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BrainX V5 — The First Brain for OpenClaw · leoyeai | DecimalAI

Agent Toolingby leoyeai

BrainX V5 — The First Brain for OpenClaw

Vector memory engine with PostgreSQL + pgvector + OpenAI embeddings. Stores, searches, and injects contextual memories into LLM prompts. Includes auto-injection hook for OpenClaw and full backup/recovery system.

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%77%of test cases passed +45 pts

Without skill7 of 22

With skill17 of 22

11 fixed6 passed either way4 still failing1 broke

Measured on gemini-3.6-flash8/6/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhich script in the processing scripts directory performs last-mile auto-promotion by distilling recurring patterns via LLM and writing rules to workspace files?

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-brainx-v5-the-first-brain-for-openclaw
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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md https://app.decimal.ai/s/leoyeai-brainx-v5-the-first-brain-for-openclaw/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

This gets SKILL.md only. The skill's 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/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-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/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-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw")
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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/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 27 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-brainx-v5-the-first-brain-for-openclaw

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-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md https://app.decimal.ai/s/leoyeai-brainx-v5-the-first-brain-for-openclaw/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-brainx-v5-the-first-brain-for-openclaw-b3aaed/SKILL.md

This gets SKILL.md only. The skill's 27 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-brainx-v5-the-first-brain-for-openclaw/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-brainx-v5-the-first-brain-for-openclaw.json

This gets SKILL.md only. The skill's 27 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

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

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

scripts/

SKILL.md

BrainX V5 — The First Brain for OpenClaw

Persistent memory system using vector embeddings for contextual retrieval in AI agents.

37 Features

#	Feature	Description
1	✅ Production	Active on 32 agent profiles with centralized shared memory (2,400+ memories)
2	🧠 Auto-Learning	Learns on its own from every conversation without human intervention
3	💾 Persistent Memory	Remembers across sessions — PostgreSQL + pgvector
4	🤝 Shared Memory	All agents share the same knowledge pool
5	💉 Automatic Briefing	Personalized context injection at each agent startup
6	🔎 Semantic Search	Searches by meaning, not exact keywords
7	🏷️ Intelligent Classification	Auto-typed: facts, decisions, learnings, gotchas, notes
8	📊 Usage-Based Prioritization	Hot/warm/cold tiers — automatic promote/degrade based on access
9	🤝 Cross-Agent Learning	Propagates important gotchas and learnings across all agents
10	🔄 Anti-Duplicates	Semantic deduplication by cosine similarity with intelligent merge
11	⚡ Anti-Contradictions	Detects contradictory memories and supersedes the obsolete one
12	📋 Session Indexing	Searches past conversations (30-day retention)
13	🔒 PII Scrubbing	Automatic redaction of sensitive data before storage
14	🔮 Pattern Detection	Detects recurring patterns and promotes them automatically
15	🛡️ Disaster Recovery	Full backup/restore (DB + configs + hooks + workspaces)
16	⭐ Quality Scoring	Evaluates memory quality and promotes only what deserves to persist
17	⚙️ Fact Extraction	Regex + LLM pipelines capture both operational facts and nuanced learnings
18	📦 Context Packs	Weekly project packs and bootstrap topic files for fast situational awareness
19	📈 Telemetry	Query logs, injection metrics, and health monitoring built in
20	🧵 Supersede Chains	Old memories can be replaced cleanly without losing history
21	🌀 Memory Distillation	Consolidates raw logs into higher-signal memories over time
22	🛡️ Pre-Action Advisory	Queries past mistakes before high-risk tool execution
23	👤 Agent Profiles	Per-agent hook injection: boosts/filters memories by agent role
24	🔀 Cross-Agent Injection Slots	Hook reserves 30% of context slots for other agents' memories
25	📊 Metrics Dashboard	CLI dashboard with top patterns, memory stats, and usage trends
26	🔧 Doctor & Auto-Fix	Schema integrity check + automatic repair of detected issues
27	👍 Memory Feedback	Mark memories as useful/useless/incorrect to refine quality
28	🗺️ Trajectory Recording	Records problem→solution paths for future reference
29	📝 Learning Details	Extended metadata extraction for learnings and gotchas
30	🔄 Lifecycle Management	Automatic promotion/degradation of memories by age and usage
31	📥 Workspace Import	Imports existing MEMORY.md files from all workspaces into the brain
32	🧪 Eval Dataset Generation	Generates evaluation datasets from real memories for quality testing
33	🏗️ Session Snapshots	Captures full agent state at session close for analysis
34	🧹 Low-Signal Cleanup	Automatic cleanup of low-value, outdated, or redundant memories
35	🔃 Memory Reclassification	Reclassifies memories with correct types and categories post-hoc
36	🔄 Auto-Promotion Pipeline	Detects high-recurrence patterns and promotes them as rules in workspace files automatically
37	📊 15-Step Daily Pipeline	Consolidated daily pipeline: bootstrap, lifecycle, distiller, harvester, bridge, auto-distiller, consolidation, cross-agent, contradiction, md-harvester, error-harvester, auto-promoter, promotion-applier, memory-enforcer, audit

When to Use

✅ USE when:

An agent needs to "remember" information from previous sessions
You want to give additional context to an LLM about past actions
You need semantic search by content
You want to store important decisions with metadata

❌ DON'T USE when:

Ephemeral information that doesn't need persistence
Structured tabular data (use a regular DB)
Simple cache (use Redis or in-memory)

Auto-Injection (Hook)

BrainX V5 includes an OpenClaw hook that automatically injects relevant memories when an agent starts.

Production Validation Status

Real validation completed on 2026-03-18:

Global hook enabled in ~/.openclaw/openclaw.json
Managed hook synced with ~/.openclaw/skills/brainx-v5/hook/ (handler.js re-synced)
Active physical database: brainx_v5
agent-profiles.json expanded from 10 to 32 profiles (all agents)
Cross-agent injection slots (30%) activated in production
20 null embeddings regenerated + 17 duplicate pairs deduped via brainx fix
2 pending migrations applied
Doctor: 18/18 passed, 0 warnings
Real bootstrap smoke test passed for 10 agents
Expected evidence confirmed:
-  block written into MEMORY.md
- Updated: timestamp present
- Fresh row recorded in brainx_pilot_log

If this validation becomes stale, rerun a bootstrap smoke test before assuming runtime is still healthy.

How it works:

agent:bootstrap event → Hook fires automatically
PostgreSQL query → Fetches hot/warm recent memories
Generates file → Creates BRAINX_CONTEXT.md in the workspace
Agent reads → File is loaded as initial context

Configuration:

In ~/.openclaw/openclaw.json:

{
  "hooks": {
    "internal": {
      "enabled": true,
      "entries": {
        "brainx-auto-inject": {
          "enabled": true,
          "limit": 5,
          "tier": "hot+warm",
          "minImportance": 5
        }
      }
    }
  }
}

Per-agent setup:

Add to AGENTS.md in each workspace:

## Every Session

1. Read `SOUL.md`
2. Read `USER.md`
3. Read `brainx.md`
4. Read `BRAINX_CONTEXT.md` ← Auto-injected context

Available Tools

brainx_add_memory

Saves a memory to the vector brain.

Parameters:

content (required) — Memory text
type (optional) — Type: note, decision, action, learning (default: note)
context (optional) — Namespace/scope
tier (optional) — Priority: hot, warm, cold, archive (default: warm)
importance (optional) — Importance 1-10 (default: 5)
tags (optional) — Comma-separated tags
agent (optional) — Name of the agent creating the memory

Example:

brainx add --type decision --content "Use embeddings 3-small to reduce costs" --tier hot --importance 9 --tags config,openai

brainx_search

Searches memories by semantic similarity.

Parameters:

query (required) — Search text
limit (optional) — Number of results (default: 10)
minSimilarity (optional) — Threshold 0-1 (default: 0.3)
minImportance (optional) — Filter by importance 0-10
tier (optional) — Filter by tier
context (optional) — Exact context filter

Example:

brainx search --query "API configuration" --limit 5 --minSimilarity 0.5

Returns: JSON with results.

brainx_inject

Gets memories formatted for direct injection into LLM prompts.

Parameters:

query (required) — Search text
limit (optional) — Number of results (default: 10)
minImportance (optional) — Filter by importance
tier (optional) — Tier filter (default: hot+warm)
context (optional) — Context filter
maxCharsPerItem (optional) — Truncate content (default: 2000)

Example:

brainx inject --query "what decisions were made about openai" --limit 3

Returns: Formatted text ready for injection:

[sim:0.82 imp:9 tier:hot type:decision agent:coder ctx:openclaw]
Use embeddings 3-small to reduce costs...

---

[sim:0.71 imp:8 tier:hot type:decision agent:support ctx:brainx]
Create SKILL.md for OpenClaw integration...

brainx_health

Verifies BrainX is operational.

Parameters: none

Example:

brainx health

Returns: PostgreSQL + pgvector connection status.

Backup and Recovery

Create Backup

./scripts/backup-brainx.sh ~/backups

Creates brainx-v5_backup_YYYYMMDD_HHMMSS.tar.gz containing:

Full PostgreSQL database (SQL dump)
OpenClaw configuration (hooks, .env)
Skill files
Workspace documentation

Restore Backup

./scripts/restore-brainx.sh backup.tar.gz --force

Fully restores BrainX V5 including:

All memories (with embeddings)
Hook configuration
Environment variables

Full Documentation

See RESILIENCE.md for:

Complete disaster scenarios
Migration to new VPS
Troubleshooting
Automatic backup configuration

Configuration

Environment Variables

# Required
DATABASE_URL=postgresql://user:pass@host:5432/brainx_v5
OPENAI_API_KEY=sk-...

# Optional
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
OPENAI_EMBEDDING_DIMENSIONS=1536
BRAINX_INJECT_DEFAULT_TIER=hot+warm
BRAINX_INJECT_MAX_CHARS_PER_ITEM=2000
BRAINX_INJECT_MAX_LINES_PER_ITEM=80

Database Setup

# Schema is in ~/.openclaw/skills/brainx-v5/sql/
# Requires PostgreSQL with pgvector extension

psql $DATABASE_URL -f ~/.openclaw/skills/brainx-v5/sql/v3-schema.sql

Direct Integration

You can also use the unified wrapper that reads the API key from OpenClaw:

cd ~/.openclaw/skills/brainx-v5
./brainx add --type note --content "test"
./brainx search --query "test"
./brainx inject --query "test"
./brainx health

Compatibility: ./brainx-v5 and ./brainx-v5-cli also work as aliases for the main wrapper.

Advisory System (Pre-Action Check)

BrainX includes an advisory system that queries relevant memories, trajectories, and recurring patterns before executing high-risk tools. Helps agents avoid repeating past mistakes.

High-Risk Tools

The following tools automatically trigger advisory checks: exec, deploy, railway, delete, rm, drop, git push, git force-push, migration, cron, message send, email send.

CLI Usage

# Check for advisories before a tool execution
./brainx-v5 advisory --tool exec --args '{"command":"rm -rf /tmp/old"}' --agent coder --json

# Quick check via helper script
./scripts/advisory-check.sh exec '{"command":"rm -rf /tmp/old"}' coder

Agent Integration (Manual)

Since only agent:bootstrap is supported as a hook event, agents should manually call brainx advisory before high-risk tools:

# In agent SKILL.md or AGENTS.md, add:
# Before exec/deploy/delete/migration, run:
cd ~/.openclaw/skills/brainx-v5 && ./scripts/advisory-check.sh <tool> '<args_json>' <agent>

The advisory returns relevant memories, similar past problem→solution paths, and recurring patterns with a confidence score. It's informational — never blocking.

Agent-Aware Hook Injection

The agent:bootstrap hook uses agent profiles (hook/agent-profiles.json) to customize memory injection per agent:

coder: Boosts gotcha/error/learning memories; filters by infrastructure/code/deploy/github contexts; excludes notes
writer: Boosts decision/learning; filters by content/seo/marketing; excludes errors
monitor: Boosts gotcha/error; filters by infrastructure/health/monitoring
echo: No filtering (default behavior)

Agents not listed in the profiles file get the default unfiltered injection. Edit hook/agent-profiles.json to add new agent profiles.

Cross-Agent Memory Sharing

The hook reserves ~30% of injection slots for cross-agent memories, ensuring each agent sees relevant learnings from other agents. The cross-agent-learning.js script tags high-importance memories for cross-agent visibility without creating duplicates.

Security & Trust

This skill is flagged with "suspicious patterns" by ClawHub's automated scanner. Here's what each pattern does and why it's necessary:

Pattern	File	Why
`child_process.execFile`	`hook/handler.js`	Invokes the BrainX CLI to query memories during agent bootstrap. No arbitrary command execution.
`process.env` access	`lib/db.js`, `lib/openai-rag.js`, `lib/cli.js`	Reads `DATABASE_URL` and `OPENAI_API_KEY` to connect to PostgreSQL and generate embeddings. Standard for any database-backed skill.
`fetch('https://api.openai.com')`	`lib/openai-rag.js`	Calls OpenAI Embeddings API to generate vector representations. Single endpoint, no other network calls.
File read/write	`hook/handler.js`	Writes `BRAINX_CONTEXT.md` and updates `MEMORY.md` in the agent's workspace during bootstrap injection.

No secrets are stored in code. All credentials come from environment variables. No data leaves the system except embedding requests to OpenAI.

Notes

Memories are stored with vector embeddings (1536 dimensions)
Search uses cosine similarity
inject is the most useful tool for giving context to LLMs
Tier hot = fast access, cold/archive = long-term storage
Memories are persistent in PostgreSQL (independent of OpenClaw)
Auto-injection hook fires on every agent:bootstrap

Feature Status (Tables)

✅ All Operational

Table	Function	Status
`brainx_memories`	Core: stores memories with embeddings	✅ Active (2,400+)
`brainx_query_log`	Tracks search/inject queries	✅ Active
`brainx_pilot_log`	Tracks auto-inject per agent	✅ Active
`brainx_context_packs`	Pre-generated context packages	✅ Active
`brainx_patterns`	Detects recurring errors/issues	✅ Active
`brainx_session_snapshots`	Captures state at session close	✅ Active
`brainx_learning_details`	Extended metadata for learning/gotcha memories	✅ Active
`brainx_trajectories`	Records problem→solution paths	✅ Active

8/8 tables operational. Population scripts implemented 2026-03-06.

Full Feature Inventory (35)

CLI Core (`brainx <cmd>`)

#	Command	Function
1	`add`	Save memory (7 types, 20+ categories, V5 metadata)
2	`search`	Semantic search by cosine similarity
3	`inject`	Formatted memories for LLM prompt injection
4	`fact` / `facts`	Shortcut to save/list infrastructure facts
5	`resolve`	Mark pattern as resolved/promoted/wont_fix
6	`promote-candidates`	Detect memories eligible for promotion
7	`lifecycle-run`	Degrade/promote memories by age/usage
8	`metrics`	Metrics dashboard and top patterns
9	`doctor`	Full diagnostics (schema, integrity, stats)
10	`fix`	Auto-repair issues detected by doctor
11	`feedback`	Mark memory as useful/useless/incorrect
12	`health`	PostgreSQL + pgvector connection status

Processing Scripts (`scripts/`)

#	Script	Function
13	`memory-bridge.js`	Syncs memory between sessions/agents
14	`memory-distiller.js`	Distills sessions into new memories
15	`session-harvester.js`	Harvests info from past sessions
16	`session-snapshot.js`	Captures state at session close
17	`pattern-detector.js`	Detects recurring errors/issues
18	`learning-detail-extractor.js`	Extracts metadata from learnings/gotchas
19	`trajectory-recorder.js`	Records problem→solution paths
20	`fact-extractor.js`	Extracts facts from conversations
21	`contradiction-detector.js`	Detects contradicting memories
22	`cross-agent-learning.js`	Shares learnings between agents
23	`quality-scorer.js`	Scores memory quality
24	`context-pack-builder.js`	Generates pre-built context packages
25	`reclassify-memories.js`	Reclassifies memories with correct types/categories
26	`cleanup-low-signal.js`	Cleans up low-value memories
27	`dedup-supersede.js`	Detects and marks duplicates
28	`eval-memory-quality.js`	Evaluates dataset quality
29	`generate-eval-dataset-from-memories.js`	Generates evaluation dataset
30	`memory-feedback.js`	Per-memory feedback system
31	`import-workspace-memory-md.js`	Imports from workspace MEMORY.md files
32	`migrate-v2-to-v3.js`	Schema migration V2→V3
33	`promotion-applier.js`	Last-mile auto-promotion: distills patterns via LLM and writes rules to workspace files

Hooks and Infrastructure

#	Component	Function
34	`brainx-auto-inject`	Auto-injection hook at each agent bootstrap
35	`backup-brainx.sh`	Full backup (DB + config + skills)
36	`restore-brainx.sh`	Full restore from backup
37	`promotion-applier.js`	Pipeline step 13: writes promoted patterns to workspace files

V5 Metadata

sourceKind — Origin: user_explicit, agent_inference, tool_verified, llm_distilled, etc.
sourcePath — Source file/URL
confidence — Score 0-1
expiresAt — Automatic expiration
sensitivity — normal/sensitive/restricted
Automatic PII scrubbing (BRAINX_PII_SCRUB_ENABLED)
Similarity-based dedup (BRAINX_DEDUPE_SIM_THRESHOLD)

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/brainx/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 27 files · scripts

Illustrative Want to see what leoyeai/BrainX V5 — The First Brain for OpenClaw would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Use this skill

BrainX V5 — The First Brain for OpenClaw

37 Features

When to Use

Auto-Injection (Hook)

Production Validation Status

How it works:

Configuration:

Per-agent setup:

Available Tools

brainx_add_memory

brainx_search

brainx_inject

brainx_health

Backup and Recovery

Create Backup

Restore Backup

Full Documentation

Configuration

Environment Variables

Database Setup

Direct Integration

Advisory System (Pre-Action Check)

High-Risk Tools

CLI Usage

Agent Integration (Manual)

Agent-Aware Hook Injection

Cross-Agent Memory Sharing

Security & Trust

Notes

Feature Status (Tables)

✅ All Operational

Full Feature Inventory (35)

CLI Core (brainx <cmd>)

Processing Scripts (scripts/)

Hooks and Infrastructure

V5 Metadata

SkillSafety

Source

Related skills

More in the registry

CLI Core (`brainx <cmd>`)

Processing Scripts (`scripts/`)