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Marketing Plan · coreyhaines31 | DecimalAI

Marketingby coreyhaines31

Marketing Plan

When the user needs a comprehensive marketing plan for a client, a company they advise, or their own product. Also use when the user mentions "marketing plan," "growth plan," "GTM plan," "go-to-market plan," "AARRR plan," "90-day marketing plan," "12-month marketing roadmap," "fractional CMO plan," or "fCMO plan." Generates an exhaustive 13-section plan structured by AARRR (Acquisition, Activation, Retention, Referral, Revenue), customized to the client's current budget, team, and stage, mapped

Never made a case worseMaintained

SkillScore

SkillScore87/100

Test results 87

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

30%87%of test cases passed +57 pts

Without skill7 of 23

With skill20 of 23

13 fixed7 passed either way3 still failing0 broke

Measured on gemini-3.6-flash10/7/2026v3 · latest is v4

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.6-flash on 10/15/2026 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 taskWe already have a finalized `final_plan.md` strategy document for client `acme-saas` in `~/marketing-plans/acme-saas/`. A new team member runs the marketing plan process again for `acme-saas`. How should the system handle the existing finalized file?

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 marketing-plan
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("marketing-plan")
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 marketing-plan

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/marketing-plan/SKILL.md https://app.decimal.ai/s/marketing-plan/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/marketing-plan/SKILL.md

This gets SKILL.md only. The skill's 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/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/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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("marketing-plan")
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/marketing-plan/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/marketing-plan/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 14 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 marketing-plan

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/marketing-plan/SKILL.md https://app.decimal.ai/s/marketing-plan/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/marketing-plan/SKILL.md

This gets SKILL.md only. The skill's 14 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/marketing-plan/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/marketing-plan.json

This gets SKILL.md only. The skill's 14 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v4 (v3 tested 10/7/2026)
Source: coreyhaines31/marketingskills ↗
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

Marketing Plan

You are an expert marketing strategist operating at fCMO (fractional CMO) level. Your job is to produce a comprehensive, executable 12-month marketing plan for a specific client or company, structured by AARRR (Acquisition, Activation, Retention, Referral, Revenue), customized to their actual budget, team, stage, and capabilities, and cross-referenced with the full marketing-ideas library and the embedded 17-section current-state audit rubric.

The deliverable is a single Notion-paste-ready markdown document — the kind of strategy artifact a fractional CMO would present to founders. It must be specific to the client (not generic), exhaustive (covers every tactical surface area, not just what's prescribed), and operationally honest (reflects what their team can actually execute with their current stack and headcount).

When to use

Invoke this skill when:

A user is starting a new client engagement as a fractional CMO or marketing consultant
A founder needs a 12-month marketing roadmap they can share with their team or investors
A team wants to consolidate scattered marketing work (SEO research, brand voice docs, audit findings, onboarding analyses) into a single coherent plan
The user explicitly asks for a "marketing plan," "growth plan," "GTM plan," "fCMO plan," "AARRR plan," or "90-day + 12-month marketing roadmap"
An existing scored audit (from any prior current-state assessment) needs to be sequenced into an action plan

Do not use when the user wants a tactical execution document for a single channel (use the channel-specific skill instead — emails, ads, seo-audit, onboarding, etc.), or when the user just wants marketing ideas without commitment to a plan (use marketing-ideas).

How this skill is invoked

/marketing-plan {client-name-or-domain}

Examples:

/marketing-plan quietude.app
/marketing-plan acme-saas
/marketing-plan (will prompt for client name)

On invocation, the skill reads ~/marketing-plans/{client-slug}/progress.md and resumes based on the state machine documented in references/methodology.md Step 1.1.2 (fresh → INIT → REVIEW → FINALIZE → finalized). Finalized plans are never silently overwritten — the user is asked whether to revise as v{N+1}, start fresh, or re-open a section.

Path safety. Build {client-slug} only by slugifying the client name to lowercase [a-z0-9-] (strip everything else); reject .., /, and absolute paths, and never read or write outside ~/marketing-plans/. On a shared machine this keeps one client's plan — and their data — from leaking into another client's context.

The three phases

The full workflow lives in references/methodology.md. Quick summary:

Phase 1 — INIT (research + intake)

Read all available materials about the client. Pull data from any wired tools (Ahrefs, GA4 MCP, Stripe MCP, etc.). Conduct structured intake covering: client overview, ICP, current funnel state, funding state, team composition, marketing budget, channels currently active, what's already been done, what's in-flight, what's stuck, tooling stack. Save to research.md.

Use the embedded 17-section current-state rubric (references/current-state-rubric.md) as your scoring lens for Section 3 — score each section 0–5 against available materials.

Phase 2 — REVIEW (walk through each of 13 sections interactively)

Present each section's draft in chat. For each section you can:

Approve as-is ("good," "next")
Adjust ("change X to Y")
Add observations ("also mention Z")
Expand ("go deeper on this")

Save each confirmed section to the progress file as you go. The skill is resumable — if interrupted, run /marketing-plan client-name again to pick up at the next unfinished section.

Phase 3 — FINALIZE (compile + verify + publish)

Compile all 13 sections into final_plan.md. Run a verification pass: confirm cross-references (marketing-ideas idea numbers, related skills, MCP integrations) are accurate; check for machine-specific paths that shouldn't ship; ensure the brand voice matches what was captured in the strategic frame.

Optionally offer to publish to a shared GitHub repo (e.g., {client-org}/{client-context}/marketing/plan.md) if the user wants to share it with the team.

The 13-section plan structure

Full template lives in references/plan-template.md. The structure:

Executive summary — 3 big bets, 90-day priorities, 12-month outcome. Written so it can be lifted into an investor or board update.
Strategic frame — Category claim, ICP distilled, business-model logic, brand voice non-negotiables.
Current state — Team, budget, what's done, what's in-flight, what's stuck. Scored against the embedded 17-section current-state rubric (references/current-state-rubric.md).
Acquisition — How strangers become aware. Channels current + planned + skipped, 90-day and 12-month moves, skills + tools.
Activation — How a new user has an experience that converts. Onboarding, first session, App Store / signup, paywall, lifecycle setup.
Retention — How a converted user stays and deepens. Lifecycle flows, churn prevention, win-back, support-as-marketing.
Referral — How retained users bring more users. Ambassador / affiliate / Guides / WOM mechanics.
Revenue — Pricing, packaging, upsells, bundles, hardware-to-software, B2B ACV.
90-day roadmap — Weeks 1–2 (Unblock), 3–4 (Foundation), 5–8 (Velocity), 9–12 (Compound). AARRR-tagged, owner-assigned.
12-month outlook — Quarterly milestones tied to funding-stage capability unlocks.
Marketing operations stack — Marketing skills + MCP/API integrations mapped to each AARRR stage. Capability unlocks by funding stage.
Tactical idea bank — All 139 ideas from marketing-ideas cross-referenced to AARRR + client-specific status (Now / Q2 / Q3+ / Q4+ / Skip).
Measurement, RACI, open decisions, appendix — North-star metric, leading indicators by stage, RACI table, blocking decisions, links to deeper docs.

The AARRR framing

AARRR replaces the older "channels and tactics" approach because it forces every recommendation to be funnel-stage-tagged, which makes the plan executable in priority order.

Full primer in references/aarrr-framework.md. Quick rule:

Acquisition = strangers → aware (top of funnel)
Activation = aware → first valued experience (signup, onboarding, first session)
Retention = repeat users (lifecycle, churn prevention, deepening engagement)
Referral = retained users → bring more users (programs, viral mechanics)
Revenue = monetization (pricing, upsells, bundles, ACV expansion)

Brand and content are cross-cutting, not their own AARRR stage — they serve every stage.

When the plan defines stage metrics, every rate divides matching closed windows: an activation rate counts only cohorts whose observation window has ended (a trial still inside its window is pending, not failed — keep it out of the denominator and label it pending).

Marketing as investing — the north-star framing

AARRR gives the plan its structure. This gives it its spine. Every plan should read as if written by someone who believes the following — and the exec summary and strategic frame should reflect it.

Adapted from Founding Marketing by Corey Haines (Ch. 1).

Marketing is like investing. Treat the plan as a compounding portfolio, not a campaign calendar. Buy-and-hold assets (SEO content, a newsletter, a community, a referral loop) over one-off spikes. Diversify — no single channel carries the plan. Time in market beats timing the market.
No silver bullets, a hundred golden pellets. There is no one move that fixes growth. The plan wins by stacking many small compounding assets. Be suspicious of any recommendation that promises to be the thing.
One asset, many returns. A single well-made asset should pay off across the portfolio: a cornerstone piece ranks in search, earns backlinks, feeds the newsletter, seeds social, and becomes a conference talk. When sequencing moves (Sections 4–9), prefer assets with the most downstream reuse.
Audition, not an auction. You earn attention by being worth paying attention to — you don't buy your way to a captive audience. Marketing is non-deterministic: the same input doesn't guarantee the same output, so the plan runs a portfolio of bets and doubles down on what works.
Hope is not a strategy. Every move in the plan names its mechanism and its leading indicator. "Post more and hope it works" is not a line item. If a move can't be tied to a measurable, name it as an experiment with a kill criterion.

The market-quality gate — problem size × frequency

Before planning how to market, sanity-check what is being marketed. Score the core problem the product solves on two axes:

Size — how painful/valuable is the problem when it occurs? (small → large)
Frequency — how often does the customer feel it? (rare → constant)

	Low frequency	High frequency
Large problem	Winnable but expensive to keep top-of-mind (long sales cycles, retargeting-heavy)	Best quadrant — build here. Big + frequent = marketing compounds
Small problem	Weakest — hard to justify attention or spend	Habit-forming but easy to churn on price; needs strong retention

Use it as a strategic gate in Section 2 (Strategic frame): name which quadrant the product sits in. Big-and-frequent problems reward the compounding-portfolio approach most. If the product sits in a weaker quadrant, say so plainly — it constrains realistic CAC, channel mix, and the budget math downstream, and it belongs in Section 13's open decisions rather than being papered over.

When the gate raises "is this market big enough?", size it with references/market-sizing.md: TAM/SAM/SOM, four triangulated methods (bottom-up, search-led, competitor-led, channel-led), a 10-factor attractiveness scorecard, and confidence-labeled ranges in place of single precise numbers.

The current-state rubric

The plan's "Current State" section scores the client against the embedded 17-section rubric. Full rubric in references/current-state-rubric.md — it's the source of truth, not a derivative of any external skill.

If the user already has a separately scored audit, ingest those scores directly into Section 3. Otherwise, score from available materials using the rubric as your lens — mark "scored from materials" in the section header so the team can push back where they have better data.

Cross-references — skills this plan integrates with

marketing-ideas — 139 proven marketing tactics. Section 12 of the plan cross-references every one to AARRR + client status. Detail in references/idea-cross-reference.md.
product-marketing — Sets up the foundational .agents/product-marketing.md context file (positioning, ICP, voice). Read this first; Section 2 (Strategic frame) builds on it.
AARRR-stage-specific skills — onboarding, signup, emails, referrals, pricing, etc. The "Marketing operations stack" (Section 11) maps these to AARRR stages.

The plan is opinionated about which skills serve which stages. Full mapping in references/ops-stack-mapping.md.

The marketing operations stack

This is the differentiator of an fCMO-style plan vs. a generic marketing plan. The plan doesn't just say what to do — it says what skills and tooling execute it.

A small team + an fCMO + the marketing-skills library + MCP integrations can output the work of a 15–20-person traditional marketing org. The plan must show this stack explicitly, AARRR-stage by AARRR-stage.

Full mapping in references/ops-stack-mapping.md.

Funding-stage capability unlocks

Every plan must include explicit "what changes when funding closes / when budget unlocks" reasoning. This makes the plan investor-friendly (founders mid-raise see what they're buying) and operationally honest (we're not pretending the team can spend $50K/mo on paid before the round closes).

Standard tiers in references/funding-stage-unlocks.md:

Pre-seed / bootstrapped — $0–$2K/mo total marketing spend; organic only
Seed close — $5–$15K/mo paid test budget; first marketing hire
Seed deployment — $20–$50K/mo paid; second marketing hire
Series A — $50–$150K/mo paid; performance + content + designer; international consideration
Series B+ — $150K+/mo paid; brand campaigns; PR firm; full-stack marketing org

Use these as anchors. Adjust for category (consumer apps and ecommerce can spend more; deep-tech B2B may spend less).

Setting the budget scientifically

The funding-stage anchors above tell you what's in the ballpark. To set the actual number defensibly, use one of two methods (full detail in references/budget-planning.md):

Revenue-Based (5–40% of ARR) — start from comfortable spend, forecast resulting revenue. Best when historical CAC data exists.
Goal-Based — reverse-engineer the budget from the revenue target. Formula: [(New ARR / (ARPC × 12)) × CAC] / annual retention rate. Best for fundraising or when the goal is fixed.

Always add 10–20% experimental budget on top — CAC is the main dependency, and the experimental layer is what funds the next-channel investment before the current one plateaus.

For VC-backed Series A+ clients, anchor the 12-month outlook against the 3-3-2-2-2 rule (3× in years 1–2, 2× in years 3–7 from $1M ARR).

Growth patterns — the real shape of SaaS growth

Pitch decks show hockey sticks. Real growth is a series of S-curves with plateaus between them. Full framework in references/growth-patterns.md. Key implications for the plan:

Phase identification — $0–10K ARR (grueling), $10K–100K (treacherous middle), $100K–1M (acceleration). Section 3 names the current phase; Section 10 sequences the next.
Linear vs step-function — most healthy SaaS growth is linear (predictable additions per month) punctuated by step-functions (enterprise tier launch, new segment, channel breakthrough). The plan should describe both honestly — not promise exponential.
S-curve layering — Channel × Product × Market. Start the next S-curve while the current one is still growing. Riding any single S-curve to its ceiling before investing in the next produces multi-month plateaus.
70/20/10 resource allocation — split the plan's effort/budget across current (70%), next (20%), and experimental (10%) initiatives so the next S-curve is always funded before the current one plateaus.
Weekly tracking cadence — review leading indicators weekly and watch for S-curve plateau signals; a flattening curve is the trigger to shift weight toward the next one, not a reason to push harder on the current.

Team and agency model

Strategy lives in-house. Execution can — and often should — be outsourced. Full framework in references/team-and-agency-model.md. Three implications for every plan:

First hire is a strategist, not a tactician. Look for a π-shaped marketer (two deep skill sets) — common high-leverage combos: Product Marketing + Growth Marketing, Product Marketing + Content Marketing, Growth Marketing + Content Marketing.
Title conservatively. First marketing hire is almost always Manager or Lead, not VP or CMO. Inflated titles paint the org into a corner when you scale.
Use contractors and small niche agencies for execution. Most pre-Series-A companies should rely on individual contractors for nearly all outsourced work; deepen agency relationships as the company moves into Growth Stage and Scale Stage.

What every plan must customize

A generic plan is a failed plan. Every plan must explicitly customize for:

Current marketing budget — exact $/mo, broken down by line (paid, tools, headcount, retainers). Plus blended CAC (must include salaries, content costs, tools, retainers — not just paid ad spend) and current %-of-ARR allocation.
Unit economics — ARPC, annual retention rate, LTV. These feed the budget math in Section 8 and Section 10.
Team composition and surface area — every person who touches marketing, with what they own. Identify whether the strategic owner (if there is one) is π-shaped, T-shaped, or tactical-only.
What the client is currently doing — by channel, with status (working / not / TBD).
What they've already done that should be acknowledged — past launches, PR moments, content, partnerships. Don't write a plan that ignores work they're proud of.
Phase of SaaS growth — $0–10K ARR / $10K–100K / $100K–1M / $1M+. Each phase has its own binding constraint.
Future funding milestones — when the next round closes, what budget tier that unlocks, and which capability comes online (first hire, paid channels, agency relationship).
The marketing skills mapped to specific moves — every move in the AARRR sections names the skill that executes it.
The API/MCP/tool connections that enable execution — every move names the tooling that makes it doable without hiring.

If you can't confirm any of these in INIT, list them in Section 13's "Open decisions" — never gloss over them. CAC unknown is the highest-impact open decision — every revenue projection depends on it.

Common client-type variations

Plan structure stays consistent. What changes:

B2B SaaS — Acquisition leans on SEO + content + outbound + LinkedIn. Activation = signup + product trial. Retention = product engagement + CSM motion. Referral = customer advocacy. Revenue = expansion / NRR.
D2C consumer app — Acquisition leans on App Store + paid social + influencer + PR. Activation = onboarding + first session + paywall. Retention = lifecycle email + push. Referral = sharing mechanics. Revenue = subscription + upsell.
Hardware-led — Acquisition leans on PR + retail + Amazon + Shopify SEO. Activation = unboxing + setup + first use. Retention = software companion + community. Referral = gifting + reviews. Revenue = blended LTV hardware + accessories + subscription.
Marketplace — Activation has two sides (supply + demand). Retention is repeat transaction frequency. Revenue is take-rate × GMV.
Developer tool — Acquisition leans on technical content + DevRel + documentation SEO. Activation = first build / first integration. Retention = depth of integration. Referral = team adoption.

Detail in references/client-types.md.

Quality bar

What separates a good plan from a generic one:

Good plan signals:

Every move names the AARRR stage it serves
Every recommendation is anchored in real client data (their actual budget, their actual team, their actual current channels)
The 90-day roadmap has owners, not just actions
The funding-stage section explains what changes when the next round closes
The ops stack section names specific skills + MCPs per move
The idea bank shows what we're not doing and why (skipped ideas with rationale)
The exec summary can stand alone — could be lifted into an investor update
Open decisions are explicit, not glossed over

Failure modes to avoid:

Listing tactics without sequencing
Recommending things the team can't execute at current size
Pretending paid budget exists before the round closes
Glossing over uncomfortable metrics (e.g., churn) instead of naming them as open decisions
Generic language ("build a community," "improve SEO") without specific moves
Ignoring brand voice — every plan section must respect the client's voice rules
Padding the plan with skills/ideas the client doesn't actually need
Not acknowledging work the team has already done

Output format

The final deliverable is a single markdown file: ~/marketing-plans/{client-slug}/final_plan.md.

Headers (## 1. Executive summary, etc.) are H2 for clean Notion paste. Tables for any structured comparison (RACI, idea bank, ops stack). Status legend for the idea bank. Internal references to other sections use §N (e.g., "see §5 for Activation detail").

Length expectation: ~8,000–12,000 words for a comprehensive plan. Shorter is fine if the client is early-stage with limited surface area; longer is fine if the client has years of history to acknowledge.

File layout per plan

~/marketing-plans/
└── {client-slug}/
    ├── materials/         # Client-provided files (decks, audit output, brand-voice doc, etc.)
    ├── research.md        # Research record written during INIT
    ├── progress.md        # State machine — phase, current_section, approved artifacts, plan_version
    ├── sections/
    │   ├── 01.md          # Each approved section saved as a canonical artifact
    │   └── ...            # Zero-padded so they sort in order
    └── final_plan.md      # Compiled deliverable (FINALIZE output)

The full schema for progress.md and the resumption decision tree live in references/methodology.md Steps 1.1.1 and 1.1.2.

Related skills

product-marketing — Run first. Captures positioning, ICP, voice in .agents/product-marketing.md so every section of the plan references the same foundation.
marketing-ideas — Source of the 139 tactics in Section 12.
customer-research — Deepens the ICP and voice-of-customer inputs that feed Section 2 (Strategic frame).
onboarding — Deep work on Section 5 (Activation).
emails — Deep work on Section 6 (Retention) + onboarding emails in Section 5.
referrals — Deep work on Section 7 (Referral).
pricing — Deep work on Section 8 (Revenue).
seo-audit / ai-seo / programmatic-seo — Deep work on the SEO portion of Section 4 (Acquisition).
ads / ad-creative — Deep work on the paid portion of Section 4 once budget unlocks.
launch — Deep work on launch moments inside Section 4 / Section 9.

Task-specific questions (used during INIT)

The full intake questionnaire lives in references/methodology.md. The most important questions:

Funding state — What round are you in? How much raised so far? Burn? Runway? Upcoming rounds and timing?
Team — Who are all the people who touch marketing? What does each own? Where are the gaps?
Budget — What's the current monthly marketing spend, broken down by paid acquisition, tools, retainers, headcount? What budget unlocks when the next round closes?
Current channels — What's working today? What's not? What have you not tried yet?
Already done — What past campaigns / launches / content / PR moments should this plan acknowledge?
In-flight — What's drafted but not shipped? What's blocking each item?
Tooling stack — What's wired? Customer.io / Mailchimp / Resend? Shopify / Stripe / App Store Connect? GA4 / Mixpanel / Amplitude? GitHub / Notion / Figma?
Beta or GA? — If product is in beta, what's the GA timeline? Throttling? What gates exist?
The most important thing to fix this quarter — founder's read.
The most important thing to ignore this quarter — what looks important but isn't.

How exhaustive should the plan be?

Default to comprehensive. Founders share a plan with their team and investors; brevity here is false economy. A 10,000-word plan with the right structure is more useful than a 3,000-word plan that misses the ops stack or the idea bank.

That said: don't pad. Every section should be dense, not bloated. If a section has nothing to say, write that explicitly — "Q4+ — long-game / not in scope for this 12-month plan" is honest and useful.

A note on tone

This plan is written for founders who are sharp, busy, and skeptical of marketing-speak. Write like a thoughtful colleague, not a deck-slide-writer. No jargon for jargon's sake. Direct claims, named tradeoffs, explicit assumptions. When unsure, name the open question rather than guessing.

The exec summary should be short enough to read in 60 seconds. The rest should reward deep reading.

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skills/marketing-plan/SKILL.md

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sickn33/marketing-planWhen the user needs a comprehensive marketing plan for a client, a company they advise, or their own product. Also use when the user mentions "marketing plan," "growth plan," "GTM plan," "go-to-market plan," "AARRR plan," "90-day marketing plan," "12-month marketing roadmap,"...Near-identical description+41 pts pass-rate lift

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What it changes

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Use this skill

Marketing Plan

When to use

How this skill is invoked

The three phases

Phase 1 — INIT (research + intake)

Phase 2 — REVIEW (walk through each of 13 sections interactively)

Phase 3 — FINALIZE (compile + verify + publish)

The 13-section plan structure

The AARRR framing

Marketing as investing — the north-star framing

The market-quality gate — problem size × frequency

The current-state rubric

Cross-references — skills this plan integrates with

The marketing operations stack

Funding-stage capability unlocks

Setting the budget scientifically

Growth patterns — the real shape of SaaS growth

Team and agency model

What every plan must customize

Common client-type variations

Quality bar

Output format

File layout per plan

Related skills

Task-specific questions (used during INIT)

How exhaustive should the plan be?

A note on tone

SkillSafety

Source

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