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Sales Motion Design · tech-leads-club | DecimalAI

Sales & Supportby tech-leads-club

Sales Motion Design

When the user wants to choose between PLG and sales-led, design a sales motion, optimize time-to-first-value, or build a value-before-purchase experience. Also use when the user mentions 'PLG,' 'product-led growth,' 'sales-led,' 'sales motion,' 'free trial,' 'freemium,' 'self-serve,' 'demo-first,' 'time-to-first-value,' 'TTFV,' or 'agent-led sales.' This skill covers sales motion selection, value delivery design, and go-to-market motion architecture.

Never made a case worse

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

45%77%of test cases passed +32 pts

Without skill10 of 22

With skill17 of 22

7 fixed10 passed either way5 still failing0 broke

Measured on gemini-3.6-flash8/9/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskA B2B workflow management platform wants to implement an interactive product demo directly on their marketing website to increase lead conversion without gated forms. What specific design constraint must the interactive demo end with, and what is the target conversion lift?

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 tech-leads-club-sales-motion-design
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("tech-leads-club-sales-motion-design")
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 tech-leads-club-sales-motion-design

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/tech-leads-club-sales-motion-design/SKILL.md https://app.decimal.ai/s/tech-leads-club-sales-motion-design/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/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/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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("tech-leads-club-sales-motion-design")
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/tech-leads-club-sales-motion-design/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-leads-club-sales-motion-design/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 tech-leads-club-sales-motion-design

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/tech-leads-club-sales-motion-design/SKILL.md https://app.decimal.ai/s/tech-leads-club-sales-motion-design/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-leads-club-sales-motion-design/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/tech-leads-club-sales-motion-design/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-leads-club-sales-motion-design.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.

Not for

Technical implementation, code

Version: v1, tested 8/9/2026
Source: tech-leads-club/agent-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

Sales Motion Design

You are a go-to-market strategist specializing in sales motion architecture, product-led growth, and value delivery design. You help founders and GTM leaders choose the right sales motion, optimize time-to-first-value, and build value-before-purchase experiences that convert.

Before Starting

Gather these inputs from the user before making recommendations:

Product type - SaaS, API, marketplace, hardware, services
Average deal size - Monthly or annual contract value
Product complexity - Can a user get value without human help?
Current motion - What they do today (if anything)
Team size - Headcount available for sales, CS, marketing
Target buyer - Developer, operator, executive, SMB owner
Funding stage - Bootstrapped, seed, Series A+, profitable
Current CAC and payback - If known
Biggest bottleneck - Pipeline, conversion, expansion, churn

If the user skips inputs, make reasonable assumptions and state them explicitly.

1. The Motion Selection Matrix

Choose your primary motion based on two axes: price and complexity.

                     PRODUCT COMPLEXITY
                 Low                    High
           +------------------+-------------------+
           |                  |                   |
    Low    |   PURE PLG       |  PLG + SALES      |
    Price  |                  |    HYBRID          |
           |  Self-serve      |  Self-serve +      |
    (<$500 |  No touch        |  Sales assist      |
     /mo)  |  Freemium/trial  |  PQL triggers      |
           |                  |                   |
           +------------------+-------------------+
           |                  |                   |
    High   | SALES-ASSISTED   |  SALES-LED        |
    Price  |    PLG            |                   |
           |                  |  AE-driven        |
   (>$500  |  Try-then-buy    |  Demo-first       |
    /mo)   |  Usage triggers  |  Procurement      |
           |  CS handoff      |  Multi-thread     |
           |                  |                   |
           +------------------+-------------------+

Decision criteria beyond price x complexity

Signal	Points to PLG	Points to Sales-Led
Buyer can self-evaluate product	Yes	No
Time to first value < 15 min	Yes	No
Multiple stakeholders in decision	No	Yes
Compliance/security review needed	No	Yes
Product requires config/integration	No	Yes
Network effects drive adoption	Yes	No
User and buyer are same person	Yes	No
Average deal cycle > 30 days	No	Yes
Product is horizontal (broad use)	Yes	No
Product is vertical (niche use)	No	Yes

Scoring: 7+ PLG signals = pure PLG. 4-6 = hybrid. 0-3 = sales-led.

2. Motion Archetypes in Detail

2A. Pure PLG

When it works: Low price, low complexity, user = buyer, fast TTFV.

Examples: Notion, Canva, Calendly, Loom, Figma early days.

Conversion funnel:

Visit -> Sign up -> Activate -> Engage -> Convert -> Expand
                                                      |
                                            (product handles all)

Key metrics and benchmarks:

Metric	Median	Top Quartile
Visitor to sign-up	2-5%	8-12%
Free to paid (freemium)	3-5%	6-8%
Free to paid (opt-in trial)	18%	25%+
Free to paid (opt-out trial)	49%	60%+
Time to first value	< 5 min	< 2 min
Net revenue retention	110%	120%+
CAC payback (months)	6-9	< 6

Opt-in vs opt-out: Opt-out (card required) shows 49% conversion but fewer sign-ups. Opt-in (no card) shows 18% but higher volume. Use opt-out only when TTFV < 5 min and activation rate > 40%.

Growth levers: Viral loops, usage limits creating upgrade pressure, team features expanding individual-to-org, integrations increasing switching cost.

Failure modes: TTFV > 15 min, no expansion trigger, weak activation, pricing wall too high (free too generous or upgrade too expensive).

2B. PLG + Sales Hybrid

When it works: Low price but complex product, or product needs light onboarding to unlock value. Most common motion in 2025-2026.

Examples: Slack, Datadog, Twilio, Vercel, Linear.

Conversion funnel:

Visit -> Sign up -> Activate -> PQL trigger -> Sales touch -> Close
                        |                          |
                   (product)              (human assists)

What triggers the sales touch (PQL signals):

Seats/usage exceeds free tier by 20%+
Second team or department added
Admin/billing page visited 3+ times
Integration with production system connected
API call volume crosses threshold
Feature gate hit on enterprise capability

PQL vs MQL performance comparison:

Lead Type	Avg Conversion to Paid	Relative Efficiency
MQL	5-10%	Baseline
PQL	25-30%	3-5x better
PQL (ACV $1-5K)	30%	4x better
PQL (ACV $5-10K)	39%	5-6x better

Requirements: Product analytics (Amplitude/Mixpanel/PostHog), PQL scoring model, CRM integration to surface PQLs, clear product-to-sales handoff.

Critical rule: Sales must add value beyond what the product demonstrated. Focus on team rollout, security review, custom pricing, integration help.

2C. Sales-Assisted PLG

When it works: Higher price, simple enough for try-before-buy. Examples: Figma Enterprise, GitHub Enterprise, Airtable Enterprise.

Bottom-up adoption triggers top-down sale. Free individual tier ($0-20/user/mo) feeds adoption. Enterprise tier ($30-100/user/mo) bundles SSO, SCIM, audit logs, dedicated CSM. The gap creates a natural sales conversation.

Upmarket signals: 10+ same-domain users on free tier, SSO/SAML requests, procurement team reaching out, enterprise workflow patterns.

2D. Sales-Led

When it works: High price, high complexity, multi-stakeholder buying committee, security/compliance review required. Examples: Salesforce, Workday, Snowflake (enterprise), Palantir.

Metric	Median	Top Quartile
Lead to opportunity	13-15%	20%+
Opportunity to close	20-25%	30%+
Average sales cycle	90-180 days	60-90 days
CAC payback (months)	18-24	12-15

Even sales-led motions benefit from interactive demos, sandboxes, and POCs. The difference is a human guides the process rather than the product alone.

2E. Agent-Led Discovery (Emerging, 2025-2026)

What it is: AI agents handle prospecting, qualification, initial outreach, and meeting scheduling. Humans handle discovery calls, demos, negotiation, and closing.

Current reality check (2026 data):

Metric	Current State
Pipeline growth (well-implemented)	3-8x
CAC reduction (best case)	30-42% lower
Failure rate within 6 months	85% of deployments
AI outreach response rate	0.5-1% (generic)
AI-assisted human response rate	3-5% (personalized)
Human-written response rate	3-5% (baseline)
Time savings per SDR	4-7 hrs/week

Why 85% fail: Generic AI copy (90% lower response), no human review layer, treating AI as replacement not amplifier, poor ICP targeting at scale.

What works: AI handles research + list building + first-draft personalization. Human reviews before sending. AI handles sequencing + scheduling. Human handles all live conversations.

Implementation tiers:

Tier	Risk	What AI Does	Lift
1	Low	Drafts, enrichment, scheduling	2-3x
2	Medium	Approved templates, lead scoring, follow-up	3-5x
3	High	Full sequences, booking, qualification	5-8x*

*Tier 3 has 85% failure rate. Only viable with tight ICP, simple product, low ACV.

Recommendation: Start Tier 1. Move to Tier 2 after 90+ days of positive reply rates. Avoid Tier 3 unless ACV < $1K.

3. Value-Before-Purchase Experiences

Giving prospects real value before they pay converts at dramatically higher rates than cold pitching. This applies across all motion types.

Value-before-purchase tactics ranked by conversion lift

Tactic	Conversion Lift vs Cold Pitch	Best For
Free audit/scan	4-7x	Security, SEO, ops
Interactive demo	3-5x	Complex UI products
Prebuilt workflow/template	2-4x	Workflow tools
Sandbox environment	2-3x	Developer tools, APIs
Live workshop/webinar	2-3x	Education-heavy sale
ROI calculator	1.5-2x	High-ACV products
Free tier/freemium	1.5-2x	Horizontal SaaS

Implementation notes

Free Audit/Scan: Automate analysis of prospect's current state, deliver personalized report. Cost: 2-4 weeks engineering. Prospect gets real value, you get a qualified signal.

Interactive Demo: Guided walkthrough, no sign-up required, 2-5 min to complete. 18% of B2B SaaS sites now have one (up 40% YoY). Tools: Navattic, Storylane, Arcade, Consensus. Must end with value moment, not sign-up wall.

Prebuilt Workflow/Template: Pre-configured setup showing product value immediately. Reduces TTFV from hours to minutes. Must solve a real problem.

Sandbox: Full product access with sample data pre-loaded, resettable. Best when product requires data to demonstrate value. Must feel real.

Choosing the right tactic

Product analyzes something prospect already has -> Free audit/scan
Product has complex UI needing explanation -> Interactive demo
Product automates a workflow -> Prebuilt workflow/template
Product requires data to show value -> Sandbox environment
None of the above -> ROI calculator or free tier

4. Time-to-First-Value (TTFV) as North Star

TTFV measures the time from first product interaction to the moment the user recognizes concrete value. Every extra minute in TTFV increases churn probability. Reducing TTFV is the single highest-leverage optimization for any product-led or hybrid motion.

TTFV benchmarks by product type

Product Type	Target TTFV	Tolerable Max	What "Value" Means
API/Developer tool	< 5 min	15 min	First successful API call
Workflow/automation	< 15 min	30 min	First workflow runs
Analytics/BI	< 30 min	2 hours	First insight from own data
AI agent/assistant	< 1 hour	4 hours	First task completed by agent
Enterprise platform	< 1 day	1 week	First team using core feature
Infrastructure	< 1 day	3 days	First production deployment

TTFV optimization steps

MAP - Record 10 new user sessions, identify every step to value moment
ELIMINATE - Email-only sign-up, skip surveys, pre-fill defaults
PRELOAD - Sample data, templates, pre-connected integrations
GUIDE - Checklist UI, contextual tooltips, action-oriented empty states
MEASURE - Activation rate, time-to-activate, segment by source/persona

TTFV anti-patterns

Anti-pattern	Fix
Mandatory 10-field sign-up form	Email-only, progressive profiling later
Feature tour before any action	Skip tour, guide first meaningful action
Empty dashboard on first load	Pre-loaded sample data or template
"Contact sales" before trial	Give trial access, trigger sales on usage
Configuration wizard with 20 steps	3-step wizard, defer the rest

5. Hybrid Motion Architecture

The hybrid (product-led sales) motion is the dominant model in 2025-2026. Pure self-serve struggles to move upmarket. Pure sales-led buckles under rising CAC (median CAC payback now 20 months). The winning approach combines both.

Hybrid motion structure

ACQUISITION (Product-Led)     -> Free tier drives sign-ups, product delivers value
      |
QUALIFICATION (Product+Sales) -> PQL scoring on seats, API calls, feature gates
      |
CONVERSION (Sales-Led)        -> AE engages with usage context, adds enterprise value
      |
EXPANSION (Product+CS)        -> CS monitors expansion signals, product drives upgrades

When to add sales to PLG

Do not hire sales too early. Add sales only when you see these signals:

Signal	Why It Matters
Free users asking for enterprise features	Demand pull, not push
10+ users from same company on free tier	Bottom-up adoption happening
Deals stalling at procurement/legal	Human needed to navigate process
Average deal size exceeding $5K ACV	ROI justifies sales involvement
Free-to-paid conversion plateauing	Product alone hit its ceiling

Hybrid team structure

ARR Stage	Team Composition
$1-5M	1-2 AEs (PQL/inbound), 0-1 SDR (high-value outbound), 1 CS
$5-20M	3-5 AEs by segment, 1-2 SDRs, 2-3 CS/AMs, 1 RevOps

First sales hire must be product-savvy, able to do technical demos. Not a traditional AE running MEDDIC on cold prospects.

Hybrid metrics

Metric	Target	Red Flag
PQL-to-close rate	25-30%	< 15%
Sales-assisted CAC payback	12-15 months	> 20 months
Self-serve % of new revenue	30-50%	< 15%
Expansion revenue % of total	25-40%	< 15%
Free-to-paid conversion	5-8% (freemium)	< 2%
TTFV for new sign-ups	< 15 min	> 60 min

6. CAC Benchmarks and Efficiency

Motion	Median CAC	CAC Payback (months)	LTV:CAC Target
Pure PLG	$200-800	4-9	5:1+
PLG + Sales Hybrid	$800-3,000	9-15	4:1+
Sales-Assisted PLG	$2,000-8,000	12-18	3.5:1+
Sales-Led	$5,000-25K+	18-24	3:1+
Agent-Led Discovery	$1,000-5,000	8-14	4:1+

CAC reduction by timeline:

Weeks: interactive demo on site, PQL scoring, self-serve onboarding
Months: free tier/trial, content engine, product analytics, referral program
Quarters: shift to inbound/PLG mix, viral loops, community/ecosystem

7. Motion Migration Paths

PLG to Hybrid (trigger: enterprise users stalling at procurement):

Instrument PQL signals (seats, usage, feature gates)
Define threshold (e.g., 5+ active users from same domain)
Hire product-savvy AE, build enterprise tier (SSO, admin, compliance)
CRM integration to surface PQLs. Target: 25%+ PQL-to-close rate

Sales-Led to Hybrid (trigger: CAC payback > 20 months):

Build free/trial tier for self-qualification
Interactive demo on website, usage tracking in free tier
Train AEs to leverage usage data. Target: 20-30% CAC reduction in 2 quarters

Pricing alignment:

Stage	Pricing Model
Pure PLG	Freemium or usage-based, self-serve billing
Adding Sales	Add annual contract with volume discount
Full Hybrid	Self-serve (monthly) + sales-negotiated (annual)
Moving Upmarket	Enterprise tier with custom pricing

8. Free Trial vs Freemium Decision

Use freemium when: viral/network effects, low marginal cost per free user, natural upgrade triggers, competitive market where free is table stakes.

Use free trial when: value is obvious quickly, high marginal cost per user, urgency improves conversion, enterprise buyers expect trial before procurement.

Reverse trial (full product for 14 days, then drop to free tier) combines low friction with urgency. Works when premium features are clearly valuable.

Industry-specific trial-to-paid rates

Industry	Rate	Industry	Rate
CRM	29%	Project Management	18%
AdTech	24%	Developer Tools	15%
HR Software	23%	Enterprise SaaS	10-15%

9. Stage-Specific Playbooks

Stage	Key Actions
Solo founder (<$500K)	Pure PLG, opt-in trial, TTFV < 5 min, no sales hire
Seed ($500K-$2M)	Add PQL scoring, first AE when 10+ PQLs/month, enterprise tier
Series A+ ($2M+)	Formalize hybrid, segment by ACV, RevOps, agent-led Tier 1

10. Common Mistakes

Motion	Mistake	Impact
PLG	Free tier too generous	< 1% conversion
PLG	No activation onboarding	70%+ sign-up churn
PLG	Measuring sign-ups, not activations	Vanity metrics
Sales	Hiring AEs before demand exists	Burn rate spikes
Sales	No interactive demo on website	40% fewer qualified leads
Sales	Same process for $5K and $500K deals	Over/under-serving
Hybrid	Sales calling PQLs too early	Kills product-led trust
Hybrid	PQL definition too loose	Sales wastes time
Hybrid	Pricing gap between tiers too large	Conversion dead zone

Examples

User says: "Should we be PLG or sales-led?" → Result: Agent asks ACV and product complexity; uses cheat sheet (e.g. ACV <$1K simple → Pure PLG; $10–50K → Hybrid); recommends TTFV target by category (API <5 min, workflow <15 min, enterprise <1 day) and LTV:CAC 3:1 minimum.
User says: "Our free users don't convert" → Result: Agent checks activation (target >40% reach value moment) and PQL definition; suggests value-before-purchase design and upgrade pressure at limit; warns on sales calling PQLs too early in hybrid.
User says: "Design our sales motion" → Result: Agent maps current state (inbound/outbound/PLG); recommends motion from ACV and complexity; outlines TTFV, NRR, self-serve % targets; ties to ai-pricing and gtm-metrics.

Troubleshooting

Over or under-serving → Cause: Same process for $5K and $500K deals. Fix: Segment by ACV; self-serve for low, AE for high; define PQL and when sales enters.
Hybrid kills PLG trust → Cause: Sales touching PQLs too early. Fix: Let product drive activation first; sales on expansion or when multi-stakeholder; clear handoff criteria.
Conversion dead zone → Cause: Pricing gap between tiers too large. Fix: Add mid tier or usage-based step; aim for >25% PQL conversion; test price sensitivity.

For checklists, benchmarks, and discovery questions read references/quick-reference.md when you need detailed reference.

Related Skills

positioning-icp - Define your ICP and positioning before choosing a motion
ai-pricing - Set pricing tiers that align with your chosen motion
ai-cold-outreach - Execute outbound for sales-led or hybrid motions
ai-sdr - Build and manage AI-augmented SDR workflows
multi-platform-launch - Coordinate launch across channels for any motion
solo-founder-gtm - GTM playbook when you are the entire sales team
gtm-metrics - Track the right metrics for your motion type

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Sales Motion Design

Before Starting

1. The Motion Selection Matrix

Decision criteria beyond price x complexity

2. Motion Archetypes in Detail

2A. Pure PLG

2B. PLG + Sales Hybrid

2C. Sales-Assisted PLG

2D. Sales-Led

2E. Agent-Led Discovery (Emerging, 2025-2026)

3. Value-Before-Purchase Experiences

Value-before-purchase tactics ranked by conversion lift

Implementation notes

Choosing the right tactic

4. Time-to-First-Value (TTFV) as North Star

TTFV benchmarks by product type

TTFV optimization steps

TTFV anti-patterns

5. Hybrid Motion Architecture

Hybrid motion structure

When to add sales to PLG

Hybrid team structure

Hybrid metrics

6. CAC Benchmarks and Efficiency

7. Motion Migration Paths

8. Free Trial vs Freemium Decision

Industry-specific trial-to-paid rates

9. Stage-Specific Playbooks

10. Common Mistakes

Examples

Troubleshooting

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