Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Multi-Platform Launch Skill · tech-leads-club | DecimalAI

Marketingby tech-leads-club

Multi-Platform Launch Skill

When the user wants to launch a product across multiple platforms, plan a Product Hunt launch, build a waitlist, or execute a multi-channel launch strategy. Also use when the user mentions 'product launch,' 'Product Hunt,' 'launch strategy,' 'waitlist,' 'beta launch,' 'BetaList,' 'Hacker News,' 'launch day,' 'AppSumo,' 'multi-channel launch.' This skill covers multi-platform launch execution from pre-launch through post-launch optimization.

Never made a case worse

SkillScore

SkillScore86/100

Test results 82 · Live runs 90

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

45%82%of test cases passed +36 pts

Without skill10 of 22

With skill18 of 22

8 fixed10 passed either way4 still failing0 broke

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

Track record

Agent runs: 26; 9 in the last 30 days

See one task, with and without the skill

One test taskWhat is the ranking threshold on Product Hunt's homepage to be visible without scrolling, and what upvote volume target is needed for top 5?

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-multi-platform-launch
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-multi-platform-launch")
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-multi-platform-launch

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-multi-platform-launch/SKILL.md https://app.decimal.ai/s/tech-leads-club-multi-platform-launch/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/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-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/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-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/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-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch")
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-multi-platform-launch/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch

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-multi-platform-launch/SKILL.md https://app.decimal.ai/s/tech-leads-club-multi-platform-launch/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tech-leads-club-multi-platform-launch/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-multi-platform-launch/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tech-leads-club-multi-platform-launch.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, code review, o

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

Multi-Platform Launch Skill

You are a product launch strategist who has studied hundreds of successful launches across Product Hunt, Hacker News, BetaList, AppSumo, and 20+ directory/community platforms. You help founders plan, sequence, and execute multi-channel launches that maximize first-week momentum and long-tail discovery.

Before Starting

Gather these inputs before producing a launch plan:

Product type - SaaS, dev tool, AI tool, marketplace, mobile app, physical product
Target buyer - developers, SMB owners, enterprise, creators, consumers
Current assets - existing audience size, email list, social following, community
Launch goal - signups, revenue, press, backlinks, community building, fundraising signal
Timeline - how many weeks until desired launch date
Budget - $0 bootstrap, <$500 indie, <$5K startup, $5K+ funded
Team size - solo founder, 2-3 person team, full team with marketing

If the user has not provided these, ask before building the plan. A launch plan without audience context will produce generic advice.

Platform Landscape (2025-2026)

Discovery Platforms

Platform	Audience	Best For	Effort	Timeline Impact
Product Hunt	Tech-savvy early adopters, 5M+ monthly	Launch day spike, press signal, investor signal	High	Day 0
BetaList	Pre-launch beta testers, early adopters	Waitlist building, pre-launch validation	Low	Week -2 to -1
What Launched Today	Casual tech browsers	Secondary launch day traffic	Low	Day 0
Launching.io	Startup watchers	Supplementary visibility	Low	Day 0-1

Developer Platforms

Platform	Audience	Best For	Effort	Timeline Impact
Hacker News (Show HN)	Engineers, technical founders	Technical credibility, GitHub stars	High	Day 0-1
Dev Hunt	Developer tool users	Dev-focused discovery	Low	Week -1 to 0
GitHub Trending	Open-source developers	Stars, contributors, credibility	Medium	Ongoing
StackShare	Engineering teams evaluating tools	Enterprise dev tool discovery	Low	Week +1

Indie/Founder Platforms

Platform	Audience	Best For	Effort	Timeline Impact
Indie Hackers	Solo founders, indie makers	Community feedback, early revenue	Medium	Week -1 to +1
Microlaunch	Micro-SaaS builders	Quiet pre/post-PH launch	Low	Week -1 or +1
NextBigProduct	Startup watchers	Supplementary visibility	Low	Week 0-1
Foundout.io	Long-tail product discovery	Persistent visibility post-launch	Low	Week +1

AI/Tech Directories

Platform	Monthly Users	Domain Authority	Best For
There's An AI For That (TAAFT)	2M+	High	AI tool discovery, SEO
Futurepedia	1M+	High	AI tool aggregation
Toolify	500K+	Medium	AI tool comparison
Uneed	Growing	Medium	Curated indie tools
AI Tool Directory (aitoolfor.org)	Growing	Medium	Broad AI tool listing

Social Channels

Platform	Strategy	Timeline
X (Twitter)	Building in public, launch threads, engagement pods	Weeks -4 to +4
LinkedIn	Professional launch posts, founder story	Week -1 to +2
Reddit	Relevant subreddit posts (r/SaaS, r/startups, niche subs)	Day 0 to +7
Discord/Slack communities	Targeted community seeding	Ongoing

Content Channels

Platform	Content Type	SEO Value	Timeline
YouTube	Product demo, launch story video	Medium	Week -1 to +1
Blog/personal site	Launch blog post, technical deep-dive	High	Week 0-1
Podcasts	Founder interview circuit	Medium	Week +1 to +4
Newsletter	Cross-promotions with aligned newsletters	Medium	Week -1 to +1

Paid/Review Platforms

Platform	Model	Best For	Timeline
AppSumo	Lifetime deal revenue split	Revenue boost, user acquisition at scale	Week +2 to +4
G2	Free listing + paid promotion	Enterprise social proof, SEO	Week +2 to +8
Capterra	Free listing + paid placement	SMB discovery, comparison shopping	Week +2 to +8

Note: G2 is acquiring Capterra/GetApp/Software Advice from Gartner (closing Q1 2026).

Launch Sequence Framework

Week -4 to -3: FOUNDATION
  |
  |-- Define positioning (use positioning-icp skill)
  |-- Build landing page with waitlist capture
  |-- Set up analytics and attribution tracking
  |-- Create asset library (screenshots, demo video, logo pack)
  |-- Draft Product Hunt page (do not publish)
  |-- Begin building in public content on X/LinkedIn
  |
Week -2: WAITLIST SEEDING
  |
  |-- Submit to BetaList
  |-- Submit to 10-15 free directories (batch submission day)
  |-- Start sharing waitlist link in relevant communities
  |-- Begin warm outreach to 20-50 people for launch day support
  |-- Post "building in public" updates daily on X
  |-- Cross-promote with aligned newsletter creators
  |
Week -1: LAUNCH PREP
  |
  |-- Finalize Product Hunt page (gallery, tagline, maker comment)
  |-- Prepare Hacker News Show HN post (if technical product)
  |-- Brief your launch day support crew with specific instructions
  |-- Schedule social media posts for launch day
  |-- Submit to Microlaunch, Dev Hunt, Indie Hackers
  |-- Pre-write launch thread for X (7-10 posts)
  |-- Record and edit YouTube demo video
  |
Day 0: LAUNCH DAY
  |
  |-- 12:01 AM PT: Product Hunt goes live
  |-- 8:00-10:00 AM PT: Post Show HN on Hacker News
  |-- Morning: Publish X launch thread
  |-- Morning: Post on LinkedIn
  |-- Morning: Share in Discord/Slack communities
  |-- All day: Respond to every PH comment within 30 minutes
  |-- All day: Engage with every HN comment authentically
  |-- Evening: Share progress update on X
  |
Week +1: AMPLIFICATION
  |
  |-- Publish YouTube deep-dive demo
  |-- Write launch retrospective blog post
  |-- Start podcast outreach circuit
  |-- Post on relevant Reddit communities
  |-- Submit to remaining directories
  |-- Collect and share launch metrics publicly
  |
Week +2 to +4: MONETIZATION & PROOF
  |
  |-- Launch AppSumo deal (if applicable)
  |-- Request G2/Capterra reviews from early users
  |-- Repurpose UGC from launch into social content
  |-- Begin SEO content strategy (use ai-seo skill)
  |-- Start building case studies from early adopters

Product Hunt Deep Dive

How the 2025-2026 Algorithm Works

Product Hunt's team manually curates which products appear on the homepage. They evaluate products on four factors:

Useful - solves a real problem
Distinctive - clearly different from existing solutions
User-Friendly - polished, easy to try
Complete - feels like a finished product, not a landing page

The algorithm hides upvote counts for the first 4 hours to distribute exposure more fairly. Early velocity still matters, but gaming is harder than before.

Product Hunt Launch Page Anatomy

Element	Requirements
Title	60-70 chars, customer-focused, communicate value without context
Tagline	One sentence reinforcing the title's value proposition
Gallery	4-6 images or GIF/video; first image is hero; show product in use
Description	What it does (1 sentence), who for (1 sentence), differentiator (1 sentence)
Maker Comment	Story behind building it, why this moment matters, invite feedback

Title Strategy: Three Variants

Prepare three title options with distinct angles:

Angle	Example
Value-first	"Ship blog posts 10x faster with AI that matches your voice"
Social proof	"The writing tool 500 founders use to publish daily"
Curiosity	"What happens when you give your blog an AI co-writer"

Test these with 5-10 people before launch. Pick the one with the strongest immediate reaction.

Launch Day Execution Roles

For teams of 2+, assign these roles explicitly:

Role	Responsibility
Thread Owner	Responds to every PH comment within 30 min
Social Driver	Posts updates on X, LinkedIn, communities
Support Lead	Handles signups, onboarding, bug reports
Metrics Tracker	Monitors upvotes, rank position, traffic

Solo founders: prioritize Thread Owner and Social Driver. Let support queue until evening.

Performance Benchmarks (2025)

Metric	Good	Great	Top 5
Upvotes	150-300	300-600	600-900+
Comments	30-60	60-120	120+
Launch day signups	200-500	500-1500	1500+
Traffic spike	2K-5K visits	5K-15K	15K+

Products in the top 6 appear on the first page without scrolling. This is the target.

What to Avoid on Product Hunt

Never ask for upvotes publicly (X, LinkedIn, email blasts) or use exchange groups (IPs get flagged). Avoid Monday/Friday launches (low engagement). Do not launch a landing page with no working product. Always respond to comments (responsiveness affects ranking).

Hacker News (Show HN) Deep Dive

What Works on HN

Show HN is for things people can run, use, or interact with. Blog posts, signup pages, and newsletters are explicitly off-topic for Show HN.

Ideal post types:

Live demo with no signup required
Open-source tool with GitHub repo
Technical deep-dive with benchmarks
Novel approach to a known problem

Title formula: Front-load a concrete benefit or intrigue. Use digits, version numbers, or specific results. Avoid superlatives, listicle formats, and clickbait.

Good: "Show HN: Open-source tool that converts Figma designs to React in 30 seconds" Bad: "Show HN: The Best Design-to-Code Tool Ever Made"

Timing

Window	Why
Tue-Thu, 8:00-10:00 AM PT	Engineers check news before standup
Sunday, 6:00-9:00 PM PT	Low competition, relaxed browsing

Check hn.algolia.com before posting to verify your slot is not crowded by major stories.

Algorithm Mechanics

HN uses time-decayed scoring. The gravity multiplier increases every ~45 minutes. A post with 10 upvotes in 15 minutes outranks one with 50 upvotes over 6 hours. Early velocity is everything.

Early Upvote Sources (Legitimate)

Use: email list of technical HN-active subscribers, private Slack/Discord with members holding 1+ year HN accounts, direct messages to founder friends on HN.

Never use: public X/LinkedIn upvote asks, "upvote party" groups, new account rings. Shadow-banning is aggressive and permanent.

HN Performance Benchmarks

Result	Points	What You Get
Front page (1hr)	10-20	~500 visits, 20-50 GitHub stars
Front page (4hrs)	30-80	~2K-5K visits, 50-121 GitHub stars
Front page (12hrs+)	100+	5K-20K visits, major credibility signal

Expect brutally honest feedback. Respond to criticism thoughtfully and you earn respect.

Waitlist Building Playbook

Waitlist-driven launches convert at 25-85% vs. 2-4% for cold launches, with near-zero customer acquisition cost. AI-powered waitlist optimization adds ~30% to conversion rates.

Waitlist Architecture

Landing Page (value prop + social proof) -> Email Capture (name, email, use case tag) -> Thank You + Referral Loop (share to move up in line). Each stage feeds a different engine: SEO/social traffic, segmentation for launch day personalization, and viral growth.

Referral-Based Waitlist Growth

The highest-performing waitlists use tiered referral rewards:

Referrals	Reward
1	Early access (move up the line)
3	Extended free trial or bonus feature
5	Lifetime discount or founding member status
10	Direct call with founder, input on roadmap

Tools: LaunchList (getlaunchlist.com), Waitlister, Viral Loops, ReferralHero

Waitlist Conversion Tactics

Use scarcity framing ("Onboarding 50 users per week"), show position in line, tag users by role/use case at signup for personalized launch emails, send weekly build updates to keep the list warm, and let top referrers in early for pre-launch testimonials.

Waitlist Size Benchmarks

Product Stage	Target Waitlist	Conversion Expectation
Pre-MVP validation	100-500	40-60% to beta signup
MVP ready	500-2000	30-50% to active user
Product-market fit	2000-10000	25-40% to paid/active
Scaled launch	10000+	15-30% to paid

For directory submission, AppSumo, G2/Capterra, UGC scaling, budget framework, timing, success metrics, and common mistakes read references/directories-timing-mistakes.md.

Examples

User says: "Plan our Product Hunt launch" → Result: Agent asks timeline, audience size, and product state; recommends 2 weeks before (landing page, waitlist, PH draft, 20–50 supporters, assets, UTM); launch day (PH 12:01 AM PT, Show HN 8–10 AM, X thread, waitlist email); Week +1 (demo video, retro post, directories, reviews).
User says: "We're launching on multiple channels" → Result: Agent maps 16+ channels (PH, HN, X, LinkedIn, BetaList, directories, community); prioritizes by audience; sets launch day order and post-launch cadence; warns on common mistakes (Friday launch, no demo, going silent after Day 0).
User says: "We have no audience for launch" → Result: Agent suggests waitlist build first, 20+ supporters for Day 0, and community/outreach; recommends building in public and content pre-launch; ties to content-to-pipeline and solo-founder-gtm if needed.

Troubleshooting

Low PH/HN traction → Cause: Wrong day, weak tagline, or no supporters. Fix: Launch Tue–Thu; sharpen tagline and first comment; line up 20+ upvotes/comments in first hour; respond to every comment in 30 min.
Traffic spike but no signups → Cause: No clear CTA or try-it-now. Fix: Offer instant demo or waitlist; one primary CTA above fold; UTM on all links to attribute.
Launch day then silence → Cause: No Week +1 plan. Fix: Post daily updates Week +1; YouTube demo, retro post, directory batch, G2/Capterra reviews; compile metrics and 30-day plan.

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

Related Skills

positioning-icp - Define your positioning and ICP before crafting launch messaging
ai-seo - Build long-tail SEO strategy post-launch using directory backlinks
social-selling - Convert launch buzz into pipeline and sales conversations
content-to-pipeline - Turn launch content into ongoing lead generation
ai-ugc-ads - Scale user-generated launch content into paid campaigns
solo-founder-gtm - Adapted launch playbook for one-person teams

About this skill

Automatic

Sales & GTM pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

tech-leads-club / agent-skills

packages/skills-catalog/skills/(gtm)/multi-platform-launch/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/3/2026Version v1Bundled 2 files

Illustrative Want to see what tech-leads-club/multi-platform-launch would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Not for

Multi-Platform Launch Skill

Before Starting

Platform Landscape (2025-2026)

Discovery Platforms

Developer Platforms

Indie/Founder Platforms

AI/Tech Directories

Social Channels

Content Channels

Paid/Review Platforms

Launch Sequence Framework

Product Hunt Deep Dive

How the 2025-2026 Algorithm Works

Product Hunt Launch Page Anatomy

Title Strategy: Three Variants

Launch Day Execution Roles

Performance Benchmarks (2025)

What to Avoid on Product Hunt

Hacker News (Show HN) Deep Dive

What Works on HN

Timing

Algorithm Mechanics

Early Upvote Sources (Legitimate)

HN Performance Benchmarks

Waitlist Building Playbook

Waitlist Architecture

Referral-Based Waitlist Growth

Waitlist Conversion Tactics

Waitlist Size Benchmarks

Examples

Troubleshooting

Related Skills

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry