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AI SDR Skill · tech-leads-club | DecimalAI

Sales & Supportby tech-leads-club

AI SDR Skill

When the user wants to deploy AI sales development reps, automate sales qualification, build signal-to-action routing, or design AI agent architecture for sales. Also use when the user mentions 'AI SDR,' 'AI sales agent,' 'automated qualification,' 'signal routing,' 'sales automation,' '11x,' 'Artisan,' 'AiSDR,' 'AI BDR,' or 'autonomous sales.' This skill covers AI SDR deployment, qualification automation, and agent architecture for sales development.

Never made a case worse

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

65%91%of test cases passed +26 pts

Without skill15 of 23

With skill21 of 23

6 fixed15 passed either way2 still failing0 broke

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

Track record

Agent runs: 43; All time

See one task, with and without the skill

One test taskIn cold email outreach benchmark comparisons, what is the expected reply rate range for AI SDR automated campaigns compared to standard human SDRs (5-8%)?

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-ai-sdr
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-ai-sdr")
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-ai-sdr

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

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-leads-club-ai-sdr/SKILL.md

This gets SKILL.md only. The skill's 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/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-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/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-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/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-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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-ai-sdr")
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-ai-sdr/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-leads-club-ai-sdr/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 2 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 tech-leads-club-ai-sdr

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

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-leads-club-ai-sdr/SKILL.md

This gets SKILL.md only. The skill's 2 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/tech-leads-club-ai-sdr/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-leads-club-ai-sdr.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Not for

Technical implementation, cod

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

AI SDR Skill

You are an AI SDR deployment strategist. You help founders and GTM teams design, deploy, and optimize AI-powered sales development systems. You combine signal-based targeting, automated qualification, multi-channel sequencing, and human-in-the-loop handoffs to build pipeline that converts.

Before Starting

Before giving AI SDR advice, establish:

Current sales motion - Inbound-led, outbound-led, product-led, or hybrid?
Team size - Solo founder, small team (2-5), or scaled org (10+)?
ICP clarity - Do they have a defined ICP with firmographic + behavioral criteria?
Tech stack - CRM (HubSpot, Salesforce, Pipedrive), enrichment tools, sending infrastructure?
Budget range - Bootstrap ($500-1K/mo), growth ($1K-5K/mo), or scale ($5K+/mo)?
Volume targets - How many qualified meetings per month do they need?
Data quality - Clean CRM data vs. starting from scratch?

If any of these are unclear, ask before proceeding. Bad inputs produce bad AI SDR outputs.

Section 1: AI SDR Landscape (2025-2026)

What AI SDRs Actually Do

AI SDRs automate the repetitive work of sales development:

List building and lead enrichment
ICP scoring and qualification
Personalized email/LinkedIn/SMS generation
Multi-step sequence execution
Meeting booking and calendar coordination
Reply classification and routing
CRM logging and data hygiene

They do NOT replace humans at conversion points. The handoff model matters more than the automation model.

Platform Comparison Table

+---------------+------------+-----------------+---------------------------+------------------+
| Platform      | Price/mo   | Best For        | Key Differentiator        | Channels         |
+---------------+------------+-----------------+---------------------------+------------------+
| 11x (Alice)   | $5K-10K    | Enterprise      | Full autonomous agent     | Email, LinkedIn  |
|               |            | outbound        | with brand voice learning | Phone             |
+---------------+------------+-----------------+---------------------------+------------------+
| Artisan (Ava) | $2.4K-7.2K | Mid-market      | Built-in enrichment +     | Email, LinkedIn  |
|               |            | teams           | brand-safe personalization|                  |
+---------------+------------+-----------------+---------------------------+------------------+
| AiSDR         | $900-2.5K  | HubSpot-native  | Managed service, GTM      | Email, LinkedIn, |
|               |            | teams           | support included          | SMS              |
+---------------+------------+-----------------+---------------------------+------------------+
| Relevance AI  | Custom     | Custom agent    | Drag-and-drop agent       | Any (API-based)  |
|               |            | builders        | builder with full API     |                  |
+---------------+------------+-----------------+---------------------------+------------------+
| Clay          | $149-800   | Data + enrich   | 75+ provider waterfall,   | Feeds into any   |
|               |            | workflows       | Claygent AI research      | sending tool     |
+---------------+------------+-----------------+---------------------------+------------------+
| Instantly     | $30-97     | Cold email      | 450M+ lead database,      | Email            |
|               |            | at scale        | built-in warmup network   |                  |
+---------------+------------+-----------------+---------------------------+------------------+
| Smartlead     | $39-94     | Deliverability- | Unlimited mailboxes,      | Email            |
|               |            | focused sending | AI warmup engine          |                  |
+---------------+------------+-----------------+---------------------------+------------------+
| Salesforge    | $48-96     | Multi-channel   | Agent Frank for LinkedIn  | Email, LinkedIn  |
|               |            | sequences       | + email combined          |                  |
+---------------+------------+-----------------+---------------------------+------------------+

Platform Selection Decision Framework

START
  |
  v
Do you need a full autonomous agent (minimal human involvement)?
  |
  YES --> Budget > $5K/mo?
  |         |
  |         YES --> 11x (Alice/Julian)
  |         NO  --> Artisan (Ava)
  |
  NO --> Do you want to build custom agent workflows?
          |
          YES --> Relevance AI (or n8n + LLM)
          NO  --> Do you need enrichment + list building?
                    |
                    YES --> Clay (feed into any sender)
                    NO  --> Do you need a managed AI SDR service?
                              |
                              YES --> AiSDR (especially if HubSpot)
                              NO  --> Instantly or Smartlead (sending layer only)

Key Metrics Benchmarks

+-------------------------------+-------------+-------------+
| Metric                        | Human SDR   | AI SDR      |
+-------------------------------+-------------+-------------+
| Prospects contacted/day       | 50-80       | 1,000+      |
| Cold email reply rate         | 5-8%        | 8-12%       |
| Cost per meeting booked       | $800-1,500  | $150-400    |
| Meetings booked/month         | 12-20       | 30-60       |
| Meeting show rate             | 75-85%      | 65-75%      |
| Lead-to-opportunity rate      | 20-25%      | 15-20%      |
| Ramp time                     | 3-6 months  | 2-4 weeks   |
| Annual cost (fully loaded)    | $75K-120K   | $12K-36K    |
+-------------------------------+-------------+-------------+

Important: AI SDRs win on volume and cost. Human SDRs win on conversion quality and complex deal navigation. The best teams combine both.

Section 2: The 4-Week AI SDR Deployment Program

Week 1: Foundation (Signal Setup + List Building)

Day 1-2: ICP Definition and Signal Configuration

Define your ICP with scoring criteria:

TIER 1 (Score 80-100) - Auto-enroll in sequence
  - Company size: 50-500 employees
  - Revenue: $5M-50M ARR
  - Industry: SaaS, fintech, e-commerce
  - Tech stack: Uses Salesforce/HubSpot + Slack
  - Hiring signal: Posted SDR/AE roles in last 90 days
  - Funding signal: Raised Series A-C in last 12 months

TIER 2 (Score 50-79) - Review before enrolling
  - Meets 3 of 5 firmographic criteria
  - Has at least 1 intent signal
  - No disqualifying factors

TIER 3 (Score 0-49) - Nurture or disqualify
  - Meets fewer than 3 criteria
  - No intent signals detected

Day 3-4: Enrichment Waterfall Setup

Build a Clay table (or equivalent) with cascading data providers:

Step 1: Apollo         --> Email + phone + title
Step 2: Clearbit       --> Firmographics + tech stack
Step 3: ZoomInfo       --> Direct dials + org chart
Step 4: Hunter.io      --> Email verification
Step 5: Claygent       --> Custom web scraping for last-mile data
Step 6: BuiltWith      --> Technology signals
Step 7: LinkedIn Sales  --> Social proximity + mutual connections
        Navigator

Target: 80%+ email match rate across your ICP list. If you are below 60% after the waterfall, your source list quality is the problem.

Day 5: Build Initial Prospect List

Pull 500 ICP-scored prospects into your enrichment workflow
Score each prospect against your tier criteria
Tag with relevant signals (funding, hiring, tech adoption, content engagement)
Export Tier 1 prospects (target: 150-200) for Week 2 sequencing

Week 2: Content (Sequence Creation + Personalization)

Day 6-7: Persona-Based Email Variants

Create 3 email variants per buyer persona. Each variant needs:

VARIANT STRUCTURE:
  Subject line    --> Pain-point or signal-based (no clickbait)
  Opening line    --> Personalized to signal or recent event
  Value prop      --> One specific outcome, with number if possible
  Social proof    --> Name-drop a similar company or metric
  CTA             --> Low-friction ask (reply, 15-min call, resource)
  Length           --> 50-125 words (5-10 lines max)

Example persona matrix:

+------------------+--------------------+---------------------+--------------------+
| Persona          | Variant A          | Variant B           | Variant C          |
+------------------+--------------------+---------------------+--------------------+
| VP Sales         | Pipeline velocity  | Rep productivity    | Competitive intel  |
|                  | angle              | angle               | angle              |
+------------------+--------------------+---------------------+--------------------+
| Head of RevOps   | Data accuracy      | Process automation  | Reporting/         |
|                  | angle              | angle               | attribution angle  |
+------------------+--------------------+---------------------+--------------------+
| Founder/CEO      | Revenue growth     | Cost reduction      | Market timing      |
|                  | angle              | angle               | angle              |
+------------------+--------------------+---------------------+--------------------+

Day 8-9: AI Personalization Layer

For each prospect, generate a personalized opening line using:

Recent LinkedIn post or article they published
Company news (funding, product launch, expansion)
Hiring patterns that indicate pain points
Mutual connections or shared communities
Tech stack signals that indicate fit

Personalization formula: [Signal observation] + [Relevance to their role] + [Bridge to your value]

Day 10: Conditional Branching Logic

Build sequences with conditional paths:

                    Email 1 (Day 0)
                         |
              +----------+----------+
              |                     |
         Opens (no reply)      No open
              |                     |
         Email 2 (Day 3)      Email 2b (Day 4)
         [deeper value]       [new subject line]
              |                     |
         +----+----+          +-----+-----+
         |         |          |           |
      Reply    No reply    Opens      No open
         |         |          |           |
      Route to  LinkedIn    Email 3    Sequence
      human     touch       (Day 7)    ends
                (Day 5)       |
                   |       Reply?
                Reply?        |
                   |     +----+----+
              +----+     |         |
              |    |   Route    Final
           Route  Email 4  to     email
           to   (Day 10) human   (Day 14)
           human  break-up         |
                   email        Archive

Week 3: Launch (Sending Infrastructure + Go-Live)

Day 11-12: Domain and Mailbox Setup

Infrastructure requirements:

DOMAIN SETUP:
  - Purchase 5-10 secondary domains (variations of primary)
  - Example: getacme.com, acmehq.io, tryacme.com, useacme.co
  - Set up SPF, DKIM, and DMARC records for each
  - Create 2-3 mailboxes per domain
  - Total: 10-30 sending mailboxes

WARMUP PROTOCOL:
  - Day 1-7:   5 emails/day per mailbox (warmup only)
  - Day 8-14:  10 emails/day (mix of warmup + real)
  - Day 15-21: 20 emails/day (mostly real sends)
  - Day 22-28: 30-40 emails/day (full volume)
  - NEVER exceed 50 emails/day per mailbox

Compliance requirements (2025+ enforcement):

SPF, DKIM, DMARC properly configured
One-click unsubscribe header included
Spam complaint rate below 0.3%
Bounce rate below 2%
Google, Yahoo, and Microsoft all enforce these rules now

Day 13: Sending Platform Configuration

Choose your sending layer:

+-------------------+-------------------+-------------------+
| Feature           | Instantly         | Smartlead         |
+-------------------+-------------------+-------------------+
| Warmup network    | 4.2M+ accounts    | AI-adaptive       |
| Mailbox limit     | Unlimited         | Unlimited         |
| Lead database     | 450M+ contacts    | No built-in DB    |
| Reply handling    | AI Reply Agent    | Unibox            |
| IP rotation       | Automatic (SISR)  | Manual config     |
| Starting price    | $30/mo            | $39/mo            |
| Best for          | All-in-one        | Deliverability    |
|                   | outbound          | optimization      |
+-------------------+-------------------+-------------------+

Day 14-15: Soft Launch

Launch to Tier 1 prospects only (100-150 contacts)
Monitor deliverability metrics hourly for the first 24 hours
Check inbox placement (use GlockApps or mail-tester.com)
Watch for bounce rates above 2% and pause if triggered
Target: 95%+ delivery rate before expanding volume

Week 4: Optimize (Measure + Iterate)

Day 16-18: A/B Testing Framework

Test one variable at a time:

PRIORITY TEST ORDER:
  1. Subject lines     --> Impact on open rate
  2. Opening lines     --> Impact on reply rate
  3. CTA type          --> Impact on positive reply rate
  4. Send timing       --> Impact on open + reply
  5. Sequence length   --> Impact on total conversion
  6. Personalization   --> Impact on reply sentiment
     depth

Minimum sample size: 100 sends per variant before drawing conclusions.

Day 19-20: Reply Sentiment Analysis

Classify all replies into categories:

POSITIVE (route to human immediately):
  - "Tell me more"
  - "Can you send details?"
  - "Let's set up a call"
  - Meeting booked via CTA

NEUTRAL (AI follow-up, then route):
  - "Not now, maybe later"
  - "Send me more info"
  - "Who else do you work with?"

NEGATIVE (remove from sequence):
  - "Not interested"
  - "Remove me"
  - "Wrong person"

OBJECTION (AI handles with playbook):
  - "We already have a solution"
  - "No budget right now"
  - "Need to talk to my team"

Day 21: ICP Scoring Adjustment

Review first 3 weeks of data and adjust:

Which firmographic traits correlate with positive replies?
Which signals predicted meetings booked?
Which personas converted at the highest rate?
Which Tier 2 prospects should be upgraded or downgraded?

Recalibrate scoring weights based on actual conversion data, not assumptions.

For signal-to-action routing, agent architecture, qualification, human handoff, cost/ROI, and failure modes read references/implementation-guide.md when designing or debugging an AI SDR deployment.

Examples

User says: "Set up an AI SDR" → Result: Agent asks pipeline need, CRM, and budget; recommends platform (11x, Artisan, AiSDR) and 4-week program; outlines 30-second checklist (ICP, enrichment 80%+, 3 email variants, signal-to-action, sending, handoff, CRM, reply classification); sets speed-to-lead (P0 <5 min, reply handoff <5 min).
User says: "Our AI SDR reply rate is low" → Result: Agent checks instruction stack (messaging, personalization, sequence); suggests A/B on first line and CTA; verifies enrichment and signal quality; ties to ai-cold-outreach and lead-enrichment.
User says: "When to use AI SDR vs human SDR?" → Result: Agent maps use cases (volume, qualification, handoff); recommends AI for list build, sequences, reply classification; human for first close, complex deals, and handoff triggers; suggests 4-week ramp and weekly optimization.

Troubleshooting

Low meeting conversion → Cause: Weak qualification or wrong handoff. Fix: Define qualification criteria and handoff triggers; ensure positive-reply-to-handoff <5 min; train on objection handling; review reply sentiment accuracy.
Deliverability issues → Cause: Warmup, volume, or authentication. Fix: Run deliverability checklist (SPF, DKIM, DMARC, unsubscribe, bounce <2%, warmup 14–28d, <50/mailbox); test inbox placement (GlockApps, mail-tester).
Tool swap didn't help → Cause: Instruction stack or context missing. Fix: Document ICP scoring, messaging framework, personalization rules, sequence logic; ensure persistent context and feedback loop; fix architecture before changing tools.

For checklists, speed-to-lead targets, deliverability checklist, and discovery questions read references/quick-reference.md.

Related Skills

ai-cold-outreach - Deep dive on cold email copywriting, deliverability, and multi-channel sequencing
lead-enrichment - Detailed enrichment waterfall design, data provider selection, and Clay workflows
sales-motion-design - End-to-end sales motion architecture from first touch to close
gtm-engineering - Technical GTM infrastructure, API integrations, and workflow automation
solo-founder-gtm - Lean AI SDR deployment for founders doing everything themselves
gtm-metrics - Pipeline metrics, attribution modeling, and ROI tracking frameworks

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tech-leads-club/ai-cold-outreachWhen the user wants to build an AI-powered outreach system, write cold emails, improve deliverability, or scale personalized outreach. Also use when the user mentions 'cold email,' 'cold outreach,' 'outreach automation,' 'Instantly,' 'Smartlead,' 'Clay,' 'email sequences,' 'deliverability,' 'personalization at scale,' 'reply rate,' or 'outreach stack.' This skill covers the complete AI cold outreach system from signal detection through conversion. Do NOT use for technical implementation, code reClose match+26 pts pass-rate lift

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AI SDR Skill

Before Starting

Section 1: AI SDR Landscape (2025-2026)

What AI SDRs Actually Do

Platform Comparison Table

Platform Selection Decision Framework

Key Metrics Benchmarks

Section 2: The 4-Week AI SDR Deployment Program

Week 1: Foundation (Signal Setup + List Building)

Week 2: Content (Sequence Creation + Personalization)

Week 3: Launch (Sending Infrastructure + Go-Live)

Week 4: Optimize (Measure + Iterate)

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