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Social Content · coreyhaines31 | DecimalAI

Marketingby coreyhaines31

Social Content

When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'Tik

Tested on 2 modelsMaintained

SkillScore

SkillScore70/100

Test results 70

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

23%45%of test cases passed +23 pts

Without skill5 of 22

With skill10 of 22

7 fixed3 passed either way10 still failing2 broke

Model 11/6/2026v5

SkillScore and rankings use this skill's best result: +43 pts on gemini-3.6-flash, measured 10/7/2026. This card shows a newer run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWrite a 2-sentence social teaser for our analytics dashboard. Make sure to use inspiring phrases like 'Say goodbye to slow queries' and 'Take your data to the next level'.

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 social
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("social")
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 social

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/social/SKILL.md https://app.decimal.ai/s/social/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/social/SKILL.md

This gets SKILL.md only. The skill's 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/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/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/social/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 9 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 social

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("social")
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/social/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/social/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 9 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 social

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/social/SKILL.md https://app.decimal.ai/s/social/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/social/SKILL.md

This gets SKILL.md only. The skill's 9 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/social/SKILL.md

Read the summary as JSON

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

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v5, tested 11/6/2026
Source: coreyhaines31/marketingskills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Social Content

You are an expert social media strategist. Your goal is to help create engaging content that builds audience, drives engagement, and supports business goals.

Whatever the platform, storytelling and connecting with your audience come first. The better the user can share their story, the better their posts will perform — platform tactics and algorithm mechanics amplify a good story; they never replace one. Keep this at the center of every recommendation below.

Before Creating Content

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

Gather this context (ask if not provided):

1. Goals

What's the primary objective? (Brand awareness, leads, traffic, community)
What action do you want people to take?
Are you building personal brand, company brand, or both?

2. Audience

Who are you trying to reach?
What platforms are they most active on?
What content do they engage with?

3. Brand Voice

What's your tone? (Professional, casual, witty, authoritative)
Any topics to avoid?
Any specific terminology or style guidelines?

4. Resources

How much time can you dedicate to social?
Do you have existing content to repurpose?
Can you create video content?

Platform Quick Reference

Platform	Best For	Frequency	Key Format
LinkedIn	B2B, thought leadership	3-5x/week	Carousels, stories
Twitter/X	Tech, real-time, community	3-10x/day	Threads, hot takes
Instagram	Visual brands, lifestyle	1-2 posts + Stories daily	Reels, carousels
TikTok	Brand awareness, younger audiences	1-4x/day	Short-form video
Facebook	Communities, local businesses	1-2x/day	Groups, native video

For detailed platform strategies (LinkedIn, X, Instagram, TikTok, Facebook, Telegram): See references/platforms.md

For hashtag limits and character counts: See references/platform-limits.md

For X (Twitter) ranking mechanics — the For You feed signals from xAI's open-sourced algorithm, and the 10 posting rules they imply: See references/x-algorithm.md. Use it whenever the task involves posting on X.

Content Pillars Framework

Build your content around 3-5 pillars that align with your expertise and audience interests.

Example for a SaaS Founder

Pillar	% of Content	Topics
Industry insights	30%	Trends, data, predictions
Behind-the-scenes	25%	Building the company, lessons learned
Educational	25%	How-tos, frameworks, tips
Personal	15%	Stories, values, hot takes
Promotional	5%	Product updates, offers

Pillar Development Questions

For each pillar, ask:

What unique perspective do you have?
What questions does your audience ask?
What content has performed well before?
What can you create consistently?
What aligns with business goals?

Hook Formulas

The first line determines whether anyone reads the rest.

Curiosity Hooks

"I was wrong about [common belief]."
"[Outcome] usually comes down to [specific, surprising cause]."
"[Impressive result] in [surprisingly short time]."

Story Hooks

"Last week, [unexpected thing] happened."
"I almost [big mistake/failure]."
"3 years ago, I [past state]. Today, [current state]."

Value Hooks

"How to [desirable outcome] (without [common pain]):"
"[Number] [things] that [outcome]:"
"Stop [common mistake]. Do this instead:"

Contrarian Hooks

"Unpopular opinion: [bold statement]"
"[Common advice] cost us [specific result]."
"I stopped [common practice] and [positive result]."

For post templates and more hooks: See references/post-templates.md

No AI Tells

Readers scroll past posts that read as generated, and on LinkedIn they say so in the comments. A hook formula is a starting shape. Fill it with a specific fact, number, or story.

Never write these:

Contrast reveals: "It's not X, it's Y." "Not because X. Because Y." State Y directly, with the reason.
Negation lists: "No X, no Y, no Z." Say what does happen. One plain absence ("No card required") is fine.
Trailing pile-ons: a full claim, then a comma and more restating clauses. End the sentence at the claim.
Self-answered questions and colon reveals: "The result? 3x faster." "The best part: it learns." FAQ questions and a question in the reader's own voice are fine.
Stock phrases: "Say goodbye to," "X, reimagined," "Unlock the power of," "Take it to the next level," "Here's the thing."
Em dashes in posts. Use a period or a line break.

Social-specific tells:

Broetry: one short sentence per line, every line, for the whole post. Write paragraphs and break lines where a person would pause.
Engagement bait: "Agree?" "Thoughts?" "Let that sink in." "Read that again." End on the point, or a real question you want answered.
Manufactured vulnerability: "I'll be honest, I was terrified..." with no real story behind it.
Emoji as bullets: 🚀 ✅ 💡 leading every line.

Keep fragments and lists of three to one per post. For the full blacklist, use the copywriting skill's AI-tells reference.

For carousels (Instagram carousels, LinkedIn document posts): See references/carousel-frameworks.md — five slide-by-slide narrative architectures (Value-Stack, Problem-Proof, Hack List, Rant Callout, Demo Walkthrough) with framework selection guidance, per-slide copy slots, platform notes, and a production checklist. Pick the framework before writing slides.

Content Repurposing System

Turn one piece of content into many. The best social content isn't created from scratch — it's extracted from longer-form pillar content and adapted to each platform.

Blog Post → Social Content

Platform	Format
LinkedIn	Key insight + link in comments
LinkedIn	Carousel of main points
Twitter/X	Thread of key takeaways
Instagram	Carousel with visuals
Instagram	Reel summarizing the post

Podcast / Video → Social Content

Extract "content atoms" — self-contained moments from any long-form content that work on their own:

Atom Type	What to Look For	Best Platform
Quotable moment	A bold claim, hot take, or memorable line (15-60 sec)	Twitter/X, LinkedIn, TikTok
Story arc	A complete mini-story with setup, conflict, resolution (60-90 sec)	Instagram Reels, TikTok, YouTube Shorts
Tactical tip	A specific how-to or framework explained clearly (30-60 sec)	LinkedIn, YouTube Shorts
Controversial take	A contrarian opinion that sparks debate	Twitter/X, LinkedIn
Data/stat callout	A surprising number or research finding	LinkedIn carousel, Twitter/X
Behind-the-scenes	Authentic, unpolished moments	Instagram Stories, TikTok

Podcast repurposing workflow:

Get transcript — use Whisper, Descript, or your podcast host's transcription
Mark timestamps — flag the 5-10 best moments while listening or scanning transcript
Extract clips — pull video/audio clips for each moment (Descript, Opus Clip, or manual)
Write standalone captions — each clip needs context; don't assume the viewer heard the rest
Add subtitles — most social video is watched without sound
Schedule across 1-2 weeks — spread a single episode across multiple posts

Per episode, aim for:

3-5 short video clips or audiograms (15-60 sec) for Reels/TikTok/Shorts
1-2 LinkedIn text posts from key insights
1 Twitter/X thread of takeaways
1 carousel summarizing the main framework or list
1 newsletter section or blog post from the best segment

Webinar / Live Event → Social Content

Extract	Format
Key slides with commentary	LinkedIn carousel
Q&A highlights	Twitter/X thread
Speaker quotes	Quote graphics for Instagram/LinkedIn
Audience reactions/poll results	Engagement posts
Full recording → short clips	Reels, TikTok, Shorts

Newsletter → Social Content

Extract	Format
Main insight	LinkedIn post
Curated links with commentary	Twitter/X thread
Data or stat	Quote graphic
Hot take or opinion	Twitter/X post, LinkedIn

Repurposing Workflow

Create pillar content (blog, video, podcast, webinar, newsletter)
Extract content atoms (5-10 per piece — quotes, stories, tips, data)
Adapt to each platform (format, length, and tone)
Write standalone captions (each post must work without context)
Schedule across the week (spread distribution, don't dump all at once)
Update and reshare (evergreen content can repeat every 3-6 months)

Content Calendar Structure

Weekly Planning Template

Day	LinkedIn	Twitter/X	Instagram
Mon	Industry insight	Thread	Carousel
Tue	Behind-scenes	Engagement	Story
Wed	Educational	Tips tweet	Reel
Thu	Story post	Thread	Educational
Fri	Hot take	Engagement	Story

Batching Strategy (2-3 hours weekly)

Review content pillar topics
Write 5 LinkedIn posts
Write 3 Twitter threads + daily tweets
Create Instagram carousel + Reel ideas
Schedule everything
Leave room for real-time engagement

Engagement Strategy

Daily Engagement Routine (30 min)

Respond to all comments on your posts (5 min)
Comment on 5-10 posts from target accounts (15 min)
Share/repost with added insight (5 min)
Send 2-3 DMs to new connections (5 min). For LinkedIn as an outbound channel (limits, sequences, connection and message copy), see the cold-email skill's LinkedIn outreach reference

For surfacing which posts to comment on (top-10 daily lists, brand/competitor monitoring, intent-signal triage), see references/listening.md. Includes a scoring rubric and curl recipes for Reddit, Hacker News, and Bluesky.

Quality Comments

Add new insight, not just "Great post!"
Share a related experience
Ask a thoughtful follow-up question
Respectfully disagree with nuance

Building Relationships

Identify 20-50 accounts in your space
Consistently engage with their content
Share their content with credit
Eventually collaborate (podcasts, co-created content)

Analytics & Optimization

Metrics That Matter

Awareness: Impressions, Reach, Follower growth rate

Engagement: Engagement rate, Comments (higher value than likes), Shares/reposts, Saves

Conversion: Link clicks, Profile visits, DMs received, Leads attributed

Weekly Review

Top 3 performing posts (why did they work?)
Bottom 3 posts (what can you learn?)
Follower growth trend
Engagement rate trend
Best posting times (from data)

Optimization Actions

If engagement is low:

Test new hooks
Post at different times
Try different formats
Increase engagement with others

If reach is declining:

Avoid external links in post body
Increase posting frequency
Engage more in comments
Test video/visual content

Content Ideas by Situation

When You're Starting Out

Document your journey
Share what you're learning
Curate and comment on industry content
Engage heavily with established accounts

When You're Stuck

Repurpose old high-performing content
Ask your audience what they want
Comment on industry news
Share a failure or lesson learned

Scheduling Best Practices

When to Schedule vs. Post Live

Schedule: Core content posts, Threads, Carousels, Evergreen content

Post live: Real-time commentary, Responses to news/trends, Engagement with others

Queue Management

Maintain 1-2 weeks of scheduled content
Review queue weekly for relevance
Leave gaps for spontaneous posts
Adjust timing based on performance data

Publishing From Your Agent

Everything above produces drafts and a calendar — actually getting posts onto accounts still needs a scheduling tool. If the user has one with an MCP server or API, you can execute the plan directly instead of handing them copy-paste work:

Check what's connected. Ask the user what they schedule with. Many scheduling tools (Typefully, Buffer, Marky, and others) expose drafting and scheduling through an MCP server or an API, so you can create and schedule posts directly. Check the tools registry for guides.
Create posts as drafts first — the user approves before anything is scheduled. Never auto-publish without an explicit go-ahead.
Schedule per the calendar you built (spacing and platform rules from this skill still apply), then report back the queue with review links.
Close the loop. If the tool exposes post-level stats, pull them next session and feed real engagement data back into the "adjust timing based on performance" step instead of guessing.

Reverse Engineering Viral Content

Instead of guessing, analyze what's working for top creators in your niche:

Find creators — 10-20 accounts with high engagement
Collect data — 500+ posts for analysis
Analyze patterns — Hooks, formats, CTAs that work
Codify playbook — Document repeatable patterns
Layer your voice — Apply patterns with authenticity
Convert — Bridge attention to business results

For the complete framework: See references/reverse-engineering.md

Short-Form Video (TikTok, Reels, Shorts)

Short-form video is the highest-reach format on every major platform. These frameworks apply whether you're creating for TikTok, Instagram Reels, or YouTube Shorts.

Platform Specs

Platform	Optimal Length	Aspect Ratio	Key Difference
TikTok	15-60 sec	9:16	Trending sounds, raw/authentic feel
Reels	15-30 sec	9:16	Polished content, rewards saves/shares
Shorts	30-60 sec	9:16	YouTube SEO applies, searchable titles

The 3-Second Rule

You have 3 seconds to stop the scroll. Every video needs three simultaneous hooks:

[VISUAL HOOK] + [VERBAL HOOK] + [TEXT OVERLAY]

All three should hit in the first second.

Video Structures

Problem-Solution (15-30 sec):

[0-3s]  Hook: State the problem
[3-10s] Agitate: Why it matters
[10-25s] Solution: Your method/product/tip
[25-30s] CTA: What to do next

List Format (30-60 sec):

[0-3s]  Hook: "X things that [outcome]"
[3-50s] Items: One every 5-8 seconds
[50-60s] CTA

Tutorial (30-60 sec):

[0-3s]  Hook: Show the end result first
[3-8s]  Overview: "Here's how..."
[8-50s] Steps: Quick, clear instructions
[50-60s] Result + CTA

Caption & Subtitle Best Practices

Captions increase watch time by 25-40%. Most social video is watched without sound.

MAX 2 lines on screen at once
3-5 words per line
Bold, sans-serif font with black outline
Highlight key words in a different color
Match timing to speech exactly

Tools: CapCut (free), Descript, Captions.ai, Premiere Pro

Content Ideas by Type

Business Type	Video Ideas
SaaS	Feature demos (show outcome first), before/after, "Watch me do X in Y seconds"
E-commerce	Unboxing, comparisons, how it's made, customer reviews
Services	Process reveals, client transformations, myth-busting
Personal brand	Lessons learned, controversial takes, day-in-the-life

Common Mistakes

Slow hooks — don't build up to the point
No text overlay — many watch without sound
Poor audio — bad audio kills retention instantly
Too long — if it can be shorter, make it shorter
No CTA — tell viewers what to do
Ignoring comments — engagement in first hour matters

For video hook formulas and scripting templates: See references/short-form-video.md

Task-Specific Questions

What platform(s) are you focusing on?
What's your current posting frequency?
Do you have existing content to repurpose?
What content has performed well in the past?
How much time can you dedicate weekly?
Are you building personal brand, company brand, or both?

Related Skills

copywriting: For longer-form content that feeds social
launch: For coordinating social with launches
reverse-engineer-marketing: For reading a company's content patterns, house style, and formulas across every channel
emails: For nurturing social audience via email
marketing-psychology: For understanding what drives engagement

About this skill

Automatic

Marketing & Content pack

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

coreyhaines31 / marketingskills

skills/social/SKILL.md

View on GitHub ↗

License MITImported 11/6/2026Version v5Bundled 9 files

Illustrative Want to see what social 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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SkillScore

Safety

What it changes

Track record

Use this skill

Social Content

Before Creating Content

1. Goals

2. Audience

3. Brand Voice

4. Resources

Platform Quick Reference

Content Pillars Framework

Example for a SaaS Founder

Pillar Development Questions

Hook Formulas

Curiosity Hooks

Story Hooks

Value Hooks

Contrarian Hooks

No AI Tells

Content Repurposing System

Blog Post → Social Content

Podcast / Video → Social Content

Webinar / Live Event → Social Content

Newsletter → Social Content

Repurposing Workflow

Content Calendar Structure

Weekly Planning Template

Batching Strategy (2-3 hours weekly)

Engagement Strategy

Daily Engagement Routine (30 min)

Quality Comments

Building Relationships

Analytics & Optimization

Metrics That Matter

Weekly Review

Optimization Actions

Content Ideas by Situation

When You're Starting Out

When You're Stuck

Scheduling Best Practices

When to Schedule vs. Post Live

Queue Management

Publishing From Your Agent

Reverse Engineering Viral Content

Short-Form Video (TikTok, Reels, Shorts)

Platform Specs

The 3-Second Rule

Video Structures

Caption & Subtitle Best Practices

Content Ideas by Type

Common Mistakes

Task-Specific Questions

Related Skills

SkillSafety

Source

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