlite 会复制迁移，原文件保留。其他位置的旧数据库需显式指定并验证所属知识库。
数据库绑定知识库，扫描或读取失败会回滚同步，越出知识库范围的符号链接会被拒绝。
全量重建需显式执行 python3 tools/vault_index.py index "$VAULT_DIR" --rebuild，会丢弃已有 embedding。

📥 导入本地材料

完整仓库中，python3 tools/chubby.py import "/path/to/document.md" 将 Markdown、文本或文字层 PDF 接入现有采集、复用和索引流程。可用 --source-url 指定原始网页；本地附件会复制并改写引用，原文件保留。PDF 需要可选 pymupdf，扫描件不会被当作成功导入。

独立安装本 skill 后，也可在 skill 目录使用：

python3 tools/import_document.py "/path/to/document.md" --output "$VAULT_DIR/00_Inbox"
python3 tools/vault_index.py index "$VAULT_DIR"

独立导入工具不写统一 CLI 的运行记录；MCP 查询或 brief 生成前仍会自动同步知识库。安装包包含该工具及所需公共模块。

📎 逐字原文资料包

完整仓库使用统一入口：

python3 tools/chubby.py brief --topic "内容策略" --output reports/content-brief.md

独立安装后，在 skill 目录运行：

python3 tools/evidence_brief.py --vault "$VAULT_DIR" --topic "内容策略" \
  --output "$VAULT_DIR/30_Output/content-brief.md"

资料包工具先自动同步索引，关键词检索没有命中时回退到本地 semantic-lite；生成的 research_brief 文档从候选中排除。输出 Markdown 与同名 JSON，包含逐字摘录、原始 source、笔记相对路径、真实文件行号和 SHA-256。

行号从源文件第一行计算，包含 frontmatter。导出前再次检查文件和摘录，来源变动时需要重新生成。已有输出默认拒绝覆盖，显式 --force 才替换。没有匹配内容时写明证据不足。

工具只整理原文证据包和 Agent 任务说明，选题由 Agent 另行生成；文件和摘录核验不代表原文事实已证实。要求 Agent 区分原作者观点与推断，并在引用中保留证据编号、路径和行号。

🗂️ 自动归档与知识卡片

tools/vault_curator.py 默认预览，确认后用 --apply 执行：

python3 tools/vault_curator.py archive "$VAULT_DIR"
python3 tools/vault_curator.py archive "$VAULT_DIR" --apply
python3 tools/vault_curator.py card "$VAULT_DIR" "10_Sources/x/example.md" --apply

archive 只处理 00_Inbox/**/*.md，按平台、摘要和 processed 标签归入 10_Sources/<platform>/ 或 20_Processed/。
card 生成 20_Processed/Cards/*.md，保留来源、摘要、要点和 tags。

🔌 MCP Server

scripts/mcp_server.py 为支持 MCP 的 Agent 提供搜索、读取和索引工具。使用 Python 3.10 或更新版本，在完整仓库根目录执行：

python3 -m pip install -r knowledge-base-management/requirements-mcp.txt
python3 tools/mcp_smoke.py --json
VAULT_DIR=/path/to/your-vault python3 knowledge-base-management/scripts/mcp_server.py

当前验证 SDK 为 mcp==1.30.0。mcp_smoke.py 用临时 vault 启动真实 stdio 服务，验证握手、6 个工具发现、搜索和读取，不访问你的知识库。--help 不需要安装 SDK。

完整仓库的安装器会一起复制 vault_index.py、vault_curator.py、evidence_brief.py、import_document.py 及所需公共模块：

python3 tools/install_skill.py knowledge-base-management --dest ~/.codex/skills
python3 -m pip install -r ~/.codex/skills/knowledge-base-management/requirements-mcp.txt
VAULT_DIR=/path/to/your-vault python3 ~/.codex/skills/knowledge-base-management/scripts/mcp_server.py

目标技能目录必须不存在；已有安装先备份，或使用新的安装目录。MCP 优先加载 skill 内的工具，安装后不依赖原仓库位置。

客户端配置使用真实的 Python 和 server 绝对路径：

{
  "mcpServers": {
    "chubby-kb": {
      "command": "/absolute/path/to/venv/bin/python",
      "args": ["/absolute/path/to/knowledge-base-management/scripts/mcp_server.py"],
      "env": { "VAULT_DIR": "/path/to/your-vault" }
    }
  }
}

工具名保持 6 个：search_vault、semantic_search_vault、read_kb_note、list_recent_notes、reindex_vault、vault_index_stats。搜索、语义检索、最近笔记和统计前自动同步索引；read_kb_note 直接读取原文；reindex_vault() 名称不变，执行增量同步。

默认数据库与 CLI 一致，为根目录下 .chubby/index.sqlite；自定义时在客户端环境中设置 VAULT_INDEX_DB。读取路径必须位于 VAULT_DIR 范围内。

以下均为可选的第三方/外部工具，不随本仓库提供。路径（如 ~/graphrag-poc/、 ~/.hermes/...）是作者本机的示例位置，请替换为你自己的安装路径。没有它们也不影响第 1、2 节的核心流程与 scripts/vault_health_check.py。

知识库三件套

工具	定位	适用场景
GBrain	搜索	知道要找什么关键词
GraphRAG	发现	不知道关键词，想发现隐藏关联
LLM Wiki	写作	用 Karpathy 模式建 LLM 可读的 Wiki
Understand-Anything	可视化	看懂全库结构，发现隐性关联

GBrain 搜索集成

日常使用：

alias gs='GBRAIN_SKIP_RECIPES=1 gbrain search'
gs "定价 品牌"
gs "奥德赛时期"

# 混合搜索
gbrain query "消费者心理剩余"

# 读全文（注意 slug 含 emoji 目录名）
gbrain get "wiki/📡 外部输入/播客/XXX/观点提取"

# 查看统计
gbrain stats

配置：DeepSeek downstream LLM（deepseek_api_key + deepseek_model: deepseek-chat + search.mode: balanced） 完整手册：wiki/🧠 AI系统/GBrain-完整操作手册.md

GraphRAG 发现

⚠️ 已移至独立项目：按你的实际安装路径配置 Flask API：localhost:8999（默认）内容：自定义知识库 + DeepSeek + BGE 嵌入

用于发现知识库中的隐性关联和跨领域连接。

LLM Wiki（Karpathy 模式）

Karpathy 的 LLM Wiki 模式：构建 interlinked markdown 知识库，让 LLM 在推理时直接查。已集成到 Obsidian vault。

Understand-Anything — 知识图谱可视化

已安装为独立 skill。

使用：

# 必须指向 wiki 根目录，必须用 python3.11+
python3 parse-knowledge-base.py /path/to/your-vault/wiki

⚠️ 关键 pitfalls：

Python 3.11+ 必需
必须指向 wiki 根目录（子目录没有 index.md 会报错）
Dashboard 需先 build core 包
需要 GRAPH_DIR 和 ACCESS_TOKEN 环境变量

4. 目录整理 (Organization)

目录审查与增量清理

当用户说"看一下目录是否合理"时执行：

Step 1: 全量目录扫描
  → find . -maxdepth 3 -type d | sort
  → 对每个 wiki 子目录统计 .md 文件数

Step 2: 逐项检查
  | 检查项 | 特征 | 严重性 |
  |--------|------|--------|
  | 旧版残留目录 | diff -rq 确认新旧版本内容不同 | 🔴 |
  | Legacy redirect 只剩 README | 目录只有一个 redirect 文件 | 🟡 |
  | 概念卡片散落根目录 | wiki/🧬 知识图谱/XXX.md 应在概念/下 | 🟡 |
  | cron 脚本写入旧路径 | grep -rn "旧目录" ~/.hermes/scripts/ | 🔴 |

Step 3: 分类执行 → 归档/删除/移动/更新引用

Step 4: 验证 → 零残留

全库审计与批量归档

Step 1: 全量扫描目录结构和文件数
Step 2: 逐目录与最新管理办法对照
Step 3: 分级标记（🔴明确无用 → 🟡重复 → 🟠草稿 → 🔵过时）
Step 4: 批量 mv 到 wiki/🗄️ 归档/
Step 5: 修复残留引用（README/wiki/index/项目索引）

归档目录结构：

wiki/🗄️ 归档/
├── 归档清单-YYYY-MM-DD.md
├── 旧版管理办法/
├── 旧版流程/
├── 旧版草稿/
├── 旧选题/
└── ...

反模式：

❌ 不要删除归档目录下的文件
❌ 归档后必须同步更新所有 index/README 引用
❌ 素材库/ 下的文件不归档
❌ 归档时不要改文件名
✅ 每次大清理必须创建 归档清单-YYYY-MM-DD.md

5. Wiki 实体管理

Wiki 实体格式

每个团队/项目在 wiki/teams/ 下有对应 .md 实体：

---
title: 团队名
created: YYYY-MM-DD
updated: YYYY-MM-DD
type: entity
tags: [team, ...]
sources: [50.团队/实际目录名]   # 重要：路径必须是实际存在的目录名
---

# 团队名

## Overview
## 文件结构（表格，列出子目录和数量）
## Related

sources 路径必须与实际目录名完全匹配：

❌ sources: [50.团队/02.事业线] （旧/错）
✅ sources: [50.团队/事业线] （新/对）

异常文件处理

某些 .md 文件可能被误认为目录名（如 千金-日报索引.md、OKR看板.md）。检查方式：

# 检查是否是文件被当作目录
for d in team_dir.iterdir():
    if d.is_file() and not d.name.startswith("."):
        print(f"⚠️ {d.name} 是文件却像目录")

修复：用 mv 把它们移到对应目录下。

6. 批量修复模式

broken wikilink 修复（播客 EP 文件）

问题：播客 wiki 文件中的 wikilink 引用素材库 EP 文件，因文件名含特殊字符（引号、emoji、反斜杠）导致相对路径解析失败。

修复方式：

# 1. 建索引: EP编号 → 实际文件路径
ep_index = {}
for f in 素材库_dir.iterdir():
    ep_match = re.match(r'(EP\d+)', f.name)
    if ep_match:
        ep_index[ep_match.group(1)] = f

# 2. 扫描 wiki 文件，用 EP 编号匹配实际文件，重建相对路径
for link in wikilinks:
    ep_match = re.search(r'(EP\d+)', target)
    if ep_match and ep_match.group(1) in ep_index:
        rel = os.path.relpath(ep_index[ep_id], md.parent)
        # 替换 wikilink

批量补 frontmatter（summary + source）

问题：大量 A+B 产物（观点提取/问题链）缺少 summary 和 source 字段。

修复方式：

# summary: 从正文第一个 # 标题提取
title_match = re.search(r'^#\s+(.+)', body, re.MULTILINE)
summary = title_match.group(1).strip()[:100] if title_match else md.stem

# source: 从文件路径推断
if '观点提取' in md.name or '问题链' in md.name:
    source = '素材库/播客/给女孩的商业第一课'

⚠️ PITFALL：GraphRAG 自动生成的实体文件（🧬 知识图谱/GraphRAG*/实体/）约 260+ 个没有 summary，这是正常的，不需要修复。

文件名含空格修复

# 扫描
find wiki/ -name "* *" -type f

# 重命名
mv "MarkItDown 操作手册.md" "MarkItDown-操作手册.md"

目录迁移提示页格式

当目录整体迁移时，旧位置保留 README.md 提示页：

---
title: "⚠️ 已迁移 - 目录名"
type: note
tags: [已废弃, 已迁移]
created: YYYY-MM-DD
summary: 已迁移到 ../../新位置。
source: "原系统名"
---

7. 事业线文件命名规范

事业线（50.团队/事业线/）的文件命名与 Agent 的对应关系：

文件名前缀	Agent	应放入子目录
`O1-猎手` / `猎手-内容情报` / `猎手-渠道情报` / `猎手-技术调研` / `内容情报-给女孩的商业第一课`	猎手	`猎手/`
`O2-笔神`	笔神	`笔神/`
`O2-天眼`	天眼	`天眼/`
`O3-管家`	管家	`管家/`
`O4-极客`	极客	`极客/`
`O4-画神`	画神	`画神/`
`千金-`	千金	`千金/`

特别注意：内容情报-给女孩的商业第一课-*.md 这些文件名（87个）不是系列名，是猎手的产出，需归入 猎手/ 目录。

O3-CFO 文件：属于 家庭线/CFO/，不是事业线。

8. 千金日报文件命名规范

文件名包含	子目录
`总裁日报`	`总裁日报/`
`O1线`/`O2线`/`O3线`/`O4线`	`O线日报/`
`晨间简报`	`晨间简报/`
`浪漫-`	`浪漫/`
`系统-`	`系统/`
其他	`其他/`

反模式

❌ 不要删除素材库/ 下的任何文件
❌ Wiki 页面必须有 source 字段指向原始素材
❌ 不要创建没有入链的孤立 wiki 页面（除索引页）
❌ 清理前先 dry-run
❌ 归档时不要改文件名
✅ 每次大清理必须创建 归档清单-YYYY-MM-DD.md

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Knowledge Base Management — 知识库全生命周期管理

核心架构

三层架构

Vault 配置

1. 素材入库 (Ingest)

新素材处理流水线

素材分级 (ABC Grading)

公众号文章处理

2. 健康检查 (Audit)

自动检查项

清理标准

文件命名规范

迁移提示页格式

3. 工具集成 (Tools)

🧠 增量索引与统一搜索

📥 导入本地材料

📎 逐字原文资料包

🗂️ 自动归档与知识卡片

🔌 MCP Server

知识库三件套

GBrain 搜索集成

GraphRAG 发现

LLM Wiki（Karpathy 模式）

Understand-Anything — 知识图谱可视化

4. 目录整理 (Organization)

目录审查与增量清理

全库审计与批量归档

5. Wiki 实体管理

Wiki 实体格式

异常文件处理

6. 批量修复模式

broken wikilink 修复（播客 EP 文件）

批量补 frontmatter（summary + source）

文件名含空格修复

目录迁移提示页格式

7. 事业线文件命名规范

8. 千金日报文件命名规范

反模式

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