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⚠️ CRITICAL RULES · leoyeai | DecimalAI

Agent Toolingby leoyeai

⚠️ CRITICAL RULES

Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.

SkillScore

SkillScore45/100

Test results 45

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%45%of test cases passed +23 pts

Without skill5 of 22

With skill10 of 22

6 fixed4 passed either way11 still failing1 broke

Measured on gemini-3.6-flash8/8/2026v114 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe want to install the inkos package from clawhub specifically for Feishu group oc_12345 without affecting other chats. What exact command should be run?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add leoyeai-files-memory-system
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install leoyeai-files-memory-system

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/leoyeai-files-memory-system/SKILL.md https://app.decimal.ai/s/leoyeai-files-memory-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-files-memory-system/SKILL.md

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

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

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

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

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add leoyeai-files-memory-system

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add leoyeai-files-memory-system

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("leoyeai-files-memory-system")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-files-memory-system/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 10 other files are not included.

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add leoyeai-files-memory-system

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/leoyeai-files-memory-system/SKILL.md https://app.decimal.ai/s/leoyeai-files-memory-system/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-files-memory-system/SKILL.md

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/leoyeai-files-memory-system/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-files-memory-system.json

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

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

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

⚠️ CRITICAL RULES

群聊中克隆仓库的硬性规定

❌ 禁止：直接使用 git clone 命令 ✅ 必须：使用 auto-clone.sh 脚本并传入 --group 参数

当用户在群聊中要求"克隆仓库"时：

不要执行 git clone <url> → 这会克隆到 /workspace/repos/
必须执行 ./skills/files-memory-system/scripts/auto-clone.sh --group feishu <group_id> <url> → 这会克隆到群组目录

Group ID 获取方式：从 metadata 中的 conversation_label 字段获取（如 oc_a2b821...）

🔴 会话开始强制检查清单 (群聊)

OpenClaw Bug: 群聊不会自动加载群记忆文件

每次在群聊中收到消息时，必须执行以下检查：

检查 metadata.is_group_chat:
├── IF true (群聊):
│   ├── 读取 memory/group_<channel>_<conversation_label>/GLOBAL.md
│   ├── 读取 memory/group_<channel>_<conversation_label>/YYYY-MM-DD.md (today)
│   └── 读取 memory/global/GLOBAL.md
│
└── IF false (私聊):
    ├── 读取 memory/private/YYYY-MM-DD.md (today)
    └── 读取 memory/global/GLOBAL.md

禁止在群聊中执行任何操作前跳过此检查！

Memory System

📖 新用户？ 请先阅读用户使用指南了解如何快速上手！

Installation

Method 1: Auto-Install with Self-Registration (Recommended)

cd ~/.openclaw/skills/files-memory-system
./scripts/install.sh

This will:

✅ Copy skill to /workspace/skills/files-memory-system/ (standard location)
✅ Auto-register in AGENTS.md so the agent knows this skill exists
✅ Enable auto-discovery on every agent startup

Method 2: Manual Install

# 1. Copy to standard location
mkdir -p /workspace/skills
cp -r ~/.openclaw/skills/files-memory-system /workspace/skills/

# 2. Run self-registration
cd /workspace/skills/files-memory-system
./scripts/post-install.sh

Method 3: Using clawhub

cd /workspace
clawhub install files-memory-system
# Then manually run:
./skills/files-memory-system/scripts/post-install.sh

Self-Registration Mechanism

This skill implements auto-declaration - it automatically adds a section to AGENTS.md upon installation. This ensures:

The agent knows files-memory-system exists without being told
Memory locations are documented where the agent reads
No manual configuration needed after installation

To verify registration:

grep -A 20 "files-memory-system" /workspace/AGENTS.md

To unregister (remove from AGENTS.md):

sed -i '/<!-- files-memory-system: installed -->/,/<!-- files-memory-system: end -->/d' /workspace/AGENTS.md

Overview

This skill provides a complete memory management system that enables OpenClaw agents to maintain context across multiple chat groups while keeping group-specific memories and skills isolated. It includes:

Group-isolated memory: Separate memory directories per group/channel
Group-isolated skills: Skills installed per group, preventing cross-group pollution
Global shared memory: Cross-group knowledge in memory/global/
Long-term memory: MEMORY.md for curated, permanent memories
Workspace organization: Structured projects/ and repos/ directories

Session Start - Automatic Memory Loading

⚠️ 重要: 每次进入会话时，必须自动加载对应的记忆！

群聊中的自动加载流程

当收到群聊消息时，按以下顺序加载记忆：

读取群组 GLOBAL.md - 获取群组关键信息和资源索引
```
memory/group_<channel>_<id>/GLOBAL.md
```

读取今日和昨日记录 - 获取最近的对话上下文

memory/group_<channel>_<id>/YYYY-MM-DD.md (today)
memory/group_<channel>_<id>/YYYY-MM-DD.md (yesterday)

读取跨群组全局记忆 - 获取跨群组共享的信息
```
memory/global/GLOBAL.md
```

私聊中的自动加载流程

读取私聊记录 - 今日和昨日

memory/private/YYYY-MM-DD.md (today)
memory/private/YYYY-MM-DD.md (yesterday)

读取跨群组全局记忆
```
memory/global/GLOBAL.md
```
读取长期精华记忆 (仅私聊)
```
MEMORY.md
```

记忆加载检查清单

群聊中必须加载：

当前群组的 GLOBAL.md
今天的群组日志 (YYYY-MM-DD.md)
昨天的群组日志 (回顾上下文)
跨群组全局记忆 (memory/global/GLOBAL.md)

私聊中必须加载：

私聊今日和昨日日志
跨群组全局记忆 (memory/global/GLOBAL.md)
MEMORY.md (仅私聊)

Usage Scenarios

Scenario 1: User Says "Clone This Repo" in a Group Chat

Context: User in group "oc_a2b821..." says: "Clone https://github.com/user/project"

Automatic Actions:

Detect current group context: group_feishu_oc_a2b821...
Clone to: memory/group_feishu_oc_a2b821.../repos/project/
Update group's GLOBAL.md with project info
Confirm: "✅ Cloned to group repos/project/"

Manual Commands:

# Option 1: Use auto-clone script with --group parameter (推荐)
./scripts/auto-clone.sh --group feishu oc_a2b821... https://github.com/user/project

# Option 2: Direct git clone
cd memory/group_feishu_xxx/repos
git clone https://github.com/user/project

# Option 3: Using environment variables (向后兼容)
GROUP_ID="oc_xxx" CHANNEL="feishu" ./scripts/auto-clone.sh https://github.com/user/project

Agent Implementation (直接执行，无需用户交互):

当用户要求克隆仓库时，先判断当前是否在群聊中（查看 inbound metadata 中的 is_group_chat 字段），然后：

如果是群聊 (is_group_chat: true):

从 metadata 读取 conversation_label 作为 group_id
使用 --group 参数执行克隆：

./skills/files-memory-system/scripts/auto-clone.sh \
    --group feishu "<conversation_label>" \
    "https://github.com/user/project"

如果是私聊 (is_group_chat: false):

./skills/files-memory-system/scripts/auto-clone.sh \
    --private \
    "https://github.com/user/project"

关键要点：

⚠️ 群聊中必须使用 --group 参数，否则仓库会克隆到全局目录
--group <channel> <group_id> 必须在 URL 之前
群组ID从 conversation_label 获取 (如 oc_a2b821...)
脚本会自动创建缺失的群组记忆目录
如果用户没指定项目名称，脚本会自动从 URL 提取

After Cloning - Update GLOBAL.md:

## 群组项目 (repos/)

| 项目名称 | 类型 | 描述 | 位置 |
|---------|------|------|------|
| project | cloned | 克隆的示例项目 | repos/project/ |

Scenario 2: Create New Project in Group Context

Context: User says: "Create a new tool for our group"

Automatic Actions:

Create project directory: memory/group_xxx/repos/my-tool/
Initialize with template if needed
Update GLOBAL.md
Start working in the group-specific location

Manual Commands:

# Create new project in group context
mkdir -p memory/group_feishu_xxx/repos/my-tool
cd memory/group_feishu_xxx/repos/my-tool
# ... create files ...

Scenario 3: Install Skill for Specific Group

Context: User says: "Install inkos skill for this group only"

Automatic Actions:

Install to: memory/group_xxx/skills/inkos/
Update group's GLOBAL.md
Skill is isolated to this group only

Manual Commands:

# Method 1: Using clawhub with --dir
clawhub install inkos --dir memory/group_feishu_xxx/skills

# Method 2: Manual copy
mkdir -p memory/group_feishu_xxx/skills/inkos
cp -r inkos/* memory/group_feishu_xxx/skills/inkos/

Update GLOBAL.md:

## 已安装的群组专属 Skills

| Skill | 版本 | 来源 | 描述 |
|-------|------|------|------|
| inkos | 1.0.0 | clawhub | 小说写作工具 |

Scenario 4: Record Information to Group Memory ⭐

Context: User in group chat says: "记录到群记忆里: 项目信息" / "记录到全局记忆里: API密钥" / "记住这个"

Automatic Actions (Agent Must Do):

Detect the intent: User wants to record information to group memory
Determine target location (按优先级匹配):
- If "记录到群记忆里" → memory/group_<channel>_<id>/GLOBAL.md
- If "记录到全局记忆里"/"记录到跨群记忆里" → memory/global/GLOBAL.md
- If "记录到私聊里" → memory/private/GLOBAL.md
- If "群文档"/"群组" (模糊指令) → memory/group_<channel>_<id>/GLOBAL.md
Write to file immediately: Do not just say "I remember", actually write to file!
Confirm: "✅ 已记录到群记忆" / "✅ 已保存到全局记忆"

Example 1: Record Project Info

User: "记录到群记忆里: 项目信息"
项目名称: MyApp
技术栈: React + Node.js
负责人: 张三

Agent Action:
1. Detect "记录到群记忆里" → Target: memory/group_xxx/GLOBAL.md
2. Format as table and append:

## 活跃项目

| 项目名称 | 技术栈 | 负责人 | 更新时间 |
|---------|--------|--------|----------|
| MyApp | React + Node.js | 张三 | 2026-03-23 |

3. Confirm: "✅ 已记录到群记忆"

Example 2: Record API Key (⚠️ Not Recommended)

User: "记录到全局记忆里：API密钥 sk-abc123"

⚠️ **SECURITY WARNING**:
Storing API keys in plain text in memory files is NOT recommended.
**Recommended approach: Use environment variables**

export API_KEY="sk-xxx"

Alternative (if environment variables not possible):
- Use a secrets manager
- Use encrypted storage

If user insists on storing in memory:
1. Warn: "⚠️ 存储 API 密钥到记忆文件存在安全风险，建议仅用于测试环境"
2. Detect target location based on user command
3. Format as table and append
4. Confirm: "✅ 已记录 (⚠️ 安全风险)"

Recommended User Commands:

"记录到群记忆里: [内容]" → 仅当前群可见
"记录到全局记忆里: [内容]" → 所有群共享
"记录到跨群记忆里: [内容]" → 同全局记忆
"记录到私聊里: [内容]" → 仅私聊可见

Critical Rules:

❌ DO NOT just say "I remember" without writing to file
❌ DO NOT store sensitive info without clarifying scope
✅ ALWAYS confirm the location where info is stored
✅ Use tables in GLOBAL.md for structured info
✅ Add timestamps for time-sensitive info

Scenario 5: Create Files in Group Chat ⭐

Context: User in group chat says: "帮我写一篇文章" / "创建一个文档" / "生成xxx文件"

Automatic Actions (Agent Must Do):

Detect the intent: User wants to create a file in group context
Determine correct location:
- Default: memory/group_<channel>_<id>/articles/ (for articles/docs)
- Code: memory/group_<channel>_<id>/repos/ (for code projects)
Create the file: Write to the correct group-specific path
Auto-update GLOBAL.md: ⚠️ CRITICAL - After creating file, auto-append to GLOBAL.md
Confirm: "✅ 已创建并记录到群文档"

Scenario 6: Working Across Multiple Groups

Context: Same user in different groups

Group A (小说创作):

Skills: memory/group_A/skills/inkos/
Projects: memory/group_A/repos/novel-tools/

Group B (编程学习):

Skills: memory/group_B/skills/code-helper/
Projects: memory/group_B/repos/exercises/

Behavior:

In Group A: Only sees inkos, not code-helper
In Group B: Only sees code-helper, not inkos
No pollution between groups!

Scenario 5: Migrate Information to Global

Context: Information becomes relevant to multiple groups

Migration Process:

Move from: memory/group_A/GLOBAL.md
To: memory/global/GLOBAL.md
Update Group A with reference
All groups can now access

Example:

# Original in group_A/GLOBAL.md
## Project Configuration
Config: xxx

# After migration - in global/GLOBAL.md
## Shared Project Configuration
Config: xxx

# In group_A/GLOBAL.md - add reference
## 跨群组资源
See: memory/global/GLOBAL.md

⚠️ Security Note: Do NOT migrate API keys, passwords, or other sensitive credentials to global memory. Keep secrets in environment variables or private memory only.

Memory Architecture

Directory Structure

/workspace/
├── memory/
│   ├── global/
│   │   ├── GLOBAL.md              # Shared key information
│   │   └── YYYY-MM-DD.md          # Daily logs
│   ├── group_feishu_<id>/         # Feishu group memories
│   │   ├── GLOBAL.md              # Group key information
│   │   ├── YYYY-MM-DD.md          # Daily logs
│   │   ├── skills/                # ⭐ Group-specific skills
│   │   └── repos/                 # ⭐ Group-specific projects
│   └── private/
│       ├── GLOBAL.md
│       └── YYYY-MM-DD.md
├── MEMORY.md
├── skills/                        # Global shared skills
├── projects/
└── repos/

Automation Scripts

scripts/init-group-memory.sh - Initialize group memory structure:

./scripts/init-group-memory.sh feishu oc_xxx "小说创作群组"

scripts/ensure-group-memory.sh - Ensure group memory directory exists (on-demand):

# Auto-initializes group memory with all subdirectories
./scripts/ensure-group-memory.sh feishu oc_xxx "小说创作群组"

scripts/ensure-private-memory.sh - Ensure private memory directory exists:

# Auto-initializes private memory when in private chat context
./scripts/ensure-private-memory.sh

scripts/ensure-global-memory.sh - Ensure global memory directory exists:

# Auto-initializes global memory for cross-group information
./scripts/ensure-global-memory.sh

scripts/auto-clone.sh - Clone repo to correct location:

# Auto-detects context and clones to appropriate location
./scripts/auto-clone.sh https://github.com/user/repo

Group-Isolated Skills System

Scope	Location	Access	Priority
Group-specific	`memory/group_<id>/skills/`	Only current group	High
Global	`/workspace/skills/`	All groups	Low

Promoting Cross-Group Memory Awareness

1. Include Reference in Group GLOBAL.md

Every group's GLOBAL.md should include:

## Cross-Group Resources

📚 **Cross-Group Global Memory**: `memory/global/GLOBAL.md`

Contains information shared across all groups.

2. Key Information to Store Globally

Relevant to multiple groups
Common standards or conventions
Shared resources (non-sensitive)
Tool documentation and links

⚠️ Security Note: Do NOT store API keys, passwords, or other sensitive credentials in global memory. Use environment variables or private memory for secrets.

3. Migration from Group to Global

When information becomes relevant to multiple groups:

Move to memory/global/GLOBAL.md
Update original with reference link
Notify affected groups

Memory Auto-Initialization

All memory directories are automatically initialized using a dual approach (A + B):

Approach A: Installation-Time Setup

When installing files-memory-system, the following directories are automatically created:

memory/private/ with GLOBAL.md
memory/global/ with GLOBAL.md

Approach B: On-Demand Setup

When using auto-clone.sh or other scripts, the system automatically detects and initializes missing directories:

Group context: Calls ensure-group-memory.sh
Private context: Calls ensure-private-memory.sh
Global operations: Calls ensure-global-memory.sh

This ensures all memory locations are always available when needed, even if:

The installation step was skipped
The directory was accidentally deleted
Working in a new workspace

Memory Directory Comparison

Feature	Private Memory	Group Memory	Global Memory
Location	`memory/private/`	`memory/group_<id>/`	`memory/global/`
Auto-init A	✅ Install-time	❌ No	✅ Install-time
Auto-init B	✅ On-demand	✅ On-demand	✅ On-demand
Skills	Uses global	Can isolate	N/A
Projects	Uses global	Can isolate	N/A
Scope	1-on-1 chats	Specific group	All groups
Ensure Script	`ensure-private-memory.sh`	`ensure-group-memory.sh`	`ensure-global-memory.sh`
Scope	1-on-1 chats only	Specific to one group
Auto-init	✅ Yes (A+B)	✅ Yes (B only)

Security Notes

MEMORY.md only loaded in private chats
Group skills completely isolated
Preserve group memory on disband

When it activates

Automatically, when a task matches. Use when initializing or managing memory systems for multi-channel deployments, creating group-specific memory directories, setting up MEMORY.md for long-term cross-group memories, organizing workspace directories (projects/repos), cloning repositories to group-isolated locations, managing group-isolated skills, or handling any

About this skill

Automatic

Knowledge Management pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/files-memory-system/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 10 files · scripts

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SkillScore

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What it changes

Track record

Use this skill

⚠️ CRITICAL RULES

群聊中克隆仓库的硬性规定

🔴 会话开始强制检查清单 (群聊)

Memory System

Installation

Method 1: Auto-Install with Self-Registration (Recommended)

Method 2: Manual Install

Method 3: Using clawhub

Self-Registration Mechanism

Overview

Session Start - Automatic Memory Loading

群聊中的自动加载流程

私聊中的自动加载流程

记忆加载检查清单

Usage Scenarios

Scenario 1: User Says "Clone This Repo" in a Group Chat

Scenario 2: Create New Project in Group Context

Scenario 3: Install Skill for Specific Group

Scenario 4: Record Information to Group Memory ⭐

Scenario 5: Create Files in Group Chat ⭐

Scenario 6: Working Across Multiple Groups

Scenario 5: Migrate Information to Global

Memory Architecture

Directory Structure

Automation Scripts

Group-Isolated Skills System

Promoting Cross-Group Memory Awareness

1. Include Reference in Group GLOBAL.md

2. Key Information to Store Globally

3. Migration from Group to Global

Memory Auto-Initialization

Approach A: Installation-Time Setup

Approach B: On-Demand Setup

Memory Directory Comparison

Security Notes

When it activates

SkillSafety

Source

Related skills

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