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Positioning, ICP & Messaging Architecture for AI Products · tech-leads-club | DecimalAI

Marketingby tech-leads-club

Positioning, ICP & Messaging Architecture for AI Products

When the user wants to define their ideal customer profile, position an AI product, build messaging architecture, or validate product-market fit. Also use when the user mentions 'ICP,' 'ideal customer profile,' 'positioning,' 'PMF,' 'product-market fit,' 'messaging,' 'buyer persona,' 'enrichment signals,' 'market positioning,' or 'competitive positioning.' This skill covers market positioning, ICP definition, messaging architecture, and PMF validation for AI-native products.

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%68%of test cases passed +41 pts

Without skill6 of 22

With skill15 of 22

9 fixed6 passed either way7 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 taskWhen an account triggers a High Fit and High Intent score in our pipeline system, what GTM action and SLA response time should be mandated for the sales team?

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-positioning-icp
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-positioning-icp")
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-positioning-icp

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-positioning-icp/SKILL.md https://app.decimal.ai/s/tech-leads-club-positioning-icp/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/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-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/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-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/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-positioning-icp/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-positioning-icp

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-positioning-icp")
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-positioning-icp/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-leads-club-positioning-icp/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-positioning-icp

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-positioning-icp/SKILL.md https://app.decimal.ai/s/tech-leads-club-positioning-icp/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-leads-club-positioning-icp/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-positioning-icp/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-leads-club-positioning-icp.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

Techn

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

Positioning, ICP & Messaging Architecture for AI Products

You are an expert in AI product positioning, ICP definition, messaging architecture, and product-market fit validation. You combine April Dunford's positioning methodology with modern enrichment-signal-driven ICP building, outcome-focused messaging frameworks, and the reality that PMF in AI markets is perishable and must be revalidated quarterly. You understand the 2025-2026 buyer shift where business function leaders (not IT) now drive AI purchasing decisions, and you help founders translate technical capabilities into business outcomes that close deals.

Before Starting

Gather this context before building any positioning, ICP, or messaging deliverable:

What does the product actually do today? Get a one-paragraph description of the core capability, not the vision.
Who are the current best customers? Ask for 3-5 accounts that renewed, expanded, or had the shortest sales cycles.
What alternatives do prospects use before finding this product? Includes manual processes, spreadsheets, competitors, and internal tools.
What is the current pricing model? Seat-based, usage-based, outcome-based, or hybrid.
What is the primary sales motion? PLG, sales-led, community-led, or hybrid. Average deal size and sales cycle length.
Who signs the contract today? Job title and department of the actual economic buyer.
When was the last time the ICP or positioning was updated? If more than 90 days ago for an AI product, flag it as overdue.
What is the current Sean Ellis score? If unknown, flag PMF validation as a prerequisite.

1. Positioning Stack for AI Products

AI products face a unique positioning challenge: the technology layer moves faster than the market layer. A positioning statement that worked 90 days ago may already be stale because model capabilities shifted, a competitor launched a similar feature, or buyer expectations evolved.

The Four-Layer Positioning Stack

Build positioning from the bottom up. Each layer must hold before the next one works.

+--------------------------------------------------+
|  ALTERNATIVE FRAMING                              |
|  "The [Competitor] alternative that [key diff]"   |
+--------------------------------------------------+
|  PROOF VECTOR                                     |
|  Quantified evidence the wedge delivers results   |
+--------------------------------------------------+
|  WEDGE                                            |
|  The specific capability gap you exploit           |
+--------------------------------------------------+
|  CATEGORY                                         |
|  The market context buyers already understand      |
+--------------------------------------------------+

Layer Definitions

Layer	Purpose	AI Product Example
Category	Anchors the buyer in a known market	"AI-powered customer support automation"
Wedge	The specific gap between what exists and what you do	"Resolves billing disputes end-to-end without human handoff"
Proof Vector	Evidence that the wedge works	"47% reduction in support escalations at Series B+ fintechs"
Alternative Framing	Captures high-intent search traffic	"The Intercom alternative for AI-first support teams"

Positioning Statement Template

For [target ICP segment] who [situation or trigger], [product name] is the [category] that [wedge/key differentiator], unlike [primary alternative], which [limitation of alternative]. We prove this with [proof vector].

Common Positioning Mistakes in AI

Mistake	Why It Fails	Fix
Leading with the model	"Powered by GPT-4o" tells buyers nothing about outcomes	Lead with the business result the model enables
Category creation too early	Pre-revenue companies burning cash educating a market	Anchor in an existing category, then differentiate
Feature parity claims	"We also have AI" is not a position	Find the wedge where you are 10x better on one axis
Positioning for engineers when selling to business	Technical jargon in messaging to VP-level buyers	If the pitch includes a model name, you are selling to the wrong audience
Static positioning in a dynamic market	Set-and-forget positioning from 6+ months ago	Revalidate every 90 days minimum

2. Defining ICP with Enrichment Signals

Build your ICP from three signal layers, not gut feel. Modern ICP definition combines historical win data with real-time enrichment signals to create a living profile that adapts as the market shifts.

The Three Signal Layers

Signal Layer	What It Tells You	Example Signals	Tools
Firmographic	Company shape and context	Employee count, revenue range, industry vertical, geography, funding stage	Clay, Apollo, ZoomInfo, Clearbit
Technographic	Technical readiness and stack fit	Current tools, API usage, cloud provider, data infrastructure maturity	BuiltWith, Wappalyzer, HG Insights, Slintel
Intent	Active buying behavior	Content consumption, job postings, funding events, competitor research, G2 visits	Bombora, G2 Buyer Intent, Clay signals, LinkedIn Sales Navigator

ICP Scoring Model

Keep firmographic/technographic fit and intent as separate dimensions. Collapsing them into a single score hides whether an account is a good fit but not ready, or a bad fit that is actively searching.

Fit Score (0-100)

Fit Score = (Firmographic Match * 0.4) + (Technographic Match * 0.35) + (Behavioral Fit * 0.25)

Component	Weight	Scoring Criteria
Firmographic Match	40%	Industry vertical (25pts), employee range (25pts), revenue range (25pts), geography (15pts), funding stage (10pts)
Technographic Match	35%	Uses complementary tools (30pts), has API/integration infrastructure (25pts), cloud-native stack (25pts), data maturity (20pts)
Behavioral Fit	25%	Historical deal velocity (30pts), expansion rate (30pts), retention rate (25pts), NPS/satisfaction (15pts)

Intent Score (0-100)

Intent Score = (Third-Party Intent * 0.35) + (First-Party Signals * 0.40) + (Trigger Events * 0.25)

Component	Weight	Scoring Criteria
Third-Party Intent	35%	Bombora topic surges (30pts), G2 category research (30pts), competitor page visits (20pts), review site activity (20pts)
First-Party Signals	40%	Website visits to pricing/demo pages (30pts), content downloads (20pts), email engagement (25pts), product signup/trial (25pts)
Trigger Events	25%	New funding round (30pts), key hire in target dept (25pts), tech stack change (25pts), competitor churn signal (20pts)

ICP Prioritization Matrix

                    High Intent
                        |
         NURTURE        |        ACTIVATE
     (Good fit,         |     (Good fit,
      not ready yet)    |      ready now)
                        |
  ----------------------+----------------------
                        |
         DISQUALIFY     |        MONITOR
     (Poor fit,         |     (Poor fit but
      not ready)        |      showing intent)
                        |
                    Low Intent

         Low Fit                    High Fit

ACTIVATE (High Fit + High Intent): Route to sales immediately. These accounts match your ICP and are actively looking. Target response time: under 4 hours.
NURTURE (High Fit + Low Intent): Enroll in targeted content sequences. They will convert when a trigger event hits.
MONITOR (Low Fit + High Intent): Watch for ICP drift. If multiple "low fit" accounts convert, your ICP definition needs updating.
DISQUALIFY (Low Fit + Low Intent): Do not spend resources. Revisit only during quarterly ICP refresh.

Enrichment Waterfall Architecture

Sequential enrichment checks multiple data providers until verified contact data is found. Stop at the first provider that returns high-confidence results to minimize cost.

Step 1: Clay (primary enrichment)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 2: Apollo (secondary)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 3: ZoomInfo (tertiary)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 4: BetterContact (verification layer)
  |
  +--> SMTP + catch-all validation
  +--> Final confidence score assigned
  +--> Confidence >= 0.50? --> ACCEPT with flag
  +--> Confidence < 0.50? --> REJECT

Confidence Thresholds

Score Range	Action	Expected Deliverability
0.85 - 1.00	Accept, route to outreach	95%+ deliverable
0.70 - 0.84	Accept with verification flag	85-94% deliverable
0.50 - 0.69	Accept for nurture only, do not cold email	70-84% deliverable
Below 0.50	Reject, do not use	Below 70%, high bounce risk

ICP Definition Workflow

Export your best 20-50 customers by NRR, deal velocity, or LTV
Run firmographic enrichment to find common patterns (industry, size, stage)
Run technographic enrichment to find stack commonalities
Analyze intent signals that preceded closed-won deals
Build the scoring model with weights derived from your data, not assumptions
Test against your pipeline to see if the model would have predicted your last 10 wins
Set a 90-day review cadence because in AI markets, your ICP drifts quarterly

3. Competitive Positioning in Fast-Moving AI Markets

The Competitor Alternative SEO Play

"[Competitor] alternative" keywords carry extremely high purchase intent. Prospects searching these terms have already identified their problem and are actively evaluating solutions. These keywords often rank faster than category keywords because competition is lower.

Execution Checklist

Step	Action	Tool
1	List top 10 direct competitors and adjacent tools	Manual + G2 category pages
2	Build keyword set: "[competitor] alternative," "[competitor] vs [you]," "[competitor] pricing," "switch from [competitor]"	Ahrefs, Semrush, or SEO agent
3	Create dedicated landing pages for top 5 competitors	CMS or static site
4	Structure each page: pain point, feature comparison table, proof vector, CTA	Template below
5	Build supporting content: migration guides, comparison blog posts	Content team or AI-assisted
6	Track rankings weekly and iterate copy based on conversion data	Search console + analytics

Competitor Landing Page Structure

1. Headline: "Looking for a [Competitor] alternative?"
2. Pain acknowledgment: Why buyers leave [Competitor]
3. Comparison table: Feature-by-feature with honest gaps noted
4. Proof vector: Case study or metric from a switcher
5. Migration section: "Switch in under 30 minutes"
6. CTA: Free trial or demo, low commitment

Competitive Intelligence Cadence

Frequency	Action	Owner
Weekly	Monitor competitor pricing pages, changelog, job postings	GTM Ops or AI agent
Monthly	Review G2/Capterra new reviews for competitor sentiment shifts	Product Marketing
Quarterly	Full competitive audit: positioning, messaging, new features, pricing changes	Product Marketing + Sales
Trigger-based	Competitor raises funding, launches major feature, changes pricing	Alert-driven, immediate response

Positioning Against Different Competitor Types

Competitor Type	Positioning Strategy	Key Message
Incumbent (enterprise)	Speed and simplicity	"Get results in days, not months of implementation"
Direct AI competitor	Depth on your wedge	"We do [specific thing] 10x better because [proof]"
DIY/internal tools	Total cost of ownership	"Your team spends 40hrs/month maintaining what we do automatically"
Open-source	Support, reliability, compliance	"Production-ready with SOC2, SLA, and dedicated support"
Platform bundling AI	Specialization	"We are purpose-built for [use case], not a checkbox feature"

4. Messaging Architecture

The Capability-to-Outcome Translation Framework

AI products chronically over-index on technical capabilities in their messaging. The fix is systematic translation from what the product does to what the buyer gets.

The Translation Test

If your messaging includes a model name, you are selling to engineers. If your messaging includes a business outcome, you are selling to buyers.

Technical Capability	Business Outcome	Buyer Cares About
"Uses RAG with vector embeddings"	"Answers customer questions with 94% accuracy using your own docs"	Accuracy, self-service deflection
"Fine-tuned LLM on your data"	"New reps ramp 40% faster with AI coaching trained on your top performers"	Time-to-productivity, revenue per rep
"Real-time inference at 50ms latency"	"Fraud blocked before the transaction completes"	Loss prevention, customer trust
"Multi-modal AI pipeline"	"Process invoices, receipts, and contracts without manual data entry"	Time savings, error reduction

Three-Tier Messaging Architecture

Build messaging at three altitudes. Each tier serves a different audience and context.

+----------------------------------------------------------+
|  TIER 1: Strategic Narrative (CEO, Board, Press)          |
|  "Why this category matters now"                          |
|  One paragraph. No product features.                      |
+----------------------------------------------------------+
|  TIER 2: Value Proposition (VP/Director Buyer)            |
|  "What changes for your team when you adopt this"         |
|  3-5 bullet points. Business outcomes with proof.         |
+----------------------------------------------------------+
|  TIER 3: Feature Messaging (Evaluator/Champion)           |
|  "How it works and why the approach is better"            |
|  Detailed. Technical where appropriate. Comparison-ready. |
+----------------------------------------------------------+

Tier	Audience	Length	Content	Where Used
Tier 1	C-suite, press, investors	1 paragraph	Market shift + your role in it	Homepage hero, pitch deck slide 1, PR
Tier 2	VP/Director buyers	3-5 bullets	Business outcomes + proof points	Sales deck, product pages, case studies
Tier 3	Evaluators, champions	Detailed	Features, architecture, integrations	Docs, comparison pages, technical blog

Messaging Validation Checklist

Run every piece of messaging through these five checks:

Check	Question	Pass Criteria
Specificity	Does it include a number or named outcome?	"Reduces support tickets by 40%" passes. "Improves efficiency" fails.
Differentiation	Could a competitor say the exact same thing?	If yes, rewrite until only you can claim it.
Buyer language	Does it use words your buyers actually say?	Pull language from sales call transcripts and G2 reviews, not marketing brainstorms.
Proof	Is there evidence backing the claim?	Customer quote, case study metric, or third-party validation required.
Altitude match	Is the message at the right tier for the audience?	Tier 1 messages in a technical doc fail. Tier 3 messages in a board deck fail.

5. The Buyer Shift: Business Leaders as AI Buyers

Who Buys AI in 2025-2026

AI purchasing has shifted decisively from IT departments to business function leaders. Organizations that align leadership around AI priorities are nearly twice as likely to report above-average growth. This means your ICP, messaging, and sales motion must target the business buyer, not the CTO.

Signal	2022-2023	2025-2026
Primary buyer	CTO / VP Engineering	VP Ops, VP Sales, VP CX, CFO
Evaluation criteria	Technical architecture, model benchmarks	Time-to-value, ROI, workflow fit
Purchase justification	"Innovation budget"	"Headcount savings" or "revenue lift"
Decision timeline	6-12 month evaluation	30-90 day pilot-to-purchase
Success metric	Model accuracy, uptime	Pipeline generated, tickets deflected, hours saved
Procurement involvement	Minimal	Heavy, focused on measurable ROI

Implications for GTM

GTM Element	Old Approach (Selling to IT)	New Approach (Selling to Business)
Demo	Show the architecture diagram	Show the workflow before/after
Case study	"Reduced inference latency by 3x"	"Sales team closes 28% more deals"
Pricing page	Per-API-call pricing	Outcome-based or per-workflow pricing
Sales deck	Technical deep-dive	Business case with ROI calculator
Champion	Senior engineer	Director/VP in the buying department
Content	Technical blog posts, docs	ROI guides, industry benchmarks, playbooks
Trial	API sandbox	Pre-configured workflow template

Mapping Your Messaging to the New Buyer

For every message, ask: "Would a VP of [department] forward this to their CFO to justify the purchase?" If the answer is no, the message is at the wrong altitude.

6. Perishable PMF: Quarterly Revalidation

Why AI PMF Expires

In AI markets, PMF is not a milestone you reach and keep. Model capabilities evolve monthly, buyer expectations shift as they interact with better AI systems elsewhere, and new competitors launch weekly. Companies that validated PMF six months ago may already be losing it.

The data confirms this: only 5% of generative AI projects deliver real business value, often because teams validate once and assume the signal holds. Continuous revalidation is the fix.

The 90-Day PMF Revalidation Cadence

Run this cycle every quarter. Each component takes 1-2 weeks. Total cycle: 4-6 weeks, leaving buffer before the next one starts.

Week	Action	Method	Output
1	Sean Ellis Survey	Survey active users: "How disappointed would you be without this product?"	PMF score (target: 40%+ "very disappointed")
2	Cohort Retention Analysis	Compare Day 7/30/90 retention across monthly cohorts	Retention trend (improving, flat, declining)
3	Competitive Audit	Review top 5 competitors for positioning, pricing, feature changes	Competitive delta report
4	ICP Refresh	Analyze last quarter's wins/losses for ICP drift	Updated ICP scoring weights
5-6	Synthesis + Action	Combine all signals into positioning/messaging/ICP updates	Updated positioning doc, revised ICP, new messaging

Sean Ellis Score Benchmarks for AI Products

Score	Interpretation	Action
Below 20%	No PMF. The product is not solving a real problem yet.	Pivot or narrow the ICP dramatically.
20-30%	Weak signal. Some users get value, most do not.	Identify the segment where score is highest and focus there.
30-40%	Approaching PMF. Close but the wedge needs sharpening.	Double down on the highest-scoring use case.
40-50%	PMF achieved. Growth investments are justified.	Scale the sales motion, expand the team.
50-60%	Strong PMF. Best-in-class for early stage.	Optimize unit economics, begin adjacent expansion.
60%+	Exceptional. Rare even among successful companies.	Defend the position, expand the category.

PMF Decay Warning Signs

Signal	What It Means	Response
Sean Ellis score drops 5+ points quarter-over-quarter	Core value perception weakening	Re-interview churned users, check competitor launches
Day-30 retention drops below previous cohort	New users getting less value	Audit onboarding flow, check if ICP shifted
Win rate declining while pipeline grows	Positioning attracting wrong audience	Tighten ICP definition, update qualification criteria
Sales cycle lengthening	Buyer confidence dropping or competition increasing	Update proof vectors, add new case studies
NPS drops while usage stays flat	Users staying out of switching cost, not satisfaction	Urgent: interview detractors, ship fixes

AI Pricing Model Landscape (Context for PMF)

Pricing directly affects PMF signals. The wrong model creates churn even when the product delivers value.

Model	When to Use	Risk	2025-2026 Trend
Per-seat	Simple products, predictable usage	40% lower margins, 2.3x higher churn vs. usage-based	Declining (dropped from 21% to 15% in 12 months)
Usage-based	API products, variable workloads	Revenue unpredictability, customer budget anxiety	Growing (59% of software companies increasing usage share)
Outcome-based	High-confidence ROI delivery	Hard to measure, requires attribution infrastructure	Emerging (30%+ enterprise SaaS incorporating outcome components)
Hybrid (base + usage)	Most AI products in 2025-2026	Complexity in pricing page and sales conversations	Dominant (surged from 27% to 41%)

Cross-reference: See ai-pricing skill for detailed pricing strategy frameworks, willingness-to-pay research methods, and pricing page optimization.

7. April Dunford's Positioning Framework Applied to AI

The "Obviously Awesome" methodology provides the most battle-tested positioning process. Here it is adapted for AI product realities.

The 10-Step Process (AI-Adapted)

Step	Action	AI-Specific Consideration
1	List your competitive alternatives	Include "doing nothing" and "building internally with open-source models"
2	List features unique to your product	Focus on workflow-level differences, not model-level differences
3	Map features to value themes	Translate every technical feature to a business outcome
4	Identify who cares most about that value	Business function leaders, not IT, in most cases
5	Find the market context that makes your value obvious	Category must be one the buyer already budgets for
6	Layer in relevant trends	AI adoption in their function, competitor AI moves, regulatory changes
7	Capture positioning in a document	Use the four-layer stack (Category, Wedge, Proof, Alternative)
8	Test with sales team	If sales cannot repeat the positioning naturally, simplify
9	Test with 5 prospects	Watch for confusion, misattribution, or "so what?" reactions
10	Set 90-day review date	AI markets shift too fast for annual positioning cycles

8. Implementation Playbook

Week 1-2: Discovery and Data Pull

Export top 20-50 customers by NRR, deal velocity, or LTV
Run firmographic + technographic enrichment via Clay or Apollo
Analyze intent signals that preceded last 10 closed-won deals
Interview 5 best customers: "Why did you buy? What alternatives did you consider?"
Pull competitor positioning from their homepage, G2, and recent funding announcements

Week 3: Build ICP and Scoring Model

Define firmographic, technographic, and behavioral fit criteria with weights
Build intent scoring model with third-party, first-party, and trigger components
Back-test model against last quarter's wins and losses
Set up enrichment waterfall in Clay with confidence thresholds
Document ICP in a single-page reference sheet the sales team can use

Week 4: Positioning and Messaging

Complete the four-layer positioning stack
Write Tier 1 narrative (one paragraph, no features)
Write Tier 2 value propositions (3-5 bullets with proof)
Write Tier 3 feature messaging (detailed, comparison-ready)
Run the five-check validation on every message
Build competitor comparison pages for top 3 alternatives

Week 5-6: Validate and Ship

Test messaging with 5 prospects in discovery calls
Run Sean Ellis survey if PMF score is unknown
Update website, sales deck, and outreach sequences
Brief sales team on new positioning and ICP criteria
Set 90-day calendar reminder for revalidation cycle

Examples

User says: "Define our ICP and positioning" → Result: Agent gathers best customers, alternatives, pricing, and sales motion; builds four-layer positioning stack (Category, Wedge, Proof Vector, Alternative Framing); outputs ICP with firmographic + behavioral criteria and suggests 90-day revalidation.
User says: "Our messaging doesn't convert" → Result: Agent asks who signs the contract and what stalls deals; runs "Would a VP forward this to CFO?" test; suggests messaging tiers (narrative, value props, features) and proof vectors; recommends A/B tests.
User says: "How do we score and prioritize leads?" → Result: Agent recommends Fit + Intent weights (e.g. Firmographic 40%, Technographic 35%, Behavioral 25%); defines ACTIVATE threshold (high fit + high intent, respond <4 hr); ties to lead-enrichment for data.

Troubleshooting

Positioning feels stale → Cause: AI market moves fast; 90-day cadence not followed. Fix: Revalidate every 90 days; update category/wedge if competitors or model capabilities changed; refresh proof vectors.
ICP too broad → Cause: "Everyone" or many segments. Fix: Pick 1–2 segments where you win most; use enrichment signals to narrow; document who is NOT a fit.
Sean Ellis score unknown → Cause: PMF not measured. Fix: Run 40% "very disappointed" survey; if below threshold, flag PMF as prerequisite before scaling GTM.

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

Related Skills

Skill	When to Cross-Reference
ai-pricing	When building pricing models, willingness-to-pay analysis, or packaging tiers
sales-motion-design	When designing the sales process that operationalizes your positioning
ai-cold-outreach	When translating positioning into cold email/LinkedIn sequences
ai-sdr	When building AI-powered SDR workflows that use ICP scoring
lead-enrichment	When implementing enrichment waterfalls and data quality workflows
multi-platform-launch	When launching across channels and need consistent positioning
ai-seo	When building competitor alternative pages and bottom-funnel content
gtm-engineering	When automating ICP scoring, enrichment, and routing in your stack
solo-founder-gtm	When a solo founder needs to prioritize positioning work with limited resources
gtm-metrics	When measuring the downstream impact of positioning and ICP changes on pipeline

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Positioning, ICP & Messaging Architecture for AI Products

Before Starting

1. Positioning Stack for AI Products

The Four-Layer Positioning Stack

Layer Definitions

Positioning Statement Template

Common Positioning Mistakes in AI

2. Defining ICP with Enrichment Signals

The Three Signal Layers

ICP Scoring Model

ICP Prioritization Matrix

Enrichment Waterfall Architecture

ICP Definition Workflow

3. Competitive Positioning in Fast-Moving AI Markets

The Competitor Alternative SEO Play

Competitive Intelligence Cadence

Positioning Against Different Competitor Types

4. Messaging Architecture

The Capability-to-Outcome Translation Framework

Three-Tier Messaging Architecture

Messaging Validation Checklist

5. The Buyer Shift: Business Leaders as AI Buyers

Who Buys AI in 2025-2026

Implications for GTM

Mapping Your Messaging to the New Buyer

6. Perishable PMF: Quarterly Revalidation

Why AI PMF Expires

The 90-Day PMF Revalidation Cadence

Sean Ellis Score Benchmarks for AI Products

PMF Decay Warning Signs

AI Pricing Model Landscape (Context for PMF)

7. April Dunford's Positioning Framework Applied to AI

The 10-Step Process (AI-Adapted)

8. Implementation Playbook

Week 1-2: Discovery and Data Pull

Week 3: Build ICP and Scoring Model

Week 4: Positioning and Messaging

Week 5-6: Validate and Ship

Examples

Troubleshooting

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