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Market Brief — Daily Morning & Evening Intelligence Digest · leoyeai | DecimalAI

Financeby leoyeai

Market Brief — Daily Morning & Evening Intelligence Digest

Daily morning and evening intelligence digest for prediction market traders. Morning brief: Kalshi portfolio P&L, Polymarket trending markets, crypto prices — scannable in 30 seconds. Evening brief: lightweight market summary or AI-filtered news digest with two-stage Qwen materiality gate. Works standalone; unlocks additional sections automatically when paired with Kalshalyst (edges), Prediction Market Arbiter (divergences), and Xpulse (social signals). Hub of the OpenClaw Prediction Market Trad

SkillScore

SkillScore76/100

Test results 76

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

20%76%of test cases passed +56 pts

Without skill5 of 25

With skill19 of 25

14 fixed5 passed either way6 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIf Kalshi API credentials are not provided or the API connection fails when generating the portfolio P&L section, what plain text string is produced to indicate portfolio unavailability?

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-market-morning-brief
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-market-morning-brief")
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-market-morning-brief

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-market-morning-brief/SKILL.md https://app.decimal.ai/s/leoyeai-market-morning-brief/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-market-morning-brief/SKILL.md

This gets SKILL.md only. The skill's 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/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-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/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-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief")
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-market-morning-brief/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-market-morning-brief/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 6 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-market-morning-brief

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-market-morning-brief/SKILL.md https://app.decimal.ai/s/leoyeai-market-morning-brief/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-market-morning-brief/SKILL.md

This gets SKILL.md only. The skill's 6 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-market-morning-brief/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-market-morning-brief.json

This gets SKILL.md only. The skill's 6 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

Market Brief — Daily Morning & Evening Intelligence Digest

Overview

Market Morning Brief is a lightweight, resilient daily intelligence system designed to be the first thing you read each morning. It combines:

Portfolio P&L — Your Kalshi positions and unrealized gains/losses
Top opportunities — Markets with highest edge (if Kalshalyst cache available)
Cross-platform divergences — Kalshi vs Polymarket pricing mismatches (if Arbiter cache available)
X signal summaries — Top-performing prediction signals from Twitter/X (if Xpulse cache available)
Crypto prices — Bitcoin, Ethereum, plus any configured altcoins (if Coinbase configured)
Polymarket insights — Notable markets and volume activity

The brief is designed for 30-second scanning — each section is 3-8 lines maximum. If a data source is unavailable, the section gracefully degrades to "unavailable" and the brief continues. If Kalshi credentials are not configured yet, the brief shows a realistic preview portfolio and edge block so first-time users still see value immediately.

When to Use This Skill

You trade prediction markets (Kalshi, Polymarket) and want daily market context
You need a quick morning summary: portfolio status + opportunities + signals
You want an evening briefing: either lightweight trading summary or AI-filtered news digest
You want integration with your other market intelligence tools (Kalshalyst, Arbiter, Xpulse)
You want plain text output suitable for SMS, iMessage, or any messaging platform
You want AI-powered news curation: two-stage filtering to prevent notification fatigue

Every Evening (Default: 6:00 PM): Day's activity summary (positions opened/closed, realized P&L), watch list for overnight risks, top X signals from the day, and unusual market activity.

Architecture

Design Philosophy

Resilience through isolation. Each section:

Wrapped in try/except to prevent one failure from breaking the entire brief
Reads from cache files (not live APIs) for speed and reliability
Gracefully degrades if data unavailable ("unavailable, check Kalshalyst directly")
Logs failures without interrupting output

Plain text output. No markdown, no emojis, no formatting — designed for chat delivery:

Compatible with iMessage, SMS, email, web chat, any platform
Scannable: max 80 chars per line, clear section headers
Readable at a glance in <30 seconds

Configurable integration. The brief reads from:

Optional Kalshalyst cache (.kalshi_research_cache.json)
Optional Arbiter cache (.crossplatform_divergences.json)
Optional Xpulse cache (.x_signal_cache.json)
Optional Coinbase API (if configured)
Public Polymarket API (no auth)

If a cache file doesn't exist (skill not installed), that section simply shows "unavailable".

Configuration

Create or update your config.yaml when you want live account data. The first run works without it and shows preview output:

market_morning_brief:
  enabled: true
  morning_time: "07:30"     # Schedule for morning brief
  evening_time: "18:00"     # Schedule for evening brief
  timezone: "America/New_York"

  # Kalshi API (required for portfolio section)
  kalshi:
    enabled: true
    api_key_id: "your-key-id"
    private_key_file: "/path/to/private.key"

  # Coinbase API (optional, for crypto prices)
  coinbase:
    enabled: false
    api_key: "sk-..."
    tickers: ["BTC", "ETH"]  # Customize as needed

  # Cache paths (for reading other skills' data)
  cache_paths:
    kalshalyst: "./state/.kalshi_research_cache.json"
    arbiter: "./state/.crossplatform_divergences.json"
    xpulse: "./state/.x_signal_cache.json"

  # Which sections to include
  include:
    portfolio: true
    kalshalyst_edges: true      # Requires Kalshalyst skill
    arbiter_divergences: true   # Requires Prediction Market Arbiter skill
    xpulse_signals: true        # Requires Xpulse skill
    crypto: false               # Requires Coinbase API key
    polymarket: true            # Free (public API)

Section-by-Section Breakdown

1. Portfolio Summary

Data source: Kalshi API (read-only)

Example output:

PORTFOLIO (3 positions, +$24 unrealized):
POTUS-2028-DEM    YES  100@48¢  $48 cost  +$18 unrl (exp: 242 days)
UKRAINE-2026-NO    YES   50@28¢  $14 cost   +$8 unrl (exp: 118 days)
FED-MAR-CUT        NO   200@35¢  $70 cost  -$2 unrl (exp: 8 days)

Fields:

Ticker | Side (YES/NO) | Quantity @ Price | Cost | Unrealized P&L | Days to Expiration

Graceful degradation: If Kalshi API unavailable:

PORTFOLIO: unavailable (check Kalshi API)

2. Kalshalyst Edges (Optional)

Data source: Kalshalyst cache (.kalshi_research_cache.json)

Example output:

EDGES (Kalshalyst, top 3):
1. POTUS-2028-DEM    NO @ 38%  (+14% edge, 72% conf)
2. INFLATION-2026    YES @ 68%  (+8% edge, 65% conf)
3. UKRAINE-PEACE     YES @ 55%  (+6% edge, 58% conf)

Fields:

Ticker | Side | Market Price | Edge % | Confidence %

Graceful degradation: If Kalshalyst skill not installed:

EDGES: unavailable — unlock with: clawhub install kalshalyst

3. Cross-Platform Divergences (Optional)

Data source: Prediction Market Arbiter cache (.crossplatform_divergences.json)

Example output:

DIVERGENCES (Arbiter, Kalshi ↔ Polymarket):
UKRAINE-2026-NO    Kalshi 28% ↔ PM 31%  ($0.03 spread, 12% vol diff)
POTUS-2028-DEM     Kalshi 38% ↔ PM 40%  ($0.02 spread, 8% vol diff)

Fields:

Ticker | Kalshi Price ↔ Polymarket Price | Spread | Volume Difference

Graceful degradation: If Arbiter skill not installed:

DIVERGENCES: unavailable — unlock with: clawhub install prediction-market-arbiter

4. X Signal Summaries (Optional)

Data source: Xpulse cache (.x_signal_cache.json)

Example output:

X SIGNALS (Xpulse, last 24h):
Fed rate cut odds +5%   (confidence: 78%, reach: 8.2K)
Ukraine ceasefire talks (+3%, 72% conf, 5.1K reach)

Fields:

Signal | Magnitude | Confidence % | Reach/Strength

Graceful degradation: If Xpulse skill not installed:

X SIGNALS: unavailable — unlock with: clawhub install xpulse

5. Crypto Prices (Optional)

Data source: Coinbase API (requires key + configuration)

Example output:

CRYPTO:
BTC  $62,400  (+1.2%)   | ETH  $3,140  (-0.8%)
SOL  $142     (-2.1%)   | AVAX $38     (+0.5%)

Fields:

Ticker | Price | 24h Change %

Graceful degradation: If Coinbase not configured:

CRYPTO: unavailable (configure Coinbase API for crypto prices)

6. Polymarket Activity

Data source: Public Polymarket API (free)

Example output:

POLYMARKET (top 3 by volume):
POTUS 2028: $2.4M vol, 48% DEM (vs 52% GOP)
Inflation >4% 2026: $1.1M vol, 32% prob
Bitcoin $100K by 2026: $0.8M vol, 58% prob

Fields:

Market | Volume | Implied Probability | (Context)

Graceful degradation: If Polymarket API unavailable:

POLYMARKET: unavailable (check Polymarket directly)

Evening Brief — Lightweight Market Summary + AI-Filtered News

Sent at configured evening time (default: 6:00 PM). Two variants: lightweight market update or full news digest with materiality filtering.

Lightweight Market Variant (6-8 lines, trading-focused)

EVENING BRIEFING — Thursday, March 7, 2026

ACTIVITY:
Current positions: 3 | Cost: $132 | Unrealized: +$24

OVERNIGHT WATCH:
• FED-MAR-CUT expires in 8 days — monitor Fed speakers before FOMC
• UKRAINE-2026 low liquidity (9 contracts asking) — wide spreads

TOP X SIGNALS TODAY:
• Ukraine ceasefire talks +3% (78% conf)
• Fed rate cut odds stabilizing (72% conf)

Key differences from morning brief:

Shorter (6-8 lines max)
Focus on intraday activity (current positions, cost, unrealized P&L)
Overnight watch items (expirations, geopolitical risks, liquidity alerts)
Top X signals from that day (not 24h rolling)

Full Evening News Digest (News-focused with AI filtering)

EVENING NEWS BRIEFING — Thursday, March 7, 2026

🏛️ Fed Signals Cautious Stance on Rate Cuts (87% conf, Reuters)
📈 Tech Stocks Rally on Earnings Beat (82% conf, Bloomberg)
🌍 Geopolitical Tensions Escalate Over Trade Deal (79% conf, AP)
💻 AI Policy Bill Advances in Congress (76% conf, TechCrunch)
📌 Crypto Markets Stabilize After Volatility (71% conf, CoinDesk)

Features:

Category icons: 🏛️ policy, 📈 markets, 💻 technology, 🌍 geopolitics, 📌 general
Confidence scores (0-100%) show Qwen relevance assessment
Two-stage filtering pipeline (see below)
48-hour rolling history prevents repeated news
Fail-closed design: if Qwen unavailable, no news sent (silence over noise)

Evening Briefing: Two-Stage News Filtering Pipeline

Evening briefing combines DuckDuckGo news search with local Qwen LLM for AI-powered news curation. Designed to prevent notification fatigue while surfacing genuinely material developments.

Architecture

Stage 1: Relevance & Significance Filter

Search for recent news across configured topics (default: prediction markets, AI policy, federal reserve)
Run each article through Qwen: is this significant? (0-1 confidence score, category classification)
Filter to articles with confidence >= min_confidence (default: 0.7)
Limit to top 10 most confident articles for Stage 2

Stage 2: Materiality Gate (Prevents Notification Fatigue)

Load history of previously sent articles (48h rolling, max 200 entries)
Compare candidate articles against recent history
Qwen decision: is this NEW and MATERIAL? Or just ongoing background noise?
Drop duplicates, commentary, routine announcements
Only pass through genuinely new developments or significant escalations

Fail-Closed Design:

If Qwen unavailable during Stage 1 or Stage 2: drop all articles (silence over noise)
If no material news: send nothing (don't interrupt with noise)
History persistence: ~/.openclaw/state/evening_news_history.json (max 200 entries, auto-cleanup)

Example Configuration

market_morning_brief:
  evening_briefing:
    enabled: true
    mode: "news"                    # "market" for activity summary, "news" for full news digest
    time: "18:00"                   # Briefing time (30-min window)
    materiality_gate: true          # Enable Stage 2 filter (prevents fatigue)
    min_confidence: 0.7             # Minimum relevance threshold (0-1)
    max_per_topic: 3                # Max articles per topic to search
    topics:
      - "prediction markets"
      - "AI policy"
      - "federal reserve"
      - "geopolitics"

Command Usage

# Lightweight market variant (activity + watch list)
python scripts/evening_brief.py --mode market

# Full news digest variant
python scripts/evening_brief.py --mode news

# Force send regardless of time/already-sent-today
python scripts/evening_brief.py --force

# Dry run (print without sending)
python scripts/evening_brief.py --dry-run

# Enable verbose logging
python scripts/evening_brief.py --debug

News History

Stored at ~/.openclaw/state/evening_news_history.json. Max 200 entries, auto-cleans after 48 hours. Used by Stage 2 materiality gate to prevent duplicate notifications.

Cache File Integration

Each optional skill writes a cache file that the Morning Brief reads automatically:

Skill	Cache File	Section Unlocked
Kalshalyst	`state/.kalshi_research_cache.json`	Edge opportunities
Prediction Market Arbiter	`state/.crossplatform_divergences.json`	Cross-platform divergences
Xpulse	`state/.x_signal_cache.json`	X/Twitter signals

If a cache file doesn't exist, that section shows "unavailable" with the install command. See references/integration.md for cache file schemas.

Example Complete Morning Brief

MARKET MORNING BRIEF — Thursday, March 7, 2026

PORTFOLIO (3 positions, +$24 unrealized):
POTUS-2028-DEM    YES  100@48¢  $48 cost  +$18 unrl (exp: 242 days)
UKRAINE-2026-NO    YES   50@28¢  $14 cost   +$8 unrl (exp: 118 days)
FED-MAR-CUT        NO   200@35¢  $70 cost  -$2 unrl (exp: 8 days)

EDGES (Kalshalyst, top 3):
1. POTUS-2028-DEM    NO @ 38%  (+14% edge, 72% conf)
2. INFLATION-2026    YES @ 68%  (+8% edge, 65% conf)
3. UKRAINE-PEACE     YES @ 55%  (+6% edge, 58% conf)

DIVERGENCES (Arbiter):
UKRAINE-2026-NO    Kalshi 28% ↔ PM 31%  ($0.03 spread)
POTUS-2028-DEM     Kalshi 38% ↔ PM 40%  ($0.02 spread)

X SIGNALS (last 24h):
Fed rate cut odds +5%   (78% conf, 8.2K reach)
Ukraine ceasefire +3%   (72% conf, 5.1K reach)

CRYPTO:
BTC  $62,400  (+1.2%)   | ETH  $3,140  (-0.8%)

POLYMARKET (top 3 by vol):
POTUS 2028 DEM: $2.4M vol, 48% prob
Inflation >4% 2026: $1.1M vol, 32% prob
Bitcoin >$100K 2026: $0.8M vol, 58% prob

Scheduling & Commands

Morning Brief (Default: 7:30 AM)

# Manual trigger
python scripts/morning_brief.py

# With debug output
python scripts/morning_brief.py --debug

# Dry run (print without side effects)
python scripts/morning_brief.py --dry-run

# Custom config path
python scripts/morning_brief.py --config /path/to/config.yaml

# Via OpenClaw (if integrated):
openclaw skill run market-morning-brief morning

Cron Scheduling

30 7 * * * python /path/to/scripts/morning_brief.py              # Morning at 7:30 AM
0 18 * * * python /path/to/scripts/evening_brief.py --mode market  # Evening at 6:00 PM

Dependencies

Required: Python 3.10+, pip install kalshi-python requests pyyaml

For evening news digest: Ollama + Qwen model (ollama pull qwen3:latest), plus pip install ddgs (or duckduckgo-search as fallback).

Optional skills for additional brief sections: Kalshalyst, Prediction Market Arbiter, Xpulse (see Implementation Notes below).

Performance & Cost

Morning Brief

Runtime: <5 seconds (all cached data)
API calls: 1 (Polymarket public API) + 1 (Kalshi portfolio) = 2 calls
Cost: $0 (Kalshi free tier, Polymarket free)

With Optional Skills

If Kalshalyst, Arbiter, Xpulse installed:

Runtime: <2 seconds (reads cache files)
Cost: Only the skill installation costs (brief itself has no additional cost)

Troubleshooting

Portfolio not showing: Verify Kalshi API key is configured and not rate-limited. Run python scripts/morning_brief.py --debug.

Sections showing "unavailable": Expected if the skill isn't installed. Install with the clawhub install command shown in the output, then run the skill once to generate its cache file.

Evening news empty: Check Ollama is running (ollama list), test Qwen (ollama run qwen3:latest "test"), and verify ddgs (python -c "from ddgs import DDGS; print('OK')").

Stage 2 drops everything: The materiality gate filters out non-novel news. Clear history with rm ~/.openclaw/state/evening_news_history.json and retry, or disable with --no-materiality-gate.

Qwen timeout: Reduce article count with --max-per-topic 2 or skip Stage 2 with --no-materiality-gate.

Implementation Notes

This skill is designed for standalone operation but unlocks its full potential with the OpenClaw Prediction Market Trading Stack.

Standalone: Portfolio P&L + Polymarket trending + Crypto prices (if configured). No other skills required.

With the full stack: Each additional skill adds a new section to your daily brief automatically — no configuration needed. Install skills, run them once, and the Morning Brief picks up their cache files on the next run.

Skill	Unlocks	Install
Kalshalyst	Contrarian edge analysis with Kelly sizing	`clawhub install kalshalyst`
Prediction Market Arbiter	Cross-platform Kalshi↔Polymarket divergences	`clawhub install prediction-market-arbiter`
Xpulse	Real-time X/Twitter social signals	`clawhub install xpulse`
Portfolio Drift Monitor	Position drift alerts between briefs	`clawhub install portfolio-drift-monitor`
Kalshi Command Center	Direct trade execution from edge alerts	`clawhub install kalshi-command-center`

Install the complete stack:

clawhub install kalshalyst kalshi-command-center polymarket-command-center prediction-market-arbiter xpulse portfolio-drift-monitor market-morning-brief personality-engine

Support

For issues: run with DEBUG=1 for verbose output, review references/sections.md and references/integration.md, or check /tmp/market-morning-brief.log.

Author: KingMadeLLC

Feedback & Issues

Found a bug? Have a feature request? Want to share results?

GitHub Issues: github.com/kingmadellc/openclaw-prediction-stack/issues
X/Twitter: @KingMadeLLC

Part of the OpenClaw Prediction Stack — the first prediction market skill suite on ClawHub.

About this skill

Automatic

Finance & Accounting 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/market-morning-brief/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 6 files · scripts

Illustrative Want to see what leoyeai/Market Morning Brief 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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SkillScore

Safety

What it changes

Track record

Use this skill

Market Brief — Daily Morning & Evening Intelligence Digest

Overview

When to Use This Skill

Architecture

Design Philosophy

Configuration

Section-by-Section Breakdown

1. Portfolio Summary

2. Kalshalyst Edges (Optional)

3. Cross-Platform Divergences (Optional)

4. X Signal Summaries (Optional)

5. Crypto Prices (Optional)

6. Polymarket Activity

Evening Brief — Lightweight Market Summary + AI-Filtered News

Lightweight Market Variant (6-8 lines, trading-focused)

Full Evening News Digest (News-focused with AI filtering)

Evening Briefing: Two-Stage News Filtering Pipeline

Architecture

Example Configuration

Command Usage

News History

Cache File Integration

Example Complete Morning Brief

Scheduling & Commands

Morning Brief (Default: 7:30 AM)

Cron Scheduling

Dependencies

Performance & Cost

Morning Brief

With Optional Skills

Troubleshooting

Implementation Notes

Further Reading

Support

Feedback & Issues

SkillSafety

Source

Related skills

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