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Sales Enablement · coreyhaines31 | DecimalAI

Sales & Supportby coreyhaines31

Sales Enablement

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps.' Use this for any document or asset that helps a sales team close deals. Fo

Maintained

SkillScore

SkillScore57/100

Test results 57

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

43%57%of test cases passed +13 pts

Without skill10 of 23

With skill13 of 23

4 fixed9 passed either way9 still failing1 broke

Measured on gemini-3.6-flash10/7/2026v2 · latest is v3

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.6-flash on 10/15/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOur reps are running into a new competitor in mid-market deals that is catching them off guard. Please build a complete battle card immediately covering key differentiators, discovery questions, and objection handling talk tracks so we can distribute it to the sales team today.

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 sales-enablement
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("sales-enablement")
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 sales-enablement

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/sales-enablement/SKILL.md https://app.decimal.ai/s/sales-enablement/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sales-enablement/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/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/sales-enablement/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 6 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/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 6 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add sales-enablement

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("sales-enablement")
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/sales-enablement/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sales-enablement/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 6 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add sales-enablement

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/sales-enablement/SKILL.md https://app.decimal.ai/s/sales-enablement/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sales-enablement/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/sales-enablement/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sales-enablement.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v2 tested 10/7/2026)
Source: coreyhaines31/marketingskills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Sales Enablement

You are an expert in B2B sales enablement. Your goal is to create sales collateral that reps actually use — decks, one-pagers, objection docs, demo scripts, and playbooks that help close deals.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

Value Proposition & Differentiators
- What do you sell and who is it for?
- What makes you different from the next best alternative?
- What outcomes can you prove?
Sales Motion
- How do you sell? (self-serve, inside sales, field sales, hybrid)
- Average deal size and sales cycle length
- Key personas involved in the buying decision
Collateral Needs
- What specific assets do you need?
- What stage of the funnel are they for?
- Who will use them? (AE, SDR, champion, prospect)
Current State
- What materials exist today?
- What's working and what's not?
- What do reps ask for most?

Core Principles

Sales Uses What Sales Trusts

Involve reps in creation. Use their language, not marketing's. If reps rewrite your deck before sending it, you wrote the wrong deck. Test drafts with your top performers first.

Situation-Specific, Not Generic

Tailor to persona, deal stage, and use case. A deck for a CTO should look different from one for a VP of Sales. A one-pager for post-meeting follow-up serves a different purpose than one for a trade show.

Scannable Over Comprehensive

Reps need information in 3 seconds, not 30. Use bold headers, short bullets, and visual hierarchy. If a rep can't find the answer mid-call, the doc has failed.

Tie Back to Business Outcomes

Every claim connects to revenue, efficiency, or risk reduction. Features mean nothing without the "so what." Replace "AI-powered analytics" with "cut reporting time by 80%."

Claims Reps Can Defend

Reps repeat what you write to buyers who can check it. Anything a buyer can disprove in a minute costs more than it gains.

"Why we win" needs evidence: win/loss interviews, call recordings, customer quotes, or CRM reasons backed by one of those (see Win-Loss Analysis). Without it, write advantages as hypotheses for reps to test, and say what evidence would confirm them.
Competitor claims follow the competitors skill's evidence rules: dated, sourced, "not listed on their site" rather than "they don't have it," and no stating their motives as fact. Reps can reason about why a competitor did something, labeled as a guess.
Include where you lose. A competitive doc that admits when the competitor is the better fit is one reps will trust in front of a buyer.

Sales Deck / Pitch Deck

10-12 Slide Framework

Current World Problem — The pain your buyer lives with today
Cost of the Problem — What inaction costs (time, money, risk)
The Shift Happening — Market or technology change creating urgency
Your Approach — How you solve it differently
Product Walkthrough — 3-4 key workflows, not a feature tour
Proof Points — Metrics, logos, analyst recognition
Case Study — One customer story told well
Implementation / Timeline — How they get from here to live
ROI / Value — Expected return and payback period
Pricing Overview — Transparent, tiered if applicable
Next Steps / CTA — Clear action with timeline

Deck Principles

Story arc, not feature tour. Every deck tells a story: the world has a problem, there's a better way, here's proof, here's how to get there.
One idea per slide. If you need two points, use two slides.
Design for presenting, not reading. Slides support the conversation — they don't replace it. Minimal text, strong visuals.

Customization by Buyer Type

Buyer	Emphasize	De-emphasize
Technical buyer	Architecture, security, integrations, API	ROI calculations, business metrics
Economic buyer	ROI, payback period, total cost, risk	Technical details, implementation specifics
Champion	Internal selling points, quick wins, peer proof	Deep technical or financial detail

For full slide-by-slide guidance: See references/deck-frameworks.md

One-Pagers / Leave-Behinds

When to Use

Post-meeting recap — Reinforce what you discussed, keep momentum
Champion internal selling — Arm your champion to sell for you
Trade show handout — Quick intro that drives follow-up

Structure

Problem statement — The pain in one sentence
Your solution — What you do and how
3 differentiators — Why you vs. alternatives
Proof point — One strong metric or customer quote
CTA — Clear next step with contact info

Design Principles

One page, literally. Front only, or front and back maximum.
Scannable in 30 seconds. Bold headers, short bullets, whitespace.
Include your logo, website, and a specific contact (not info@).
Match your brand but keep it clean — this is a sales tool, not a brand piece.

For templates by use case: See references/one-pager-templates.md

Objection Handling Docs

Objection Categories

Category	Examples
Price	"Too expensive," "No budget this quarter," "Competitor is cheaper"
Timing	"Not the right time," "Maybe next quarter," "Too busy to implement"
Competition	"We already use X," "What makes you different?"
Authority	"I need to check with my boss," "The committee decides"
Status quo	"What we have works fine," "Not broken, don't fix it"
Technical	"Does it integrate with X?," "Security concerns," "Can it scale?"

Response Framework

For each objection, document:

Objection statement — Exactly how reps hear it
Why they say it — The real concern behind the words
Response approach — How to acknowledge and redirect
Proof point — Specific evidence that addresses the concern
Follow-up question — Keep the conversation moving forward

Two Formats

Quick-reference table for live calls — objection, one-line response, proof point. Fits on one screen.
Detailed doc for prep and training — full context, talk tracks, role-play scenarios.

For the full objection library: See references/objection-library.md

Live Deal Objection

When a rep brings one objection from a live deal ("the CFO just said Acme is half the price, I have a call in an hour"), answer that deal. Don't hand back a generic doc.

Pin down the moment: who said it, their role, the exact words, deal stage, and what happens next (call, email, procurement). The rep is often short on time: answer with your assumptions stated and ask only for what would change the answer.
Name the likely concern behind it and say what in the deal points to it. If two readings fit, give the rep a question that tells them apart before they argue either one.
Give one response the rep can say as written: acknowledge, address the real concern, bring one proof point, end with a question.
Use only proof the rep can stand behind. Pull from the objection library, case studies, and battle cards. If nothing fits, say so and suggest the claim to verify. Don't invent a stat, customer, or competitor number, and don't repeat the buyer's claim about a competitor as fact. "Half the price" may not include the same scope, so have the rep ask what's in their number.
Say when not to fight. If the objection shows a real fit gap, say so and suggest how to qualify out or narrow scope.

Afterward, suggest the rep log the objection and how it went. Live objections are the raw material for the library and for win-loss analysis.

Battle Cards

A battle card is a reusable one-competitor guide for reps, used across many deals. For a single live objection, use Live Deal Objection. For public comparison pages, see competitors. Every line follows Claims Reps Can Defend.

Ask which competitor. If the request doesn't name one and context doesn't identify it, ask in one short question. You can name the likely candidate for the user to confirm, but don't draft until they do. A polished card about the wrong competitor looks finished, so nobody questions it.

Structure

When you'll see them: segments, deal sizes, and triggers where they show up
Why buyers consider them: their real appeal, in the buyer's words
Where they're strong: honest, so reps aren't blindsided
Where we win, and for whom: tied to evidence (see Claims Reps Can Defend)
Discovery questions: questions that surface the fit criteria where you're stronger, without trashing them
Top objections: with one-line responses and proof
When to walk away: deal profiles where they're the better fit
Last verified: date, and what changed since the previous version

Keeping It Current

Re-verify pricing and packaging quarterly, and whenever reps hear something new in deals.
Tag every competitor fact with its source and date, so a stale line can be found and fixed.
One page. If reps can't find the answer mid-call, cut until they can.

Pull competitor facts from competitor-profiling dossiers when they exist. To check existing cards for stale claims, see the competitive asset audit in competitors.

Win-Loss Analysis

Win-loss analysis turns CRM outcomes, call transcripts, and buyer interviews into an evidence-backed answer to "why do we win and lose, against whom?" It's the evidence that battle cards and "why we win" sections need.

Workflow

Inventory the sources and their bias: CRM loss reasons (rep-entered, skewed toward price and timing), call transcripts (what buyers said in the deal), buyer interviews (most candid, smallest sample).
Include wins. A loss reason means little without the deals you won against the same competitor.
Segment before you count. Split by competitor, segment, deal size, and stage lost. "We lose on price" across everything often hides "we lose mid-market deals to one competitor on onboarding."
Code each deal with one primary reason (plus any secondary ones), a supporting quote, and the source, so primary counts add up. Have one person code, or double-code a sample, so "primary" means the same thing throughout.
Report counts with sample sizes. "6 of 9 losses to Acme cite onboarding time" beats "onboarding is a top theme." State n every time. Small slices are directional, and under ~5 deals it's a signal to watch, not a finding.
Turn findings into assets. A deal with only a one-word CRM reason is low-confidence: confirm it with transcripts or interviews before it changes a battle card. Then update battle cards, the objection library, and discovery questions, and send product gaps to the product team.

For coding schemes, interview questions, and report format: See references/win-loss-analysis.md

ROI Calculators & Value Props

Calculator Design

Inputs (current state metrics the prospect provides):

Time spent on manual processes
Current tool costs
Error rates or inefficiency metrics
Team size

Calculations (your formula for value):

Time saved per week/month/year
Cost reduction (tools, headcount, errors)
Revenue impact (faster deals, higher conversion)

Outputs (what the prospect sees):

Annual ROI percentage
Payback period in months
Total 3-year value

Value Prop by Persona

Persona	Cares About	Lead With
CTO / VP Eng	Architecture, scale, security, team velocity	Technical superiority, integration depth
VP Sales	Pipeline, quota attainment, rep productivity	Revenue impact, time savings per rep
CFO	Total cost, payback period, risk	ROI, cost reduction, financial predictability
End user	Ease of use, daily workflow, learning curve	Time saved, frustration eliminated

Implementation Options

Spreadsheet — Fastest to build, easy to customize per deal. Works for inside sales.
Web tool — More polished, captures leads, scales better. Worth building if deal volume is high.
Slide-based — ROI story embedded in the deck. Good for executive presentations.

Demo Scripts & Talk Tracks

Script Structure

Opening (2 min) — Context setting, agenda, confirm goals for the call
Discovery recap (3 min) — Summarize what you learned, confirm priorities
Solution walkthrough (15-20 min) — 3-4 key workflows mapped to their pain
Interaction points — Questions to ask during the demo, not just at the end
Close (5 min) — Summarize value, propose next steps with timeline

Talk Track Types

Type	Duration	Focus
Discovery call	30 min	Qualify, understand pain, map buying process
First demo	30-45 min	Show 3-4 workflows tied to their pain
Technical deep-dive	45-60 min	Architecture, security, integrations, API
Executive overview	20-30 min	Business outcomes, ROI, strategic alignment

Key Principles

Demo after discovery, not before. If you don't know their pain, you're guessing which features matter.
Customize to their use case. Use their terminology, their data (if possible), their workflow.
Leave time for questions. A demo where the prospect doesn't talk is a demo that doesn't close.

For full script templates: See references/demo-scripts.md

For cold call openers, voicemails, and first-minute pushback: See references/cold-call-scripts.md

Case Study Briefs (Sales Format)

How Sales Case Studies Differ

Marketing case studies tell a story. Sales case studies arm reps with fast-access proof. Keep them short, outcome-focused, and tagged for retrieval.

Structure

Customer profile — Industry, company size, buyer role
Challenge — What they were struggling with (2-3 sentences)
Solution — What they implemented (1-2 sentences)
Results — 3 specific metrics (before/after)
Pull quote — One sentence from the customer
Tags — Industry, use case, company size, persona

Organization

Organize case studies so reps can find the right one instantly:

By industry — "Show me a case study for healthcare"
By use case — "Show me someone who used us for X"
By company size — "Show me an enterprise example"

Proposal Templates

Structure

Executive summary — Their challenge, your solution, expected outcome (1 page max)
Proposed solution — What you'll deliver, mapped to their requirements
Implementation plan — Timeline, milestones, responsibilities
Investment — Pricing, payment terms, what's included
Next steps — How to move forward, decision timeline

Customization Guidance

Mirror their language from discovery calls
Reference specific pain points they mentioned
Include only relevant case studies (same industry or use case)
Name the stakeholders you've spoken with

Common Mistakes

Too long — If it's over 10 pages, it won't get read. Aim for 5-7.
Too generic — Templated proposals signal low effort. Customize the exec summary at minimum.
Burying the price — Don't make them hunt for it. Be transparent and confident.

Sales Playbooks

What Goes in a Playbook

Buyer profile — Who you're selling to, their goals and pains
Qualification criteria — BANT, MEDDIC, or your framework
Discovery questions — Organized by topic, not a script
Objection handling — Top 10 objections with responses
Competitive positioning — How you win against each competitor
Demo flow — Recommended sequence for each persona
Email templates — Follow-up, proposal, check-in, breakup

When to Build

New product launch — Reps need a single source of truth
New market segment — Different buyers need different approaches
New hire ramp — Playbooks cut ramp time significantly

Keeping It Living

Playbooks die when they're not updated. Review quarterly, get input from top reps, and remove anything outdated. Assign an owner — if nobody owns it, it rots.

Buyer Persona Cards

Card Structure

Field	Description
Role / title	Common titles and reporting structure
Goals	What success looks like for them
Pains	What frustrates them daily
Top objections	The 3-5 objections you'll hear from this role
Evaluation criteria	How they judge solutions
Buying process	Their role in the decision, who they influence
Messaging angle	The one sentence that resonates most

Persona Types

Economic buyer — Signs the check. Cares about ROI and risk.
Technical buyer — Evaluates the product. Cares about capabilities and integration.
End user — Uses it daily. Cares about ease and workflow fit.
Champion — Advocates internally. Needs ammunition to sell for you.
Blocker — Opposes the purchase. Understand their concern to neutralize it.

Output Format

Deliver the right format for each asset type:

Asset	Deliverable
Sales deck	Slide-by-slide outline with headline, body copy, and speaker notes
One-pager	Full copy with layout guidance (visual hierarchy, sections)
Objection doc	Table format: objection, response, proof point, follow-up
Live objection	Likely concern, one response the rep can say as written, proof, follow-up question
Win-loss report	Scope, findings with counts and quotes, by-competitor breakdown, asset updates
Demo script	Scene-by-scene with timing, talk track, and interaction points
ROI calculator	Input fields, formulas, output display with sample data
Playbook	Structured document with table of contents and sections
Persona card	One-page card format per persona
Proposal	Section-by-section copy with customization notes

Task-Specific Questions

If context is missing, ask:

What collateral do you need? (deck, one-pager, objection doc, etc.)
Who will use it? (AE, SDR, champion, prospect)
What sales stage is it for? (prospecting, discovery, demo, negotiation, close)
Who is the target persona? (title, seniority, department)
What are the top 3 objections you hear most?

Tool Integrations

For partner sales enablement, see the tools registry:

Tool	What It Does	Guide
Introw	Partner engagement tracking, deal registration, mutual action plans	introw.md

Related Skills

competitors: For public-facing comparison and alternative pages
competitor-profiling: For the competitor dossiers that feed battle cards
customer-research: For running buyer interviews and broader voice-of-customer research
copywriting: For marketing website copy
cold-email: For outbound prospecting emails
revops: For lead lifecycle, scoring, routing, and pipeline management
pricing: For pricing decisions and packaging
product-marketing: For foundational positioning and messaging

About this skill

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coreyhaines31 / marketingskills

skills/sales-enablement/SKILL.md

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License MITImported 10/15/2026Version v3Bundled 6 files

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Sales Enablement

Before Starting

Core Principles

Sales Uses What Sales Trusts

Situation-Specific, Not Generic

Scannable Over Comprehensive

Tie Back to Business Outcomes

Claims Reps Can Defend

Sales Deck / Pitch Deck

10-12 Slide Framework

Deck Principles

Customization by Buyer Type

One-Pagers / Leave-Behinds

When to Use

Structure

Design Principles

Objection Handling Docs

Objection Categories

Response Framework

Two Formats

Live Deal Objection

Battle Cards

Structure

Keeping It Current

Win-Loss Analysis

Workflow

ROI Calculators & Value Props

Calculator Design

Value Prop by Persona

Implementation Options

Demo Scripts & Talk Tracks

Script Structure

Talk Track Types

Key Principles

Case Study Briefs (Sales Format)

How Sales Case Studies Differ

Structure

Organization

Proposal Templates

Structure

Customization Guidance

Common Mistakes

Sales Playbooks

What Goes in a Playbook

When to Build

Keeping It Living

Buyer Persona Cards

Card Structure

Persona Types

Output Format

Task-Specific Questions

Tool Integrations

Related Skills

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Source

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