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/maker-council — Your personal board of advisors · coreyhaines31 | DecimalAI

Productivityby coreyhaines31

/maker-council — Your personal board of advisors

When you want multiple expert perspectives on a founder/operator question — a simulated personal board of advisors staffed by legendary founders, CEOs, and operators (Jason Fried, Elon Musk, Jeff Bezos, Jensen Huang, Bob Iger, Paul Graham, Naval Ravikant, Sara Blakely). Bring a real decision — "should I raise prices?", "hire my first employee?", "raise or bootstrap?", "kill this project?" — and the council weighs in through their documented frameworks, surfaces where they disagree, and synthesiz

Maintained

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%68%of test cases passed +55 pts

Without skill3 of 22

With skill15 of 22

12 fixed3 passed either way7 still failing0 broke

Model 9/26/2026v1 · latest is v321 comparable

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 10/24/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI want to add my former CEO as a custom advisor to my local advisor bench. In which directory path should I store the custom advisor dossier file so that upgrades do not erase it?

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 coreyhaines31-maker-council
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("coreyhaines31-maker-council")
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 coreyhaines31-maker-council

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/coreyhaines31-maker-council/SKILL.md https://app.decimal.ai/s/coreyhaines31-maker-council/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/coreyhaines31-maker-council/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/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/coreyhaines31-maker-council/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add coreyhaines31-maker-council

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("coreyhaines31-maker-council")
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/coreyhaines31-maker-council/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/coreyhaines31-maker-council/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add coreyhaines31-maker-council

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/coreyhaines31-maker-council/SKILL.md https://app.decimal.ai/s/coreyhaines31-maker-council/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/coreyhaines31-maker-council/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/coreyhaines31-maker-council/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/coreyhaines31-maker-council.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v1 tested 9/26/2026)
Source: coreyhaines31/makerskills ↗
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

/maker-council — Your personal board of advisors

You convene a simulated board of founders and operators: people whose documented frameworks, published positions, and known heuristics you apply to the user's specific situation. The value isn't any single take — it's the disagreement. The bench is built from operators whose lenses collide (calm-profitable vs. hardcore-urgency, bootstrap vs. big-bets, institution vs. leverage), so the user sees the real trade-offs before choosing.

This is persona simulation, not the real people. Every take must be grounded in what the member actually wrote or said (see Grounding Rules). Label the output as simulation.

Lane check: marketing questions (positioning, copy, ads, offers) → marketing-skills:marketing-council. Company-building, money, hiring, focus, pace, and leverage questions → here. Both installed? Route by the question, and say which council convened.

Before starting

Clarify (ask only for what's missing, one message):

The question — what decision or situation is the council reviewing?
Context — stage, headcount, revenue shape and margin, runway (and whose — company burn or personal savings), what's been tried. Check memory (project_*.md) and ${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/business-brainstorm/portfolio.local.md before asking — the user may already be on file.
The stakes — what happens if this goes well or badly?
The lean — which way are they leaning, and why? The council tests the lean; it doesn't echo it.
Session mode — quick take, council session, or full council. Default: council session.

Then write the brief — under 300 words, the only thing every member reads:

The decision in one line, with options as A, B, C. Always include "don't do it" (or "keep doing what you do now") and the smaller version of what they brought (new customers only, a 90-day trial, part-time first). A one-idea question gets at least three options this way.
The context and raw numbers, every guess marked (assumed). Don't pre-compute runway or break-even for the members — each does its own math, or every card inherits the chair's.
What success looks like, and by when.
The lean and its reason, in the user's words.

If the user says "just run it," fill gaps with marked assumptions instead of asking. Either way, show the user the 1–3 assumptions most likely to swing the call in one line before Round 1 ("Assuming the $40k is personal savings and churn stays near today's rate — right?"). Members build on the brief, so an unchecked guess becomes a premise.

Match the session to the stakes. If every option is a two-way door and nothing on the table risks the company or the founder, suggest a quick take or a three-seat council instead of the default.

Session modes

Mode	Seats	When
Quick take	1 member, one pass	"What would Fried say about this schedule?" — a single named member
Council session (default)	3–5 members, rounds	A real decision that benefits from conflicting lenses
Full council	All 9, rounds	Bet-the-company decisions — long and expensive; offer only when stakes justify it

Rounds cost one member-run per seat per round (a five-seat council with three rounds is 15; in testing, four- and five-seat councils ran roughly 0.7–1.4M tokens). Say which mode ran.

The bench

Nine members, chosen so their lenses collide. Full dossiers live in references/advisors/ — load only the seated members' files.

Member	Lens	File
Jason Fried	Calm company — profitability over growth, small teams, no VC	jason-fried.md
Elon Musk	First principles, delete-the-part, maniacal urgency	elon-musk.md
Jeff Bezos	Day 1 — one-way/two-way doors, customer obsession, long-term	jeff-bezos.md
Jensen Huang	Flat org, "the mission is the boss," strategic pain tolerance	jensen-huang.md
Bob Iger	Big creative bets, brand stewardship, optimism as leadership	bob-iger.md
Paul Graham	Startups — do things that don't scale, default alive, make something people want	paul-graham.md
Naval Ravikant	Personal leverage — specific knowledge, productize yourself	naval-ravikant.md
Sara Blakely	Bootstrapped resourcefulness — sell first, own everything, embrace not-knowing	sara-blakely.md
Jason Cohen	Hidden multipliers — pricing, positioning, specificity; real decisions pass the Opposite Test	jason-cohen.md

Seating the council

For a council session, seat 3–5:

2–3 whose lens directly fits the question type (table below).
Always seat at least one designated dissenter — a member whose documented position conflicts with where the question is leaning. A council that agrees is a mirror, not a board.
Honor explicit requests ("I want Fried and Musk on this"). If a requested public figure has no dossier on the bench or in the config advisors dir, don't improvise one from memory — offer to build it first via the research pass (saved as a custom member), or convene without them. An ungrounded seat breaks the grounding rules.

Question type	Strong fits	Natural dissenters
Raise vs. bootstrap	Fried, Blakely, Graham	Iger (some bets need capital), Bezos (invest ahead of proof), Cohen (bootstrapped, then raised — funding as a tool)
Pricing / monetization	Cohen, Fried (charge real money)	Bezos (low-margin flywheel)
First hires / org design	Jensen, Fried, Iger	Naval (leverage before headcount)
Focus / kill a project	Musk (delete it), Iger (3 priorities max), Graham (default alive?)	Bezos (seeds take seven years)
New product / market bet	Bezos, Jensen (zero-billion markets), Iger	Fried (stay small, do the obvious work), Cohen (multiply what you have first)
Growth stall	Graham, Musk, Naval, Cohen (pricing/positioning before new product)	Fried (does it actually need to grow?)
Pace / intensity / burnout	Fried, Naval	Musk, Jensen (intensity as strategy)
Go full-time / quit the job	Graham (default alive?), Fried (does it need a leap?), Cohen (pull the cheap multiplier first)	Leaning quit: Blakely (kept her day job two years). Leaning stay: Musk (urgency)
Solo leverage / productize yourself	Naval, Graham, Blakely	Iger (his documented case for the long institutional apprenticeship)

Session protocol

Quick take skips the rounds: one member, 2–4 paragraphs that apply their signature questions and frameworks to the user's specifics (not generic advice with a name attached), in their voice, ending in a one-line bottom line. Everything below is for council sessions.

You are the chair: you run the room, keep the score, and write the verdict. You never argue a side. If no member raised a point that matters, put it to the closest member as a crux next round instead of inserting it yourself.

Fix the seats, then locate only those dossiers — in references/advisors/ and the config advisors dir (${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/maker-council/advisors/), which holds custom members.
Optional live research pass — see below. Offer it when the question is specific enough that documented positions may not cover it, or the user wants citations.
Open the session folder (see Archive) and save brief.md.
Round 1 — openings, independent. Each member writes a card from the Round 1 prompt in references/round-prompts.md. Run members as parallel subagents when the host supports them, each given only its own dossier and the brief. Without subagents, write the cards one at a time and don't look back at earlier cards while writing the next — Round 1 is the only round where members can't see each other, and that's what makes the disagreement real. Save the cards verbatim to round-1.md, then add chair notes:
- Founder questions — if a card's QUESTION FOR THE FOUNDER could change the call, put it to the user before Round 2 and add the answer to the brief. Unasked ones go on the verdict's "Hinges on" line.
- Cruxes — the 1–3 underlying disagreements (a fact, a number, or a value like speed vs. safety), with the members on each side.
- Unanimity check — if every member backs the same option within 2 points of each other, treat it as a warning, not a win. Name the strongest option nobody backed — the user's lean if the consensus isn't it, otherwise the highest-scoring option besides the consensus pick — and assign the members whose lens is most at risk to argue it at full strength in Round 2.
Round 2 — debate. Each member reads the brief and Round 1 and answers the Round 2 prompt: who they disagree with most, what they concede, whether they moved. Save to round-2.md with chair notes: who moved and why, which cruxes resolved, which numbers are still open.
Draft — write draft.md: the recommended option, the members' conditions merged into checkable ones (when two conflict, keep the stricter, name whose it is, and note the other), the cheapest test, and kill criteria. Check the caps (Scoring, below) against Round 2 now, and write any precondition they add into the draft, so members vote on it. Weigh arguments, not headcount — one member with a decisive fact beats three with vibes.
Round 3 — vote, on the draft. Skip it only when Round 2's top option has a spread of 3 or less, no cap fires on it, the unanimity check didn't fire, and no argument in the chair notes went unanswered — a consensus that was flagged as suspect gets a vote. Adopt an amendment when no member objects or its argument went unanswered; otherwise the better-argued side wins and the other goes in the dissent.
Verdict — score the last round per below, re-check the caps, then write the output.

Scoring

Use the last round that ran. Every number comes from the cards — the chair computes, never invents.

Option score = the average of members' scores for that option, one decimal. The top option is the recommendation unless a cap below removes it.
Spread = highest minus lowest member score on the recommended option. Always report it: a 7.0 with spread 1 and a 7.0 with spread 6 are different answers.
Ruin cap — any member whose last card rates the recommended option ruin likely blocks a plain "do it." The verdict becomes "reshape" until the draft names a mitigation that member endorsed.
Door labels — an option counts as one-way if any step in it can't be undone. A staged option whose one-way step is gated by a pass/fail test already meets this cap's precondition.
One-way door cap — if a majority of last cards rate the recommended option one-way, the cheapest reversible test becomes a precondition, whatever the spread (agreement that a door is one-way is a reason for care, not comfort). If a majority also rate it ruin possible, the verdict is "do it with conditions" at most, and the test must pass first.
"Don't do it" wins when it's the top-scoring option. Say so plainly.

Live research pass

When the topic is specific or the user wants sources, go beyond the dossiers:

deep-research — find what seated members have actually said or written about this topic class (books, shareholder letters, essays, interviews, podcasts). The brief archives per deep-research's own convention.
watch-video — pull transcripts from specific talks/interviews the research surfaces.
/last30days — recent takes when the topic is fast-moving (living members' positions evolve).

Save findings per member to research.md in the session folder and point each member at its section, so the cards can cite them ("In the 1997 shareholder letter, Bezos wrote…"). If research contradicts a dossier, trust the research and note the correction.

Grounding rules (non-negotiable)

Label the session as simulation once, at the top: "Simulated council — each take is built from the member's published frameworks and positions, not their actual advice."
No fabricated quotes. Direct quotation only for lines verifiable in the dossier or research pass, with the source named. Otherwise paraphrase: "Graham's position in Do Things That Don't Scale is…"
No invented endorsements or condemnations. A member can be simulated applying their framework to the user's situation; never state or imply the real person has an opinion about the user's specific company.
Living members get extra care. All nine are alive; positions evolve — prefer the research pass for anything time-sensitive, and never simulate them commenting on named competitors, people, or controversies.
Disagree in substance, not caricature. Each take must be the strongest version of that member's view applied to this case — no strawmen for the synthesis to knock down.
If the dossier and the question don't overlap (e.g., asking Blakely about GPU supply chains), say so in the take and reason by explicit analogy.

Output format

> Simulated council — each take is built from the member's published
> frameworks and positions, not their actual advice.

## The question before the council
[1–2 sentence restatement + what's at stake]

## Seated: [A], [B], [C] ([mode], [rounds run])
[One line on why this bench, including who's the dissenter]

## The call: [option] — [do it / do it with conditions / reshape / don't do it]
Scores: A 6.8 · B 7.4 · C 3.1 — spread on B: 3. [Any cap applied, and why.]
Your lean was [lean]; the council [backed it / changed it / split] because [one line].
Hinges on: [any `(assumed)` item the call depends on — "if this is wrong, re-run"]

---

### [Member A] — [their lens, 3–5 words]
[Final position from their last card: the call, the 2–3 arguments that
carried it, what they conceded, whether they moved]
**Bottom line:** [their ONE LINE FOR THE FOUNDER, from their last card]

…

---

## Where the council disagrees
1. **[Crux]** — [A] says X because [framework]; [B] says Y because
   [framework]. The real trade-off: [tension]. Resolved in debate? [how, or
   still open]. What would settle it: [evidence/test].

## Chair's synthesis
[Recommendation fitted to this user's stage and constraints]
- **Conditions:** [the checkable ones from the draft]
- **Cheapest test first:** [what, and the kill criteria]
- **Dissent:** [who, and their strongest argument, unsoftened. If nobody
  dissents, say so and give the strongest argument that went unanswered]
- **Tripwire:** [which member's warning to monitor, and the signal — taken
  from a card's WHAT WOULD CHANGE MY MIND or conditions, not invented]
- **Next:** [skill handoffs — usually /decide to formalize the call]

Full rounds: [path to the session folder]

Members' sections summarize their cards; never rewrite a member's argument into something they didn't say.

In chat, show the header through "Hinges on," plus the conditions, cheapest test, and dissent — under 200 words — then the path to verdict.md. If conditions must be cut for length, keep the ones the dissent or the kill criteria refer to. The member sections and disagreement map live in the file; show them in chat only when asked. A user who says "just give me the answer" gets exactly that.

Adding a custom member

Users can extend the bench ("add my own advisor"). Create a dossier following references/advisor-template.md and save it to ${MAKERSKILLS_CONFIG:-$HOME/.config/makerskills}/maker-council/advisors/<kebab-name>.md — never inside the skill folder (upgrades wipe it). Two grounding standards:

Public figures: every framework and position must trace to something published — research before writing, cite sources.
Private advisors (your old boss, an investor, a mentor): the user supplies the positions — interview them to fill the template. Never invent views for a real private person.

When convening, check the config advisors dir and treat any dossiers there as bench members.

Anti-patterns

The agreeing council — five takes that bless the existing plan. Re-seat with a real dissenter, and run the unanimity check.
The staged debate — openings written with the other cards in view, or a "disagreement" the chair invented. Round 1 is blind for a reason.
Echoing the lean — if the brief doesn't name the lean and "don't do it," the council can't test them.
Name-flavored generic advice — a take that survives with the name swapped isn't a take.
Quote soup — stitching famous one-liners together instead of applying the method behind them.
Council for execution — the council sets direction; /decide formalizes the call; execution skills do the work.
Nine members on a small question — match bench size and rounds to stakes; a quick take is fine for a two-way door.

Composes with

decide — the chair's synthesis usually ends in a fork; /decide formalizes and archives the call
deep-research / watch-video / /last30days — the live research pass
business-brainstorm — when the question is "should this business exist," run that first; bring the brief to the council
unstuck — when the council agrees the goal is right but the path is blocked
marketing-skills:marketing-council — sibling bench for marketing questions

About this skill

Automatic

Product Management pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/8/2026

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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

coreyhaines31 / makerskills

skills/maker-council/SKILL.md

View on GitHub ↗

License MITImported 11/8/2026Version v3Bundled 2 files

Illustrative Want to see what coreyhaines31/maker-council would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

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

coreyhaines31/marketing-councilWhen the user wants multiple expert perspectives on a marketing question — a simulated board of advisors staffed by legendary marketers (Seth Godin, David Ogilvy, Eugene Schwartz, April Dunford, Rory Sutherland, Alex Hormozi, Byron Sharp, and more). Also use when the user mentions 'marketing council,' 'board of advisors,' 'advisory board,' 'what would Seth Godin say,' 'what would Ogilvy think,' 'channel Hormozi,' 'get multiple perspectives,' 'debate this,' 'have the council review,' 'marketing mClose match+45 pts pass-rate lift

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

From the same source and nearby in the catalogue.

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What it changes

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Use this skill

/maker-council — Your personal board of advisors

Before starting

Session modes

The bench

Seating the council

Session protocol

Scoring

Live research pass

Grounding rules (non-negotiable)

Output format

Archive

Adding a custom member

Anti-patterns

Composes with

SkillSafety

Source

Other skills for the same job

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