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Marketing Strategy & PMM · borghei | DecimalAI

Marketingby borghei

Marketing Strategy & PMM

Product marketing skill for positioning, GTM strategy, competitive intelligence, and product launches. Covers April Dunford positioning, ICP definition, competitive battlecards, launch playbooks, and international market entry.

Maintained

SkillScore

SkillScore65/100

Test results 65

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

17%65%of test cases passed +48 pts

Without skill4 of 23

With skill15 of 23

11 fixed4 passed either way8 still failing0 broke

Measured on gemini-3.6-flash8/20/2026v1 · latest is v2

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen building the top level of our product messaging hierarchy, what is the target length constraint for the primary headline?

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 borghei-marketing-strategy-pmm
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("borghei-marketing-strategy-pmm")
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 borghei-marketing-strategy-pmm

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/borghei-marketing-strategy-pmm/SKILL.md https://app.decimal.ai/s/borghei-marketing-strategy-pmm/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/borghei-marketing-strategy-pmm/SKILL.md

This gets SKILL.md only. The skill's 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/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/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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("borghei-marketing-strategy-pmm")
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/borghei-marketing-strategy-pmm/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/borghei-marketing-strategy-pmm/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 7 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 borghei-marketing-strategy-pmm

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/borghei-marketing-strategy-pmm/SKILL.md https://app.decimal.ai/s/borghei-marketing-strategy-pmm/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/borghei-marketing-strategy-pmm/SKILL.md

This gets SKILL.md only. The skill's 7 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/borghei-marketing-strategy-pmm/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/borghei-marketing-strategy-pmm.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/20/2026)
Source: borghei/claude-skills ↗
License: NOASSERTION

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 Strategy & PMM

Product marketing patterns for positioning, GTM strategy, and competitive intelligence.

ICP Definition Workflow
Positioning Development
Competitive Intelligence
Product Launch Planning
Sales Enablement
International Expansion
Reference Documentation

Clarify First

Before building the PMM artifact, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

Deliverable type: positioning statement, GTM plan, battlecard, or sales enablement (determines which workflow runs).
Target customer / ICP: best-fit segment, ideally top 20% by LTV (drives the FOR clause and targeting).
Competitive alternatives: direct, adjacent, and status-quo options (drives the UNLIKE clause and battlecards).
Unique differentiation: what only you do and why it matters (drives the positioning differentiator and value prop).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

ICP Definition Workflow

Define ideal customer profile for targeting:

Analyze existing customers (top 20% by LTV)
Identify common firmographics (size, industry, revenue)
Map technographics (tools, maturity, integrations)
Document psychographics (pain level, motivation, risk tolerance)
Define 3-5 buyer personas (economic, technical, user)
Validate against sales cycle and churn data
Score prospects A/B/C/D based on ICP fit
Validation: A-fit customers have lowest churn and fastest close

Firmographics Template

Dimension	Target Range	Rationale
Employees	50-5000	Series A sweet spot
Revenue	$5M-$500M	Budget available
Industry	SaaS, Tech, Services	Product fit
Geography	US, UK, DACH	Market priority
Funding	Seed to Growth	Willing to adopt

Buyer Personas

Economic Buyer (signs contract):

Title: VP, Director, Head of [Department]
Goals: ROI, team productivity, cost reduction
Messaging: Business outcomes, ROI, case studies

Technical Buyer (evaluates product):

Title: Engineer, Architect, Tech Lead
Goals: Technical fit, easy integration
Messaging: Architecture, security, documentation

User/Champion (advocates internally):

Title: Manager, Team Lead, Power User
Goals: Makes job easier, quick wins
Messaging: UX, ease of use, time savings

ICP Validation Checklist

5+ paying customers match this profile
Fastest sales cycles (< median)
Highest LTV (> median)
Lowest churn (< 5% annual)
Strong product engagement
Willing to do case studies

Positioning Development

Develop positioning using April Dunford methodology:

List competitive alternatives (direct, adjacent, status quo)
Isolate unique attributes (features only you have)
Map attributes to customer value (why it matters)
Define best-fit customers (who cares most)
Choose market category (head-to-head, niche, new category)
Layer on relevant trends (timing justification)
Test with 10+ customer interviews
Validation: 7+ customers describe value unprompted

Positioning Statement Template

FOR [target customer]
WHO [statement of need]
THE [product] IS A [category]
THAT [key benefit]
UNLIKE [competitive alternative]
OUR PRODUCT [primary differentiation]

Value Proposition Formula

Template: [Product] helps [Target Customer] [Achieve Goal] by [Unique Approach]

Example: "Acme helps mid-market SaaS teams ship 2x faster by automating project workflows with AI"

Messaging Hierarchy

Level	Content	Example
Headline	5-7 words	"Ship faster with AI automation"
Subhead	1 sentence	"Automate workflows so teams focus on what matters"
Benefits	3-4 bullets	Speed, quality, collaboration, cost
Features	Supporting evidence	AI automation → 10 hrs/week saved
Proof	Social proof	Customer logos, stats, case studies

Competitive Intelligence

Build competitive knowledge base:

Identify tier 1 (direct), tier 2 (adjacent), tier 3 (status quo)
Sign up for competitor products (hands-on evaluation)
Monitor competitor websites, pricing, messaging
Analyze sales call recordings for competitor mentions
Read G2/Capterra reviews (pros and cons)
Track competitor job postings (roadmap signals)
Update battlecards monthly
Validation: Sales team uses battlecards in 80%+ competitive deals

Competitive Tier Structure

Tier	Definition	Examples
1	Direct competitor, same category	[Competitor A, B]
2	Adjacent solution, overlapping use case	[Alt Solution C, D]
3	Status quo (what they do today)	Spreadsheets, manual, in-house

Battlecard Template

COMPETITOR: [Name]
OVERVIEW: Founded [year], Funding [stage], Size [employees]

POSITIONING:
- They say: "[Their claim]"
- Reality: [Your assessment]

STRENGTHS:
1. [What they do well]
2. [What they do well]

WEAKNESSES:
1. [Where they fall short]
2. [Where they fall short]

OUR ADVANTAGES:
1. [Your advantage + evidence]
2. [Your advantage + evidence]

WHEN WE WIN:
- [Scenario where you win]

WHEN WE LOSE:
- [Scenario where they win]

TALK TRACK:
Objection: "[Common objection]"
Response: "[Your response]"

Win/Loss Analysis

Track monthly:

Win rate by competitor
Top win reasons (product fit, ease of use, price)
Top loss reasons (missing feature, price, relationship)
Action items for product, sales, marketing

Product Launch Planning

Plan launches by tier:

Tier	Scope	Prep Time	Budget
1	New product, major feature	6-8 weeks	$50-100k
2	Significant feature, integration	3-4 weeks	$10-25k
3	Small improvement	1 week	<$5k

Tier 1 Launch Workflow

Execute major product launch:

Kickoff meeting with Product, Marketing, Sales, CS
Define goals (pipeline $, MQLs, press coverage)
Develop positioning and messaging
Create sales enablement (deck, demo, battlecard)
Build campaign assets (landing page, emails, ads)
Train sales and CS teams
Execute launch day (press, email, ads, outbound)
Monitor and optimize for 30 days
Validation: Pipeline on track to goal by week 2

Launch Day Checklist

Launch Metrics

Metric	Leading (Daily)	Lagging (Weekly)
Traffic	Landing page visitors	-
Engagement	Demo requests, signups	Feature adoption %
Pipeline	MQLs generated	SQLs, pipeline $
Revenue	-	Deals closed, revenue

Sales Enablement

Equip sales team with PMM assets:

Create sales deck (15-20 slides, visual-first)
Build one-pagers (product, competitive, case study)
Develop demo script (30-45 min with discovery)
Write email templates (outreach, follow-up, closing)
Create ROI calculator (input costs, output savings)
Conduct monthly enablement calls
Deliver quarterly training (positioning, competitive)
Validation: Sales uses assets in 80%+ of opportunities

Sales Deck Structure

Slide	Content
1-2	Title, agenda
3-4	Company intro, problem statement
5-7	Solution, key benefits, demo
8-10	Differentiation, case study, pricing
11-12	Implementation, support, next steps

Demo Flow

1. Intro (2 min): Who we are, agenda
2. Discovery (5 min): Their needs, pain points
3. Demo (20 min): Product focused on their use case
4. Q&A (10 min): Objection handling
5. Next steps (3 min): Trial, POC, proposal

Sales-Marketing Handoff

Handoff	Frequency	Content
Weekly sync	30 min	Win/loss, competitive, new assets
Monthly enablement	60 min	Product updates, training
Quarterly review	Half-day	Results, strategy, planning

International Expansion

Enter new markets systematically:

Validate market demand (inbound leads, TAM analysis)
Localize website, pricing, legal
Establish sales coverage (hire or agency)
Adapt messaging for cultural fit
Build local partnerships and references
Launch localized campaigns
Monitor CAC and conversion by market
Validation: 3+ paying customers from market in first 90 days

Market Priority (Series A)

Market	Timeline	Budget %	Target ARR
US	Months 1-6	50%	$1M
UK	Months 4-9	20%	$500k
DACH	Months 7-12	15%	$300k
France	Months 10-15	10%	$200k
Canada	Months 7-12	5%	$100k

Localization Checklist

Website translation (professional, not machine)
Currency and pricing localized
Local phone number and address
Legal compliance (GDPR, PIPEDA)
Local payment methods
Sales coverage during local hours
Local case studies and references

Reference Documentation

Positioning Frameworks

references/positioning-frameworks.md contains:

April Dunford 5-step positioning process
Geoffrey Moore positioning statement template
Positioning validation interview protocol
Competitive positioning map construction

Launch Checklists

references/launch-checklists.md contains:

Tier 1/2/3 launch checklists
Week-by-week launch timeline
Launch day runbook
Post-launch metrics dashboard

International GTM

references/international-gtm.md contains:

US, UK, DACH, France, Canada playbooks
Market-specific channel mix and messaging
Localization requirements per market
Entry timeline and budget allocation

Messaging Templates

references/messaging-templates.md contains:

Value proposition formulas
Persona-specific messaging
Competitive response scripts
Objection handling templates
Channel-specific copy (landing pages, emails, ads)

PMM KPIs

Metric	Target	Measurement
Product adoption	>40% in 90 days	Feature usage after launch
Win rate	>30% competitive	Deals won vs. competitors
Sales velocity	-20% YoY	Days from SQL to close
Deal size	+25% YoY	Average contract value
Launch pipeline	3:1 ROMI	Pipeline $ : marketing spend

Quick Reference

PMM Monthly Rhythm

Week	Focus
1	Review metrics, update battlecards
2	Create assets, publish content
3	Support launches, optimize campaigns
4	Monthly report, plan next month

Proactive Triggers

No documented positioning -- Without clear positioning, all marketing is guesswork. Start with April Dunford framework.
Messaging differs across channels -- Inconsistent story confuses buyers. Align messaging hierarchy across all touchpoints.
No ICP defined -- Selling to everyone means selling to no one. Define ICP before any campaign spend.
Competitor repositioning -- Market shift detected. Review your positioning and update battlecards.

Output Artifacts

When you ask for...	You get...
"Position my product"	Positioning framework (April Dunford method) with completed output
"GTM strategy"	Go-to-market plan with channels, messaging, and timeline
"Competitive positioning"	Positioning map with competitive gaps and opportunities
"Sales enablement"	Sales deck structure, battlecards, and demo flow

Communication

All output passes quality verification:

Self-verify: source attribution, assumption audit, confidence scoring
Output format: Bottom Line first, then What (with confidence), Why, How to Act
Every finding tagged with confidence level: verified, medium confidence, or assumed

Related Skills

marketing-demand-acquisition: For executing acquisition campaigns planned by PMM.
campaign-analytics: For measuring launch and campaign effectiveness.
content-creator: For creating content assets defined in PMM strategy.
social-media-analyzer: For tracking social media performance of PMM campaigns.

Troubleshooting

Symptom	Likely Cause	Resolution
Positioning resonates internally but customers do not repeat it	Positioning built from company perspective, not customer language	Rerun April Dunford methodology starting from competitive alternatives, not from product features
Win rate against specific competitor below 25%	Battlecard outdated or sales team not using it	Run win_loss_analyzer.py to identify loss patterns; update battlecard monthly; validate 80%+ sales usage
GTM motion producing high MQLs but low pipeline conversion	Wrong GTM motion for ACV and buyer type; marketing-led when should be sales-led	Reassess motion using gtm_planner.py; for ACV >$25K, shift to sales-led or hybrid PLG+sales
Sales enablement assets gathering dust	Assets created without sales input; format does not match how sales actually works	Co-create assets with sales; survey sales on what they need; track asset usage in deal cycles
International expansion burning cash with zero pipeline	Market entered without validating demand (inbound signal, TAM)	Validate 3+ paying customers from market in first 90 days; if not, pause and reassess market priority
Competitive intelligence always reactive to lost deals	No proactive monitoring system; battlecards only updated post-loss	Set up monthly competitor monitoring (website, pricing, job postings, G2 reviews); update battlecards proactively
Messaging differs across website, sales deck, and ads	No messaging hierarchy documented; each team creates independently	Build messaging hierarchy (headline > subhead > benefits > features > proof) and enforce across all touchpoints

Success Criteria

Positioning validated with 10+ customer interviews; 7+ describe value unprompted
Win rate above 30% in competitive deals, measured and tracked monthly
Sales velocity improves 20%+ YoY (days from SQL to close decreasing)
Sales team uses battlecards in 80%+ of competitive opportunities
Product adoption exceeds 40% within 90 days of launch
GTM plan generates 3:1 pipeline-to-marketing-spend ROMI
Messaging hierarchy consistent across all customer-facing touchpoints

Scope & Limitations

In Scope: Product positioning (April Dunford methodology), ICP definition and validation, competitive intelligence and battlecards, GTM strategy and motion selection (PLG, sales-led, marketing-led, community-led), product launch planning, sales enablement, win/loss analysis, international expansion planning, messaging hierarchy, PMM KPIs.

Out of Scope: Brand identity and visual design (see brand-guidelines skill), demand generation execution (see marketing-demand-acquisition skill), content creation (see content-creator skill), pricing strategy optimization, sales process design.

Limitations: Positioning frameworks require real customer input to be effective — internally generated positioning is unreliable. Win/loss analysis requires honest deal outcome data from sales; incomplete data produces misleading patterns. GTM motion recommendations are based on ACV and buyer type heuristics; edge cases may require hybrid approaches. International expansion timelines assume US-first model and may not apply to non-US companies.

Scripts

Script	Purpose	Usage
`scripts/gtm_planner.py`	Generate GTM plans with motion selection, channel strategy, and timeline	`python scripts/gtm_planner.py config.json --demo`
`scripts/win_loss_analyzer.py`	Analyze deal outcomes by competitor, segment, and reason	`python scripts/win_loss_analyzer.py deals.json --demo`
`scripts/battlecard_generator.py`	Generate competitive battlecards with feature comparison and objection handling	`python scripts/battlecard_generator.py competitor.json --demo`

About this skill

Automatic

Marketing & Content pack

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borghei / claude-skills

marketing/marketing-strategy-pmm/SKILL.md

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License MIT + Commons ClauseImported 10/25/2026Version v2Bundled 7 files · scripts

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

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Marketing Strategy & PMM

Table of Contents

Clarify First

ICP Definition Workflow

Firmographics Template

Buyer Personas

ICP Validation Checklist

Positioning Development

Positioning Statement Template

Value Proposition Formula

Messaging Hierarchy

Competitive Intelligence

Competitive Tier Structure

Battlecard Template

Win/Loss Analysis

Product Launch Planning

Tier 1 Launch Workflow

Launch Day Checklist

Launch Metrics

Sales Enablement

Sales Deck Structure

Demo Flow

Sales-Marketing Handoff

International Expansion

Market Priority (Series A)

Localization Checklist

Reference Documentation

Positioning Frameworks

Launch Checklists

International GTM

Messaging Templates

PMM KPIs

Quick Reference

PMM Monthly Rhythm

Proactive Triggers

Output Artifacts

Communication

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Troubleshooting

Success Criteria

Scope & Limitations

Scripts

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