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Marketing Demand & Acquisition · davila7 | DecimalAI

Marketingby davila7

Marketing Demand & Acquisition

Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbooks, HubSpot integration, and international expansion tactics.

Never made a case worse

SkillScore

SkillScore61/100

Test results 61

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

13%61%of test cases passed +48 pts

Without skill3 of 23

With skill14 of 23

11 fixed3 passed either way9 still failing0 broke

Measured on gemini-3.6-flash8/11/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat target company employee size range and buying timeline requirement must a lead satisfy to be qualified as a Sales Qualified Lead (SQL)?

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 davila7-marketing-demand-acquisition
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("davila7-marketing-demand-acquisition")
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 davila7-marketing-demand-acquisition

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/davila7-marketing-demand-acquisition/SKILL.md https://app.decimal.ai/s/davila7-marketing-demand-acquisition/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/davila7-marketing-demand-acquisition/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/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/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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("davila7-marketing-demand-acquisition")
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/davila7-marketing-demand-acquisition/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/davila7-marketing-demand-acquisition/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 1 other file is 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 davila7-marketing-demand-acquisition

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/davila7-marketing-demand-acquisition/SKILL.md https://app.decimal.ai/s/davila7-marketing-demand-acquisition/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/davila7-marketing-demand-acquisition/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/davila7-marketing-demand-acquisition/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/davila7-marketing-demand-acquisition.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/11/2026
Source: davila7/claude-code-templates ↗
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 Demand & Acquisition

Expert acquisition playbook for Series A+ startups scaling internationally (EU/US/Canada) with hybrid PLG/Sales-Led motion.

Keywords

demand generation, paid media, paid ads, LinkedIn ads, Google ads, Meta ads, CAC, customer acquisition cost, lead generation, MQL, SQL, pipeline generation, acquisition strategy, performance marketing, paid social, paid search, partnerships, affiliate marketing, SEO strategy, HubSpot campaigns, marketing automation, B2B marketing, SaaS marketing

Role Coverage

This skill serves:

Demand Generation Manager - Multi-channel campaigns, pipeline generation
Paid Media/Performance Marketer - Paid search/social/display optimization
SEO Manager - Organic acquisition and technical SEO
Affiliate/Partnerships Manager - Co-marketing and channel partnerships

Core KPIs by Role

Demand Gen: MQL/SQL volume, cost per opportunity, marketing-sourced pipeline $, pipeline velocity, MQL→SQL conversion rate

Paid Media: CAC, ROAS, CPL, CPA, incrementality lift, channel efficiency ratio

SEO: Organic sessions, non-brand traffic %, keyword rankings (P1-P3), organic-assisted conversions, technical health score

Partnerships: Partner-sourced pipeline $, partner CAC, net new logos via partners, co-marketing ROI

Tech Stack Integration

HubSpot CRM - Campaign tracking, lead scoring, attribution, workflows Google Analytics - Traffic analysis, conversion tracking, funnel optimization Search Console - Keyword performance, technical issues, indexing LinkedIn Campaign Manager - B2B paid social Google Ads - Search, Display, YouTube Meta Ads - Facebook, Instagram

1. Demand Generation Framework

1.1 Full-Funnel Strategy (2025 Best Practice)

TOFU (Awareness) → MOFU (Consideration) → BOFU (Decision) → Handoff to Sales/Product

TOFU Tactics

Paid social (LinkedIn thought leadership, Meta awareness)
Display advertising (programmatic, retargeting)
Content syndication
SEO (informational keywords)
Partnerships (co-webinars, guest content)
Target: Brand lift, site traffic, early-stage engagement

MOFU Tactics

Paid search (solution keywords)
Retargeting campaigns
Gated content (eBooks, templates, webinars)
Email nurture sequences
Comparison pages (SEO)
Target: MQLs, demo requests, trial signups

BOFU Tactics

Paid search (brand + competitor keywords)
Direct outreach campaigns
Free trial CTAs
Case studies & ROI calculators
Intent-based retargeting
Target: SQLs, demos booked, pipeline $

1.2 Campaign Planning Template

Campaign Brief (use this for every campaign):

Campaign Name: [Q2-2025-LinkedIn-ABM-Enterprise]
Objective: [Generate 50 SQLs from Enterprise accounts ($50k+ ACV)]
Budget: [$15k/month]
Duration: [90 days]
Channels: [LinkedIn Ads, Retargeting, Email]
Audience: [Director+ at SaaS companies, 500-5000 employees, EU/US]
Offer: [Gated Industry Benchmark Report]
Success Metrics:
  - Primary: 50 SQLs, <$300 CPO
  - Secondary: 500 MQLs, 10% MQL→SQL rate, 40% email open rate
HubSpot Setup:
  - Campaign ID: [create in HubSpot]
  - Lead scoring: +20 for download, +30 for demo request
  - Attribution: First-touch + Multi-touch
Handoff Protocol:
  - SQL criteria: Title + Company size + Budget confirmed
  - Routing: Enterprise SDR team via HubSpot workflow
  - SLA: 4-hour response time

1.3 HubSpot Campaign Tracking Setup

Step-by-step:

Create Campaign in HubSpot
- Marketing → Campaigns → Create Campaign
- Name: Q2-2025-LinkedIn-ABM-Enterprise
- Tag all assets (landing pages, emails, ads) with campaign ID

UTM Parameter Structure (critical for attribution)

utm_source={channel}       // linkedin, google, facebook
utm_medium={type}          // cpc, display, email, organic
utm_campaign={campaign-id} // q2-2025-linkedin-abm-enterprise
utm_content={variant}      // ad-variant-a, email-1
utm_term={keyword}         // [for paid search only]

Lead Scoring Configuration
- Navigate to: Settings → Marketing → Lead Scoring
- Campaign engagement: +10-30 points based on action depth
- Channel quality: LinkedIn +5, Google Search +10, Organic +15
Attribution Reports
- Use HubSpot's multi-touch attribution (W-shaped for hybrid motion)
- First-touch: Awareness credit
- Multi-touch: Full journey credit
- Build custom report: Marketing → Reports → Attribution

1.4 International Expansion Considerations

EU Market Entry:

GDPR compliance: Double opt-in for email, explicit consent tracking in HubSpot
Localization: Translate landing pages, ads, emails (DE, FR, ES priority)
Payment: Display prices in EUR
Partnerships: Local co-marketing partners for credibility
Paid channels: LinkedIn most effective for B2B EU, Google Ads second

US/Canada Market Entry:

Messaging: Direct, ROI-focused, less formal than EU
Paid channels: Google Ads + LinkedIn equal priority
Partnerships: Industry associations, review sites (G2, Capterra)
Content: Case studies with $ impact, not just features
Sales alignment: Faster sales cycles, need immediate lead follow-up

Budget Allocation (Series A recommended):

EU: 40% LinkedIn, 25% Google, 20% SEO, 15% Partnerships
US/CA: 35% Google, 30% LinkedIn, 20% SEO, 15% Partnerships

2. Paid Media Optimization

2.1 Channel Strategy Matrix

Channel	Best For	CAC Benchmark	Conversion Rate	Series A Priority
LinkedIn Ads	B2B, Enterprise, ABM	$150-$400	0.5-2%	⭐⭐⭐⭐⭐
Google Search	High-intent, BOFU	$80-$250	2-5%	⭐⭐⭐⭐⭐
Google Display	Retargeting, awareness	$50-$150	0.3-1%	⭐⭐⭐
Meta (FB/IG)	SMB, consumer-like products	$60-$200	1-3%	⭐⭐⭐
YouTube	Product demos, brand	$100-$300	0.5-1.5%	⭐⭐
Reddit/Twitter	Technical audiences	$40-$180	0.5-2%	⭐⭐

2.2 LinkedIn Ads Playbook (Primary B2B Channel)

Campaign Structure:

Account
└─ Campaign Group: [Q2-2025-Enterprise-ABM]
   ├─ Campaign 1: [Awareness - Thought Leadership]
   │  ├─ Ad Set: [CTO/VP Eng, US, Tech Companies]
   │  └─ Creatives: [3 carousel posts, 2 video ads]
   ├─ Campaign 2: [Consideration - Product Education]
   │  ├─ Ad Set: [Engaged audience, retargeting]
   │  └─ Creatives: [2 lead gen forms, 1 landing page]
   └─ Campaign 3: [Conversion - Demo Requests]
      ├─ Ad Set: [Website visitors, content downloaders]
      └─ Creatives: [Direct demo CTA, case study]

Targeting Best Practices:

Company Size: 50-5000 employees (Series A sweet spot)
Job Titles: Director+, VP+, C-level (use LinkedIn's precise targeting)
Industries: Software, SaaS, Tech Services
Matched Audiences: Website retargeting (install Insight Tag), uploaded email lists
Budget: Start $50/day per campaign, scale 20% weekly if CAC < target

Creative Frameworks:

Thought Leadership - Industry insights, no product pitch
Social Proof - Customer logos, testimonials, case study snippets
Problem-Solution - Pain point + your solution in 3 seconds
Demo-First - Show product immediately, skip fluff

LinkedIn Lead Gen Forms vs. Landing Pages:

Lead Gen Forms: Higher conversion (2-3x), lower quality, use for TOFU/MOFU
Landing Pages: Lower conversion, higher quality, use for BOFU/demo requests
HubSpot Sync: Connect LinkedIn Lead Gen Forms via native integration

2.3 Google Ads Playbook (High-Intent Capture)

Campaign Types Priority:

Search - Brand (highest priority, protect brand terms)
Search - Competitor (steal market share)
Search - Solution (problem-aware buyers)
Search - Product Category (earlier stage)
Display - Retargeting (re-engage warm traffic)

Search Campaign Structure:

Campaign: [Search-Solution-Keywords]
├─ Ad Group: [project management software]
│  ├─ Keywords:
│  │  - "project management software" [Phrase]
│  │  - "best project management tool" [Phrase]
│  │  - +project +management +solution [Broad Match Modifier]
│  └─ Ads: [3 responsive search ads with 15 headlines, 4 descriptions]
│
├─ Ad Group: [team collaboration tools]
   ├─ Keywords: [5-10 tightly themed keywords]
   └─ Ads: [3 responsive search ads]

Keyword Strategy:

Brand Terms: Exact match, bid high, protect brand
Competitor Terms: "[Competitor] alternative", "[Competitor] vs [You]"
Solution Terms: "best [category] software", "top [category] tools"
Problem Terms: "how to [solve problem]"
Negative Keywords: Maintain list of 100+ (free, cheap, jobs, career, reviews)

Bid Strategy (2025 best practice):

New campaigns: Start Manual CPC for control
After 50+ conversions: Switch to Target CPA
After 100+ conversions: Test Maximize Conversions with tCPA
EU markets: Bid 15-20% higher for same quality

Ad Copy Framework (Responsive Search Ads):

Headlines (15 required):
- H1-3: Value props (Save 10 hours/week, Trusted by 500+ teams)
- H4-6: Features (AI-powered, Real-time sync, Mobile app)
- H7-9: Social proof (4.8★ G2 rating, Used by Microsoft)
- H10-12: CTAs (Start free trial, Book demo, See pricing)
- H13-15: Keywords pinned (Dynamic insertion)

Descriptions (4 required):
- D1: Primary value prop + CTA (30-60 chars)
- D2: Feature list + differentiator (60-90 chars)
- D3: Social proof + urgency (45-90 chars)
- D4: Backup generic (60-90 chars)

2.4 Meta Ads Playbook (SMB/Lower ACV)

When to Use Meta:

✅ Product ACV <$10k
✅ Visual product (UI, consumer-facing)
✅ SMB/prosumer audience
✅ Broader awareness campaigns
❌ Enterprise/high ACV (use LinkedIn)

Campaign Setup:

Campaign Objective: [Conversions]
├─ Ad Set 1: [Lookalike - 1% of converters]
│  └─ Placement: [Feed + Stories, Auto]
├─ Ad Set 2: [Interest - Business Software]
│  └─ Placement: [Feed only]
└─ Ad Set 3: [Retargeting - Website 30d]
   └─ Placement: [All placements]

Audience Strategy:

Core Audiences: Interests (business tools, productivity, startups)
Lookalike: 1% of purchasers/high-value leads
Retargeting: 30-day website visitors, video viewers (75%+)

Creative Best Practices:

Use video (1:1 or 9:16 for Stories)
First 3 seconds = hook (problem or result)
Show product UI in action
Add captions (85% watch muted)
Test 3-5 creative variants per campaign

2.5 Budget Allocation & Scaling

Initial Budget (Series A, $30k-50k/month total):

Channel            Budget    Expected Results
─────────────────────────────────────────────
LinkedIn Ads       $15k      50 MQLs, 10 SQLs, $1.5k CAC
Google Search      $12k      80 MQLs, 20 SQLs, $600 CAC
Google Display     $5k       120 MQLs, 5 SQLs, $1k CAC
Meta Ads           $5k       100 MQLs, 8 SQLs, $625 CAC
Partnerships       $3k       20 MQLs, 5 SQLs, $600 CAC
─────────────────────────────────────────────
TOTAL              $40k      370 MQLs, 48 SQLs, $833 avg CAC

Scaling Rules:

If CAC <target → Increase budget 20% weekly
If CAC >target → Pause, optimize, relaunch
If conversion rate drops >20% → Check landing page, offer fatigue
Scale winners, kill losers fast (2-week test minimum)

HubSpot ROI Dashboard:

Marketing → Reports → Create Custom Report
Metrics: Spend, Leads, MQLs, SQLs, CAC, ROAS, Pipeline $
Dimensions: Campaign, Channel, Region
Frequency: Daily review, weekly optimization

3. SEO Strategy

3.1 Technical SEO Foundation (Must-Have)

Pre-Launch Checklist:

XML sitemap submitted to Search Console
Robots.txt configured (allow crawling)
HTTPS enabled (SSL certificate)
Page speed >90 mobile (Google PageSpeed Insights)
Core Web Vitals passing (LCP, FID, CLS)
Structured data (Organization, Product, FAQ schema)
Canonical tags on all pages
Hreflang tags for international (en-US, en-GB, de-DE, etc.)

Technical Audit (quarterly):

1. Crawl site with Screaming Frog
2. Check for:
   - 404 errors (fix or redirect)
   - Redirect chains (consolidate)
   - Duplicate content (canonicalize)
   - Missing meta descriptions
   - Slow pages (>3s load time)
   - Mobile usability issues
3. Fix issues in priority order: Critical → High → Medium

3.2 Keyword Strategy Framework

Keyword Research Process:

Seed Keywords - Your product category (e.g., "project management software")
Use Tools - Ahrefs, SEMrush, or free: Google Keyword Planner + Search Console
Analyze - Volume, difficulty, intent, SERP features
Prioritize - Quick wins (low difficulty, high intent)

Keyword Tiers:

Tier 1: High-Intent BOFU (target first)

"best [product category]"
"[product category] for [use case]"
"[competitor] alternative"
Volume: 100-1k/mo, Difficulty: Medium, Intent: Commercial

Tier 2: Solution-Aware MOFU

"how to [solve problem]"
"[problem] solution"
"[use case] tools"
Volume: 500-5k/mo, Difficulty: Medium-High, Intent: Informational-Commercial

Tier 3: Problem-Aware TOFU

"what is [concept]"
"[problem] examples"
"[industry] challenges"
Volume: 1k-10k/mo, Difficulty: High, Intent: Informational

International Keyword Research:

Use Ahrefs/SEMrush with language filters
Translate keywords, don't just localize (cultural nuances matter)
EU: Higher trust in localized content (domain.de > domain.com/de)
UK: Use British spelling (optimise vs. optimize)

3.3 On-Page SEO Template

Page Optimization Checklist:

URL: [/best-project-management-software]
Title Tag (60 chars): [Best Project Management Software 2025 | YourBrand]
Meta Description (155 chars): [Compare top 10 PM tools. Features, pricing, reviews. Find the perfect fit for your team. Free trials available.]

H1 (60 chars): [Best Project Management Software in 2025]
H2s (structure):
  - What is Project Management Software?
  - Top 10 PM Tools Compared
  - Key Features to Look For
  - Pricing & Plans
  - How to Choose
  - FAQ

Content:
  - Length: 2000-3000 words (comprehensive)
  - Keyword density: 1-2% (natural)
  - Internal links: 3-5 relevant pages
  - External links: 2-3 authoritative sources
  - Images: 3-5 with alt text
  - Schema: Product, FAQ, HowTo

CTA:
  - Above fold: [Start Free Trial]
  - Mid-content: [Compare Plans]
  - End: [Book Demo]

Content Refresh Schedule:

Tier 1 pages: Update quarterly (rankings, pricing, features)
Tier 2 pages: Update semi-annually
Tier 3 pages: Update annually
All pages: Monitor Search Console for ranking drops, refresh immediately

3.4 Link Building Strategy (2025 Best Practices)

Link Acquisition Tactics (in priority order):

1. Digital PR (highest ROI)

Publish original research/data
Create industry reports
Pitch journalists (use HARO, Terkel, Featured)
Target: Industry blogs, tech publications

2. Guest Posting (quality over quantity)

Target: Domain Authority (DA) 40+ sites
Avoid: Link farms, PBNs, paid links (Google penalty risk)
Anchor text: Branded (70%), topical (20%), exact match (10%)

3. Partnerships & Co-Marketing

Partner with complementary SaaS tools
Create co-branded content
Exchange homepage links (footer or partner section)

4. Community Engagement

Answer questions on Reddit, Quora
Participate in industry forums
Create tools/calculators → natural backlinks

5. Broken Link Building

Find broken links on competitor sites
Offer your content as replacement
Tools: Ahrefs' Broken Backlinks report

Link Velocity (avoid penalties):

Natural: 5-10 links/month for new sites
Aggressive: 20-30 links/month after 6 months
Monitor: Google Search Console for manual actions

3.5 Content Strategy for SEO

Content Types by Funnel Stage:

TOFU (Awareness):

Blog posts: "Ultimate Guide to [Topic]"
Listicles: "Top 10 [Category]"
Industry reports: "[Industry] State of 2025"
Target: Broad keywords, thought leadership

MOFU (Consideration):

Comparison pages: "[Your Product] vs [Competitor]"
Best of lists: "Best [Category] for [Use Case]"
How-to guides: "How to [Solve Problem] with [Product]"
Target: Solution keywords, product education

BOFU (Decision):

Product pages: "[Product] Features & Pricing"
Case studies: "How [Customer] Achieved [Result]"
Landing pages: "Start Free Trial"
Target: Brand keywords, high-intent searches

Content Calendar (Series A minimum):

TOFU: 4 posts/month (1 per week)
MOFU: 2 posts/month
BOFU: 1 post/month
Refresh: 2 existing posts/month

3.6 Local SEO (For Regional Offices)

Google Business Profile Setup (per location):

Complete all fields: Name, address, phone, hours, category
Upload photos: Office, team, product (10+ images)
Collect reviews: Ask customers, automate via HubSpot workflow
Post updates: Weekly posts about company news, events

Local Citations (US/Canada/EU):

Submit to: Yelp, Yellow Pages, local directories
NAP consistency: Name, Address, Phone identical everywhere
Industry directories: Software review sites (G2, Capterra)

4. Partnerships & Affiliate Programs

4.1 Partnership Types & Strategy

Partnership Tiers:

Tier 1: Strategic Partnerships (high impact, low volume)

Target: Complementary SaaS tools with overlapping ICPs
Structure: Co-marketing, product integrations, revenue share
Examples: Slack ↔ Asana, Shopify ↔ Klaviyo
Effort: High (6-12 months to establish)
ROI: Very high (100+ leads/month after ramp)

Tier 2: Affiliate Partners (scalable)

Target: Bloggers, review sites, industry influencers
Structure: Commission per sale (10-30% first year)
Platform: Use PartnerStack, Impact, or Rewardful
Effort: Medium (setup once, ongoing management)
ROI: Medium-High (depends on partner quality)

Tier 3: Referral Partners (customer-driven)

Target: Your existing customers
Structure: Referral bonus ($500-$1k per SQL)
Platform: Built into HubSpot or standalone (Friendbuy)
Effort: Low (automate via workflows)
ROI: Medium (5-10% of customers refer)

Tier 4: Marketplace Listings (distribution)

Target: Shopify App Store, Salesforce AppExchange, HubSpot Marketplace
Structure: Free listing + revenue share
Effort: Medium (initial listing, ongoing updates)
ROI: Low-Medium (brand visibility + discovery)

4.2 Partnership Playbook

Step 1: Identify Partners

Criteria:
- Similar ICP (overlapping audience, no direct competition)
- Product fit (complementary, not substitute)
- Scale (similar company size, funding stage)
- Values alignment (culture, brand positioning)

Research:
- Tools: BuiltWith, SimilarWeb, LinkedIn Sales Nav
- Look for: Integration pages, partner pages, co-marketing history

Step 2: Outreach Template

Subject: [YourBrand] ↔ [TheirBrand] Partnership Idea

Hi [Name],

I'm [Your Name] at [YourBrand] - we help [ICP] with [value prop].

I noticed [TheirBrand] serves a similar audience, and I think our customers would benefit from an integration between [YourProduct] and [TheirProduct].

Would you be open to exploring a partnership? I'm thinking:
- Product integration (bi-directional sync)
- Co-marketing (joint webinar, case study)
- Revenue share (referral fees)

Let me know if you'd like to chat. Happy to send more details.

Best,
[Your Name]

Step 3: Partnership Agreement

Define scope (integration depth, marketing commitment)
Revenue model (rev share %, referral fees, co-selling)
Success metrics (leads, pipeline, revenue)
Term (12-24 months, with renewal)
Exit clause (90-day notice)

Step 4: Activation & Enablement

Create co-branded assets (landing page, webinar deck, one-pager)
Train partner sales team (product demo, pitch deck, objection handling)
Set up tracking (UTM parameters, partner portal in HubSpot)

Step 5: Ongoing Management

Quarterly business reviews (QBRs)
Monthly check-ins (pipeline, blockers)
Co-marketing calendar (1-2 activities/quarter)
Reporting (HubSpot dashboard for partner-sourced pipeline)

4.3 Affiliate Program Setup

Platform Selection:

PartnerStack - Best for B2B SaaS, native integrations
Impact - Enterprise-grade, high control
Rewardful - Lightweight, Stripe integration
FirstPromoter - Budget-friendly, good analytics

Commission Structure (Series A typical):

Tier 1: Influencers/Publishers
- 30% recurring for 12 months
- Or: $500 flat per SQL
- Bonus: $1k for 10+ referrals/quarter

Tier 2: Bloggers/Content Creators
- 20% recurring for 12 months
- Or: $300 flat per SQL

Tier 3: Customers (Referral Program)
- $500 per closed deal
- Or: 1 month free for both referrer + referee

Recruitment Strategy:

Outbound: Find industry bloggers, YouTubers, newsletter writers
Inbound: "Become an Affiliate" page, promote in product
Events: Recruit at conferences, meetups
Communities: Reddit, LinkedIn groups, Slack communities

Affiliate Enablement Kit:

Brand assets (logos, product screenshots)
Pre-written content (blog post templates, social posts)
Tracking links (unique UTM codes per affiliate)
Sales collateral (one-pagers, case studies, demo videos)

4.4 Co-Marketing Campaigns

Joint Webinar Playbook:

Planning (6 weeks out):
- Define topic (audience pain point, not product pitch)
- Assign roles (host, co-host, Q&A moderator)
- Create landing page (co-branded, dual logos)
- Design promo assets (social graphics, email templates)

Promotion (4 weeks out):
- Email: 3 sends (announcement, reminder, last chance)
- Social: 8-10 posts per partner (LinkedIn, Twitter)
- Paid: $2k budget for LinkedIn ads → landing page
- Partners: Cross-promote to each other's audiences

Execution (day of):
- 60-min format: 5min intro, 40min content, 15min Q&A
- Record for on-demand
- Polls/CTAs: Mid-webinar poll, end with demo CTA

Follow-up (1 week after):
- Send recording to all registrants
- Nurture sequence: 3 emails over 2 weeks
- Split leads: Each partner owns their referred leads
- Report: Attendees, pipeline generated, next steps

Other Co-Marketing Tactics:

Co-branded Content: eBook, report, guide
Case Study: Joint customer success story
Bundle Offer: "Buy [YourProduct] + [TheirProduct], save 20%"
Cross-promotion: Feature each other in newsletters
Social Media Takeover: Guest post on each other's channels

4.5 HubSpot Partner Tracking

Setup:

Create Partner Property
- Settings → Properties → Create "Partner Source" dropdown
- Values: Partner A, Partner B, Affiliate Network, etc.
UTM Tracking
- Partner links: ?utm_source=partner-name&utm_medium=referral
- HubSpot auto-captures UTM parameters
Lead Assignment
- Workflow: If "Partner Source" is set → Assign to Partner Manager
- Notification: Slack alert when partner lead arrives
Reporting
- Dashboard: Partner-sourced leads, pipeline, revenue
- Report to partners: Monthly performance summary

5. Attribution & Reporting

5.1 Attribution Models (HubSpot Native)

Model Selection (use multi-touch for hybrid motion):

First-Touch - Credit to first interaction

Use case: Awareness campaigns, brand building
Pro: Shows what drives discovery
Con: Ignores nurturing influence

Last-Touch - Credit to last interaction before conversion

Use case: Direct response, BOFU campaigns
Pro: Shows what closes deals
Con: Ignores earlier touchpoints

Multi-Touch (W-Shaped) - Credit to first, last, and middle (40-20-40 split)

Use case: Hybrid PLG/Sales-Led (recommended for Series A)
Pro: Full-funnel view
Con: More complex to explain to stakeholders

HubSpot Setup:

Marketing → Reports → Attribution → Select Model
Default: Use Multi-Touch for holistic view
Compare: Run reports side-by-side to see differences

5.2 Reporting Dashboard (HubSpot)

Weekly Performance Dashboard:

Metrics to Track:
1. Traffic: Visits, unique visitors, bounce rate
2. Leads: MQLs, SQLs, conversion rates
3. Pipeline: Opportunities created, value, velocity
4. CAC: Spend ÷ customers acquired
5. Channel Mix: % of leads by source

Dimensions:
- By Channel: Organic, Paid, Email, Social, Referral
- By Campaign: Individual campaign performance
- By Region: US, CA, EU breakdown
- By Stage: TOFU, MOFU, BOFU metrics

Monthly Executive Dashboard:

KPIs:
1. Marketing-Sourced Pipeline: $[X]M (target: $[Y]M)
2. Marketing-Sourced Revenue: $[X]k (target: $[Y]k)
3. Blended CAC: $[X] (target: $[Y])
4. MQL→SQL Rate: [X]% (target: [Y]%)
5. Pipeline Velocity: [X] days (target: [Y] days)
6. ROMI: [X]:1 (target: 3:1+)

Insights:
- Top performing campaigns
- Underperforming channels (kill or optimize)
- New experiments to test next month
- Budget reallocation recommendations

5.3 Google Analytics Setup

Events to Track (GA4):

Engagement:
- page_view (auto-tracked)
- scroll (75% depth)
- video_play (product demos)
- file_download (whitepapers, eBooks)

Conversions:
- sign_up (free trial, account created)
- demo_request (calendar booking)
- contact_form (inbound interest)
- pricing_view (pricing page visit)

E-commerce (if applicable):
- add_to_cart
- begin_checkout
- purchase

Custom Dimensions:

User Type: Free vs. Paid
Plan Type: Starter, Pro, Enterprise
HubSpot Lead Status: MQL, SQL, Customer
Campaign: HubSpot Campaign ID

Integration with HubSpot:

Use HubSpot tracking code (includes GA4 by default)
Or: Google Tag Manager for advanced tracking
Sync: GA4 audiences → HubSpot lists for retargeting

6. Experimentation Framework

6.1 A/B Testing Prioritization (ICE Score)

Formula: ICE = (Impact × Confidence × Ease) ÷ 3

Rate each factor 1-10:

Impact: How much will this move the needle?
Confidence: How sure are you it will work?
Ease: How easy is it to implement?

Example Tests (sorted by ICE score):

Test	Impact	Confidence	Ease	ICE	Priority
CTA button color (red vs. green)	3	8	10	7.0	Low
Landing page headline rewrite	8	7	8	7.7	Medium
Pricing page redesign	9	6	4	6.3	Medium
New lead magnet offer	9	8	7	8.0	High
Add live chat to pricing page	7	9	8	8.0	High

6.2 Test Design & Execution

Test Template:

Hypothesis: [Adding a case study carousel to the pricing page will increase demo requests by 20% because users need social proof before committing]

Metric: [Demo requests from /pricing page]
Sample Size: [1000 visitors per variant]
Duration: [2 weeks or until significance]
Success Criteria: [20% lift, 95% confidence]

Variant A (Control): [Current pricing page]
Variant B (Treatment): [Pricing page + case study carousel]

Tools: [HubSpot A/B test, or Google Optimize]

Statistical Significance:

Minimum: 95% confidence, 1000 visitors/variant
Use calculator: Optimizely Sample Size Calculator
Don't stop tests early (false positives)

Test Velocity (Series A target):

4-6 tests/month across channels
70% win rate not realistic (aim for 30-40%)
Document losers (learnings matter)

6.3 Common Experiments

Landing Page Tests:

Headline variations (problem-focused vs. solution-focused)
CTA copy ("Start Free Trial" vs. "Get Started" vs. "Try Now")
Form length (5 fields vs. 2 fields)
Social proof placement (above vs. below fold)
Hero image (product screenshot vs. people vs. abstract)

Ad Tests:

Creative format (static vs. video vs. carousel)
Messaging angle (feature-led vs. benefit-led vs. outcome-led)
Audience targeting (broad vs. narrow)
Landing page destination (homepage vs. dedicated LP)

Email Tests:

Subject line length (short vs. long)
Personalization (generic vs. first name vs. company name)
Send time (morning vs. afternoon vs. evening)
CTA placement (top vs. middle vs. bottom)

7. Handoff Protocols

7.1 MQL → SQL Handoff (Marketing → Sales)

SQL Definition Criteria (customize for your ICP):

Required:
✅ Job title: Director+ (or Budget Authority confirmed)
✅ Company size: 50-5000 employees
✅ Budget: $10k+ annual (or Qualified Need confirmed)
✅ Timeline: Buying within 90 days
✅ Engagement: Demo requested OR High intent action

Optional:
✅ Industry: Target verticals
✅ Geography: US/CA/EU
✅ Use case: Matches product capabilities

HubSpot Workflow:

Lead reaches MQL threshold (lead score >75)
Trigger: Automated email to SDR
SDR qualification call (BANT: Budget, Authority, Need, Timeline)
If qualified → Mark as SQL, assign to AE
If not qualified → Recycle to nurture, adjust lead score

SLA (Service Level Agreement):

SDR responds to MQL: 4 hours
AE books demo with SQL: 24 hours
First demo: Within 3 business days of SQL status

7.2 SQL → Opportunity Handoff (Sales → RevOps)

Opportunity Creation:

AE creates opportunity in HubSpot after first demo
Required fields: Company, Deal value, Close date, Stage
Pipeline stages: Discovery → Demo → Proposal → Negotiation → Closed Won/Lost

Marketing Support Post-SQL:

Retargeting ads to target accounts (ABM)
Send case studies, ROI calculator
Invite to customer webinar
Executive briefing (for Enterprise deals)

7.3 Lost Opportunity Handoff (Sales → Marketing)

Recycle to Nurture:

Reason: No budget, bad timing, wrong fit
Action: Move to "Nurture" list in HubSpot
Sequence: Quarterly check-in emails, invite to webinars
Re-engage: After 6-12 months, SDR re-qualification

Closed Lost Reasons (track in HubSpot):

Price too high
Missing features
Chose competitor
No budget
Bad timing
Champion left company

Use lost reasons to inform:

Product roadmap
Pricing changes
Competitive positioning
Messaging adjustments

8. Quick Reference

8.1 Channel-Specific Benchmarks (B2B SaaS Series A)

Metric	LinkedIn	Google Search	SEO	Email	Partnerships
CTR	0.4-0.9%	2-5%	1-3%	15-25%	N/A
CVR	1-3%	3-7%	2-5%	2-5%	5-10%
CAC	$150-400	$80-250	$50-150	$20-80	$100-300
MQL→SQL	10-20%	15-25%	12-22%	8-15%	20-35%

8.2 Budget Allocation (Recommended)

Series A ($40k-60k/month):

40% Paid Acquisition (LinkedIn + Google)
25% Content/SEO
20% Partnerships
10% Tools/Automation
5% Experiments/Testing

8.3 Team Handoff Quick Guide

Demand Gen → Sales:

Deliver: SQLs with BANT qualification
Frequency: Real-time via HubSpot
SLA: 4-hour response time

Demand Gen → Product Marketing:

Request: Product positioning, competitive intel, case studies
Frequency: Monthly sync
Deliverables: Updated messaging, new collateral

Demand Gen → Marketing Ops:

Request: Campaign tracking setup, attribution reports, data cleaning
Frequency: Weekly check-in
SLA: 48-hour turnaround for new campaigns

Paid Media → Creative/Brand:

Request: Ad creative (10-20 variants/month)
Format: Specs sheet with dimensions, copy length, brand guidelines
SLA: 5 business days per request

SEO → Content:

Request: Content based on keyword research
Deliverables: Content brief with target keywords, structure, length
Frequency: Monthly editorial calendar

Partnerships → Sales:

Deliver: Partner-sourced leads with partner context
Co-selling: Joint calls for strategic deals
Frequency: Weekly partner pipeline review

Resources

references/

hubspot-workflows.md - Pre-built HubSpot workflow templates for lead scoring, nurture, assignment
campaign-templates.md - Ready-to-use campaign briefs for LinkedIn, Google, SEO
international-playbooks.md - Market-specific tactics for EU, US, Canada expansion
attribution-guide.md - Deep dive on multi-touch attribution setup and analysis

scripts/

calculate_cac.py - Calculate blended and channel-specific CAC
experiment_calculator.py - A/B test sample size and significance calculator

assets/

campaign-brief-template.docx - Editable campaign planning document
dashboard-template.xlsx - Pre-configured performance dashboard

Last Updated: October 2025 | Version: 1.0

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Use this skill

Marketing Demand & Acquisition

Keywords

Role Coverage

Core KPIs by Role

Tech Stack Integration

1. Demand Generation Framework

1.1 Full-Funnel Strategy (2025 Best Practice)

TOFU Tactics

MOFU Tactics

BOFU Tactics

1.2 Campaign Planning Template

1.3 HubSpot Campaign Tracking Setup

1.4 International Expansion Considerations

2. Paid Media Optimization

2.1 Channel Strategy Matrix

2.2 LinkedIn Ads Playbook (Primary B2B Channel)

2.3 Google Ads Playbook (High-Intent Capture)

2.4 Meta Ads Playbook (SMB/Lower ACV)

2.5 Budget Allocation & Scaling

3. SEO Strategy

3.1 Technical SEO Foundation (Must-Have)

3.2 Keyword Strategy Framework

3.3 On-Page SEO Template

3.4 Link Building Strategy (2025 Best Practices)

3.5 Content Strategy for SEO

3.6 Local SEO (For Regional Offices)

4. Partnerships & Affiliate Programs

4.1 Partnership Types & Strategy

4.2 Partnership Playbook

4.3 Affiliate Program Setup

4.4 Co-Marketing Campaigns

4.5 HubSpot Partner Tracking

5. Attribution & Reporting

5.1 Attribution Models (HubSpot Native)

5.2 Reporting Dashboard (HubSpot)

5.3 Google Analytics Setup

6. Experimentation Framework

6.1 A/B Testing Prioritization (ICE Score)

6.2 Test Design & Execution

6.3 Common Experiments

7. Handoff Protocols

7.1 MQL → SQL Handoff (Marketing → Sales)

7.2 SQL → Opportunity Handoff (Sales → RevOps)

7.3 Lost Opportunity Handoff (Sales → Marketing)

8. Quick Reference

8.1 Channel-Specific Benchmarks (B2B SaaS Series A)

8.2 Budget Allocation (Recommended)

8.3 Team Handoff Quick Guide

Resources

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