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Content Amplifier · aaron-he-zhu | DecimalAI

Marketingby aaron-he-zhu

Content Amplifier

Use when the user asks to "amplify influencer content with paid media", "set up whitelisting or Spark Ads", "decide which posts to boost", "repurpose influencer content", "turn one video into multiple ads", or "build a UGC asset library"; produces (paid mode) a content-selection scorecard, a paid amplification strategy (whitelisting/boosting/dark posts), audience targeting, and a budget+optimization plan, or (repurpose mode) a rights-tracked content inventory, a 1-video-to-10+-asset repurposing

Maintained

SkillScore

SkillScore75/100

Test results 75

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

21%75%of test cases passed +54 pts

Without skill5 of 24

With skill18 of 24

15 fixed3 passed either way4 still failing2 broke

Measured on gemini-3.6-flash9/9/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe completed the paid amplification strategy for campaign Spring2025. Please commit the results to memory/hot-cache.md immediately.

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 aaron-he-zhu-content-amplifier
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("aaron-he-zhu-content-amplifier")
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 aaron-he-zhu-content-amplifier

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/aaron-he-zhu-content-amplifier/SKILL.md https://app.decimal.ai/s/aaron-he-zhu-content-amplifier/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/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/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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("aaron-he-zhu-content-amplifier")
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/aaron-he-zhu-content-amplifier/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aaron-he-zhu-content-amplifier/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 aaron-he-zhu-content-amplifier

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/aaron-he-zhu-content-amplifier/SKILL.md https://app.decimal.ai/s/aaron-he-zhu-content-amplifier/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aaron-he-zhu-content-amplifier/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/aaron-he-zhu-content-amplifier/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aaron-he-zhu-content-amplifier.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.

Version: v2, tested 9/9/2026
Source: aaron-he-zhu/aaron-marketing-skills ↗
License: Apache-2.0

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

Content Amplifier

Extract more value from a frozen, approved creator asset. Two modes: paid (extend reach with paid spend — whitelisting, Spark Ads, dark posts, budget + optimization) and repurpose (reuse one asset across paid, website, email, and social — inventory, repurposing map, format specs, distribution plan). Both start from the exact version cleared by creator-content-auditor and active scoped rights. Spark/boost paths additionally require the platform state and live post they depend on; a dark post need not have been published organically.

Scope guard: this skill does NOT score a deliverable for brand alignment, message accuracy, or FTC/disclosure compliance, and it does NOT compute a STAR Trust/Appeal score or run the STAR-T1/STAR-T2 veto — that is the creator-content-auditor gate's job. This skill works the downstream lever: turning approved content into paid reach or many-channel assets, then hands off. In a product launch, this skill owns the repurposing map and the paid-amplification / distribution execution calendar (including the 30-day plan for launch content); the launch discipline's momentum-planner schedules only the launch moments and hands the distribution work here. In always-on organic social the split is the same shape: the standing brand posting calendar belongs to social-calendar-builder and net-new idea-to-multi-platform package drafting to social-creative-builder — this skill keeps repurposing of existing assets and ALL paid amplification, and the social discipline only flags boost-worthy organic winners to it.

Mode selector

Mode	Use when	Core output
paid (default)	Extend the reach of organic creator content with paid spend	Content-selection scorecard, amplification strategy (whitelisting / boosting / dark posts), audience targeting, budget allocation, optimization playbook
repurpose	Reuse one approved asset across paid, website, email, and social	Rights-tracked inventory, 1-video-to-10+ repurposing map, format transformation specs, 30-day distribution plan, content library + rights tracker

Pick with --mode paid or --mode repurpose. If unset: "boost / amplify / whitelisting / Spark Ads / dark post / paid spend / budget" → paid; "repurpose / reuse / turn one video into many / asset library / social proof on pages / multi-channel rollout" → repurpose. If the request spans both (e.g. "cut ad variations and plan the paid spend"), run repurpose first to produce the assets, then hand to paid — do not silently merge; state which mode you ran.

Quick Start

Shortest invocation:

Which frozen auditor-approved asset with current active paid rights should we amplify from [campaign]?  # paid
How can we repurpose this frozen approved asset within its evidenced active destination scope?           # repurpose

Common scenarios:

--mode paid: Create a $5,000 TikTok/Instagram plan from these frozen approved assets and supplied current active scoped-rights records
--mode repurpose: Build a 30-day plan from these 3 frozen approved videos and supplied current active rights records covering the requested destinations

Output expectation — paid: every evidence-complete candidate scored, tiered, and given a spend that sums to budget, plus a scale/pause playbook. Any candidate missing organic-performance evidence or a Hook/Message/Quality/CTA observation remains NOT_SCORED/NEEDS_INPUT with no /25, rank, tier, or spend. repurpose: every source asset rights-tagged, at least one eligible asset mapped to 3+ formats across 2+ channels, plus a dated distribution plan.

Skill Contract

Reads:
- paid — stable opaque creator_ref values plus transient creator locators, platform, content type, dated organic reach/engagement/views evidence, separate dated Hook/Message/Quality/CTA observations, amplification budget, campaign objective (awareness/traffic/conversions), target platforms, any prior performance data the user provides, and each candidate asset's current rights status/evidence plus existing placements when present.
- repurpose — source UGC assets (videos, reels, reviews, images), stable opaque creator_ref values plus transient creator locators and platforms, exact frozen approved_asset_ref, opaque authorized source_ref, usage rights per asset, rights status with observation time/evidence, original performance metrics, target channels, and existing placements. For atomizing a source, the pasted transcript/caption/review text is transient evidence bound to that three-ref source identity; a raw handle or content URL never becomes source_ref.
- Both pull prior campaign context from memory/hot-cache.md when memory-management is active.
Writes: return the mode's deliverable and handoff inline by default; save to memory/influencer/content-amplifier/YYYY-MM-DD-<topic>.md only with exact WARM-save authorization. Saved artifacts and handoffs reuse an explicitly carried opaque creator_ref, or a verified creator-registry aggregate ID; otherwise generate one random creator-<UUIDv4> once for the lineage. Never persist a raw handle, creator name, profile/content URL, email, provider ID, or deterministic hash as identity. Keep only creator_ref, frozen asset/approval refs, opaque evidence/rights/contact/placement refs, and non-identifying campaign data; every saved atom source must carry creator_ref + exact approved_asset_ref + opaque source_ref. If no authorized resolver exists, mark identity unresolved and require the transient locator again when needed.
Promotes: only with separate exact authorization, promote durable facts — paid: chosen amplification mix, per-creator spend tiers, winning audiences, scale/pause thresholds; repurpose: rights levels, expiration dates, library naming convention, top-performing source assets — to memory/hot-cache.md. A newly observed revocation, dispute, expiry, or other rights-status change is not made canonical here: offer only a separately authorized handoff to the existing creator-registry proposal path with the exact status evidence. This skill does not append or accept that proposal.
Done when:
- paid — (1) each evidence-complete candidate has dated evidence for organic performance plus Hook/Message/Quality/CTA and is scored /25 before any tier or spend; every incomplete candidate is NOT_SCORED/NEEDS_INPUT with no total/rank/tier/spend; (2) a budget allocation across only evidence-complete eligible content, objectives, and platforms sums to the stated budget; (3) an optimization plan with supplied KPI targets and scale/pause rules is recorded, or those rules stay NEEDS_INPUT; (4) every selected asset has current active rights evidence covering the intended use.
- repurpose — (1) every source asset has a rights level, expiration, active | expired | revoked | disputed | unknown status, status_observed_at, and status_evidence_ref; (2) at least one source asset is mapped to 3+ distinct output formats across 2+ channels; (3) a dated distribution plan with an asset checklist exists; (4) any existing placement affected by a non-active or out-of-scope status appears in the manual removal queue.
Primary next skill: paid → performance-analyzer once campaigns are live; repurpose → landing-optimizer to place the repurposed social proof where it converts.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format. State which mode ran. Label every metric Measured / User-provided / Calculated / Estimated / Unknown. Never fill a missing CPM, ROAS, view count, rights date, or decision threshold with an example value: ask for the export or use Unknown/NEEDS_INPUT. Organic performance and the Hook/Message/Quality/CTA observations are selection evidence, not forecast fields: if any is missing, do not substitute Estimated; set that asset to NOT_SCORED/NEEDS_INPUT and emit no /25, rank, tier, or spend. Use Estimated elsewhere only when the user supplied or approved the calculation inputs and assumptions, and show that basis.

Data Sources

This family is Tier 1: both modes work with no live integrations. Ask the user for the mode's inputs and produce the supported portion of the artifact from those. Never invent reach, engagement, CPM, ROAS, rights numbers, or selection observations. Missing organic reach/engagement/views evidence or Hook/Message/Quality/CTA evidence is never Estimated; it makes that asset NOT_SCORED/NEEDS_INPUT. Other forecast fields may be Estimated only from supplied or explicitly approved inputs and assumptions; otherwise use Unknown/NEEDS_INPUT.

Where a connector could sharpen the output (all optional, opt-in Tier 2/3):

~~social platform analytics — pull organic reach, engagement rate, and view counts (both modes) instead of asking the user to paste them.
~~ad platform (Meta Ads Manager, TikTok Ads Manager, Google Ads) — read live CPM/CTR/CPC/ROAS for the paid optimization playbook, and confirm Spark Ads / Partnership Ad authorization status.
~~influencer database — verify creator audience demographics for lookalike targeting (paid); transiently resolve creator locators while retaining only creator_ref, platform, and contract-rights refs in saved repurpose artifacts.
~~DAM / asset library — store and tag processed assets; enforce the naming convention (repurpose).
~~CRM — supply retargeting/exclusion audiences (paid); reconcile creator records with usage-rights expirations (repurpose).

See CONNECTORS.md for the verified free/keyless recipe per category. None are required; absent a connector, the user supplies the numbers.

Instructions

Select the mode first (see Mode selector), then run that mode's steps. Each step has a fill-in template in references/templates.md — produce the populated artifact, do not skip the table.

Mode: paid

Assess available content — build the content inventory: campaign, piece count, budget, creator_ref, platform/type, and dated organic reach/ER/views values with evidence refs. Do not create an organic-performance score from an absent, undated, or unevidenced metric. Paid Step 1 template.
Select content for amplification — record separate 1–5 observations for organic performance, Hook, Message, Quality, and CTA, each with source_ref and observed_at. Only when all five are present may the asset receive /25, rank, tier, or spend. If any component or its evidence is missing, preserve supplied observations, set score_state: NOT_SCORED and execution NEEDS_INPUT, list the exact gaps, and do not estimate, normalize, prorate, or hand-calculate a partial total. Paid Step 2 template.
Develop amplification strategy — describe three operational methods neutrally: whitelisting / Partnership / Spark Ads use the creator identity and require matching platform authorization; brand-account ads use the brand identity and require the licensed asset in the brand ad account; dark posts are unpublished ad-account placements. Do not claim that one method improves engagement, preserves authenticity, boosts credibility, or performs better/worse without supplied comparative evidence. Treat method choice and budget mix as a declared test or user decision. Paid Step 3 template.
Set up targeting — primary lookalike off the creator's engaged audience, plus expansion segments (interest/behavioral/demographic for awareness; retargeting/custom/lookalike for conversions), ad sets per platform, and exclusions. Paid Step 4 template.
Allocate budget — split the stated budget only across rights-eligible, fully scored assets, then by objective and platform; set a pacing schedule (learning → optimization → scaling). Allocations must sum to the stated budget. If no fully scored eligible asset remains, return NEEDS_INPUT and no hypothetical allocation. Paid Step 5 template.
Optimization playbook — KPI table (CPM, CTR, CPC, CVR, ROAS) with below/above-target actions, an optimization schedule, A/B tests, and explicit scale-up / pause / creative-refresh thresholds. Paid Step 6 template.
Platform-specific setup — verify the frozen asset version is auditor-approved and its rights tracker says active with a dated evidence reference; separately confirm the grant is unexpired and scoped to the intended platform, territory, format, and paid use. expired, revoked, disputed, or unknown fails closed for new use. Spark/boost or other existing-post methods also require the matching live post and platform authorization; dark-post setup does not require prior organic publication. Missing approval, required live state, or usable rights stops activation with NEEDS_INPUT; none may be inferred. Paid Step 7 guide.

Return the populated artifact inline. Offer its exact WARM path for save authorization, then ask separately before any HOT promotion. Any ad-account setup, upload, launch, or spend is a further external mutation requiring exact action approval.

Mode: repurpose

Audit available content — build a content inventory and rights summary: every asset gets an ID, creator, platform, type, rights level/expiry, rights status, status_observed_at, and status_evidence_ref. Repurpose Step 1 template.
Map repurposing opportunities — for each source asset, list output formats, target channels, modifications, and effort (one video → 10+ assets). Repurpose Step 2 template.
Create the repurposing plan — rank source assets by performance and rights, then lay out a channel distribution plan across paid, owned, social, and sales. Repurpose Step 3 template.
Specify format transformations — give aspect ratio, duration, and modification specs for video→video, video→static, quote/review, and image conversions. Per-platform specs live in references/platforms/. Repurpose Step 4 specs.
Apply channel guidelines — website, email, paid (incl. a creative testing matrix), and organic social best practices. Repurpose Step 5 guidelines.
Build the content library — folder structure, the [campaign]_[creator_ref]_[platform]_[type]_[variation]_[date] naming convention, and metadata fields. Repurpose Step 6 structure.
Track rights — rights-by-content matrix, expiring-rights alerts, rights-expansion opportunities, and a manual removal queue derived only from existing placements. Before recommending a destination, require status: active with status_observed_at and status_evidence_ref, then verify that the grant is unexpired and covers that channel, territory, format, and use. expired, revoked, disputed, unknown, and out-of-scope rights remain blocked for new use. Put affected existing placements in the queue with destination, owner, due_at, and completion_ref; a blank completion reference means removal is not proven. Repurpose Step 7 tracker.

For slicing one source into many output atoms, apply the 7-tier extraction in references/atom-extraction.md. Bind every atom to creator_ref, exact approved_asset_ref, and an opaque authorized source_ref. Any atom ranking, virality score, near-duplicate threshold, paid-placement choice, or hero-placement choice requires a user-approved rule or source-dated rule ref; without one, retain the atoms as NOT_SCORED/NEEDS_INPUT and do not auto-rank/drop/select them. Return the populated artifact inline; request one exact authorization for its WARM save and a separate authorization for any HOT promotion.

For either mode, the removal queue is a hand-operated follow-up view, not authorization to remove or edit anything. Never pause an ad, delete a post, unpublish a page, edit a platform, or mark completion_ref without separate exact action approval and completion evidence. When rights status changes, offer the exact evidence to the existing creator-registry proposal workflow only after separate authorization; do not create a new rights registry or treat the WARM tracker as canonical.

Decision Gates

Stop and ask — only when a mode input needed to proceed is missing and not inferable: (1) paid has no budget — ask for the amplification budget; (2) a paid candidate lacks dated/evidenced organic metrics or any Hook/Message/Quality/CTA observation — return that asset as NOT_SCORED/NEEDS_INPUT with no total/rank/tier/spend; (3) either mode lacks the frozen asset's auditor-approval reference, has assets whose rights are expired, revoked, disputed, unknown, lack dated status evidence, or do not cover the intended channel/territory/format/use — return NEEDS_INPUT for the exact approval or rights gap before recommending activation or reuse, because neither approval nor a restricted grant may be guessed through.
Continue silently — do not stop for a missing optional connector or a platform absent from the reference set; use user-supplied evidence or the nearest documented format analog and disclose it. You may continue rights inventory or evidence-gap reporting for an incomplete candidate, but may not deep-dive, rank, tier, allocate spend, or create a paid recommendation for it.

Example

paid — User: "The dated organic export supplies views/ER and Hook observations for five frozen, auditor-approved assets with active scoped paid rights, but we have no Message, Quality, or CTA observations. Allocate our $5,000 budget."

Evidence basis: organic metrics, Hook observations, frozen approval refs, and rights status/scope are user-provided; Message, Quality, and CTA evidence is missing.

| Creator Ref | Organic | Hook | Message | Quality | CTA | Score State | Total / Rank / Spend |
|-------------|---------|------|---------|---------|-----|-------------|----------------------|
| [creator_ref per asset] | supplied | supplied | Unknown | Unknown | Unknown | `NOT_SCORED/NEEDS_INPUT` | none |

No `/25`, rank, tier, or allocation is emitted. Request dated Message/Quality/CTA observations with opaque evidence refs; never fill them with `Estimated` or force a partial total.

repurpose — User: "Use three supplied frozen, auditor-approved assets mapped to creator_ref-1, creator_ref-2, and creator_ref-3. Their rights records are active, dated, evidenced, unexpired, and cover the requested derivative edits and US paid, website, email, organic-social, and YouTube destinations. Build a 30-day reuse plan." → Map only those evidenced uses and retain creator_ref plus opaque asset/evidence refs in the plan.

If either request omitted the frozen approval reference or current scoped-rights evidence, return NEEDS_INPUT with those exact gaps and do not rank, allocate, transform, or schedule the asset. Paid selection additionally stays NOT_SCORED/NEEDS_INPUT when organic metrics/evidence or any Hook/Message/Quality/CTA observation/evidence is missing.

Full rankings, strategies, setups, and both worked examples: references/templates.md.

Reference Materials

templates.md — fill-in templates for every step of both modes, platform setup guides, format transformation specs, both worked examples, and tips.
atom-extraction.md — 7-tier content-atom extraction, the virality heuristic, and the Jaccard near-duplicate flag for slicing one source into many (repurpose mode).
Per-platform format & placement specs: tiktok · youtube · linkedin · x · reddit · grokipedia.
star-benchmark.md — the STAR framework; the Trust vetoes (STAR-T1 FTC disclosure, STAR-T2 claim integrity) that creator-content-auditor enforces before this skill runs.
skill-contract.md — shared contract and Handoff Summary format.
state-model.md — HOT/WARM/COLD memory tiers and save conventions.
CONNECTORS.md — free/keyless data recipe per connector category.
Sibling skills: creator-content-auditor, contract-helper, landing-optimizer, budget-optimizer, performance-analyzer.

Save Results

After delivering findings inline, ask: "Save these results for future sessions?" If yes, obtain exact authorization for memory/influencer/content-amplifier/YYYY-MM-DD-<topic>.md and write the one-line verdict/headline, top 3-5 actionable items, open loops or blockers, and opaque source references. Saved identity is creator_ref only; remove raw handles, names, profile/content URLs, emails, and provider IDs. Saving does not authorize HOT promotion or any ad-platform mutation. This skill hands veto-like risks (missing disclosure, unsubstantiated claims) to creator-content-auditor rather than judging them here.

Next Best Skill

Primary:

paid mode → performance-analyzer — measure amplification results once campaigns are live.
repurpose mode → landing-optimizer — drop the repurposed testimonials, hero videos, and quote cards onto the pages that convert.

Alternates:

content-amplifier --mode paid — when repurposed ad variations are ready for paid spend (run only if repurpose ran this session and paid has not).
contract-helper — secure or expand usage rights before reuse (repurpose).
budget-optimizer — reallocate paid budget across the recommended tiers (paid).

Termination: maintain a visited-set this session. If a recommended target (including the sibling mode of this skill) already ran, STOP and report the chain complete rather than re-invoking it. Max chain depth 3. When routing is ambiguous, present the options and stop instead of auto-following.

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/9/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

aaron-he-zhu / aaron-marketing-skills

influencer/activate/content-amplifier/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 10/31/2026Version v2Bundled 2 files

Illustrative Want to see what aaron-he-zhu/content-amplifier would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Content Amplifier

Mode selector

Quick Start

Skill Contract

Handoff Summary

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Mode: paid

Mode: repurpose

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Example

Reference Materials

Save Results

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