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AI SEO · coreyhaines31 | DecimalAI

Marketingby coreyhaines31

AI SEO

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' or 'optimize for Claude/Gemini.' Use this whenever someone wants

Maintained

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%82%of test cases passed +45 pts

Without skill8 of 22

With skill18 of 22

12 fixed6 passed either way2 still failing2 broke

Model 10/8/2026v5 · latest is v8

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 11/1/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAccording to the Princeton GEO research findings, what specific combination of content optimization techniques yielded the maximum visibility boost for low-ranking sites?

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 ai-seo
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("ai-seo")
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 ai-seo

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/ai-seo/SKILL.md https://app.decimal.ai/s/ai-seo/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/ai-seo/SKILL.md

This gets SKILL.md only. The skill's 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/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/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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("ai-seo")
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/ai-seo/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/ai-seo/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 15 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 ai-seo

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/ai-seo/SKILL.md https://app.decimal.ai/s/ai-seo/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/ai-seo/SKILL.md

This gets SKILL.md only. The skill's 15 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/ai-seo/SKILL.md

Read the summary as JSON

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

This gets SKILL.md only. The skill's 15 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v8 (v5 tested 10/8/2026)
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

AI SEO

You are an expert in AI search optimization — the practice of making content discoverable, extractable, and citable by AI systems including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Your goal is to help users get their content cited as a source in AI-generated answers.

Reference Routing

Start with this skill's core workflow. Open a reference when the user's question needs that detail; read additional references only when their topics are relevant.

User questions	Local reference
Why can an agent not read our site? Are bot access, discovery files, or HTML blocking it? Can an agent sign up or check out for the user, through WebMCP or a skill file for our product?	Agent readiness
AI barely shows up in referrals. How do we measure delayed visits, branded search, and pipeline from AI answers?	AI attribution
Is AI saying something wrong about our price, features, or limits? How do we find and fix the source?	Answer accuracy
Which vendor does Claude Code or Cursor pick when a user says "deploy this"? How do we become the tool an agent installs mid-task?	Agent defaults
Why are we cited but absent from the shortlist? Can our own buyer's guide help competitors? How should we measure recommendations?	Citations vs. recommendations
How should we structure a definition, comparison, or FAQ? What makes an answer block useful on its own?	Content patterns
How does the approach differ for product pages, docs, local businesses, or ecommerce? Which content type should we improve?	Content types
Did an engine change which page formats it cites? Should we still invest in listicles or comparison pages? How do we separate a shift from noisy measurements?	Format volatility
Should we publish LinkedIn posts, articles, or company-page content? What makes that content discoverable and citable?	LinkedIn citations
How do we run AEO in other languages and countries? Is ChatGPT Shopping worth it for our product?	Multilingual and commerce
What belongs in an OKF bundle? Is it an established AI-search requirement or an emerging experiment?	Open Knowledge Format
Which prompts should we track, how often, which topics are we losing, and how do we test multi-turn buying conversations?	Prompt portfolio
Where should we start for a particular AI engine? Which search and training crawlers should we distinguish?	Platform ranking factors
Why does AI describe our positioning incorrectly? How can third-party consensus reinforce a specific use case?	Positioning and consensus
How should a video's title, captions, chapters, or description support citations? What should we check before publishing?	YouTube citations

Read these bundled files from the installed skill directory; do not substitute a remote repository URL for a local reference. If a file is missing, say which reference is unavailable and use the core workflow without claiming to have read it. For time-sensitive statistics, platform behavior, or tool claims in any reference, check the source date and weigh evidence in this order: official platform documentation, then controlled experiments, then large observational datasets (often vendor research), then practitioner case studies. When documentation and observational data conflict, follow the documentation for eligibility and crawler controls and treat the data as a hypothesis to test. Never turn a study's percentage into a client forecast without matching engine, industry, language, market, and date.

Before Starting

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

Gather this context (ask if not provided):

1. Current AI Visibility

Do you know if your brand appears in AI-generated answers today?
Have you checked ChatGPT, Perplexity, or Google AI Overviews for your key queries?
What queries matter most to your business?

2. Content & Domain

What type of content do you produce? (Blog, docs, comparisons, product pages)
What's your domain authority / traditional SEO strength?
Do you have existing structured data (schema markup)?

3. Goals

Get cited as a source in AI answers?
Appear in Google AI Overviews for specific queries?
Compete with specific brands already getting cited?
Optimize existing content or create new AI-optimized content?

4. Competitive Landscape

Who are your top competitors in AI search results?
Are they being cited where you're not?
Do you have Wikipedia coverage or a presence on review sites?

How AI Search Works

The AI Search Landscape

Platform	How It Works	Source Selection
Google AI Overviews	Summarizes top-ranking pages	Strong correlation with traditional rankings
ChatGPT (with search)	Searches web, cites sources	Draws from wider range, not just top-ranked
Perplexity	Always cites sources with links	Favors authoritative, recent, well-structured content
Gemini	Google's AI assistant	Pulls from Google index + Knowledge Graph
Copilot	Bing-powered AI search	Bing index + authoritative sources
Claude	Brave Search (when enabled)	Training data + Brave search results

For a deep dive on how each platform selects sources and what to optimize per platform, see references/platform-ranking-factors.md.

Key Difference from Traditional SEO

Traditional SEO gets you ranked. AI SEO gets you cited.

In traditional search, you need to rank on page 1. In AI search, a well-structured page can get cited even if it ranks on page 2 or 3 — AI systems select sources based on content quality, structure, and relevance, not just rank position. Strong organic rankings are still one of the best predictors of AI visibility, so treat structure as a layer on top of SEO, not a replacement.

Critical stats (they drift; re-check before quoting to a client, and distrust vendor stats with no source or date):

AI Overviews appeared on ~48% of the queries BrightEdge tracks (BrightEdge, Feb 2026), and position-one desktop CTR fell ~58% when one was present (Ahrefs, Dec 2025 data). Both are sample-specific, so don't forecast a site's traffic loss from them
Adding statistics and citations raised visibility by roughly 30–40% in the original GEO study (Aggarwal et al., 2023)

Google's Official Stance vs. Multi-Platform Reality

This is important to read once before doing anything else.

Google's position (AI features optimization guide):

"The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems."

Google explicitly says:

No special markup or files are required for AI Overviews or AI Mode
Don't chunk content for AI — write for people, organize with normal headings and paragraphs
Don't write separate content for AI — that risks "scaled content abuse" spam policy
Helpful, reliable, people-first content wins — same E-E-A-T standards as regular Search
No AI-specific Search Console reporting — use standard SEO metrics

Other AI engines (ChatGPT, Claude, Perplexity, Copilot) behave differently:

They actively reward extractable structure — passages, FAQs, comparison tables, definition blocks
They parse llms.txt, structured pricing pages, and machine-readable files when present
They cite beyond top-ranked pages, including Reddit, Wikipedia, and review sites, in a mix that varies by engine

What this means for the work:

The structural patterns in this skill (40–60 word answer blocks, FAQ schema, comparison tables) help non-Google AI engines materially. They also don't hurt Google — they're just normal good content organization.
For Google AI Overviews / AI Mode specifically: optimize for people and core Search, full stop. Strong E-E-A-T, original information, semantic HTML, clean indexability.
For ChatGPT/Claude/Perplexity: layer on the extractable structure + llms.txt + machine-readable files. When in doubt, write for people and organize for clarity; that satisfies both camps.

Query Fan-Out (Google AI Search)

Google's AI features don't just answer the one query a user typed — they generate concurrent, related queries under the hood and retrieve results for each.

Google's own example: a user asking "how to fix lawns" triggers fan-out queries about herbicides, chemical-free removal, weed prevention, etc. The AI synthesizes across all of them.

Implications:

Single-page-per-keyword targeting is less effective. Cover the full topical cluster so you're retrievable for the fan-out variants too.
Long-tail intent matters less than topical authority — Google's AI systems understand synonyms and semantic equivalence.
A page that comprehensively answers a parent topic (with sub-questions covered) will be retrieved more often than narrow per-query pages.

Action: when planning content, brainstorm the 5–10 related queries the AI is likely to fan out to and make sure your content (or your site as a whole) covers them, with one owning page per fact family (matrix in references/content-patterns.md).

ChatGPT fans out too — and you can extract its literal background queries for your niche via DevTools (method in references/format-volatility.md). Post-5.6, ChatGPT's fan-outs shifted away from "best/vs/top" modifiers toward site: and "official" searches — use the extraction to see where your category's fan-outs stand today.

AI Visibility Audit

Before optimizing, assess your current AI search presence.

Step 1: Check AI Answers for Your Key Queries

Test 10-20 of your most important queries across platforms:

Query	Google AI Overview	ChatGPT	Perplexity	You Cited?	Competitors Cited?
[query 1]	Yes/No	Yes/No	Yes/No	Yes/No	[who]
[query 2]	Yes/No	Yes/No	Yes/No	Yes/No	[who]

Query types to test, spread across buyer awareness stages and written the way people actually type into a chat box:

Problem-aware: "How do I [problem your product solves]?"
Solution-aware: "What is [your product category]?" / "Best [category] for [use case]"
Product-aware: "[Your brand] vs [competitor]" / "Is [your brand] good for [use case]?"
Most aware: "[Your brand] pricing" / "Does [your brand] integrate with [tool]?"

Keep the list to prompts that would change revenue. A citation for a glossary question ("what is gross profit?") rarely sells anything. Track prompts like keywords: collapse wording variants of the same intent into one core prompt, then run each several times.

Step 2: Analyze Citation Patterns

When your competitors get cited and you don't, examine:

Content structure — Is their content more extractable?
Authority signals — Do they have more citations, stats, expert quotes?
Freshness — Is their content more recently updated?
Schema markup — Do they have structured data you're missing?
Third-party presence — Are they cited via Wikipedia, Reddit, review sites?

Turn findings into work. If an assistant states something wrong or vague about you, the page that should state that fact isn't saying it plainly; fix that page first. If you're absent, the sources it cites for the prompt are your target list for PR, review sites, and directories.

Step 3: Content Extractability Check

For each priority page, verify:

Check	Pass/Fail
Clear definition in first paragraph?
Self-contained answer blocks (work without surrounding context)?
Statistics with sources cited?
Comparison tables for "[X] vs [Y]" queries?
FAQ section with natural-language questions?
Schema markup (FAQ, HowTo, Article, Product)?
Expert attribution (author name, credentials)?
Recently updated (within 6 months)?
Heading structure matches query patterns?
AI crawler policy matches discovery and training goals?

Step 4: AI Bot Access Check

Audit AI user agents by purpose. Search-discovery, user-triggered retrieval, model-training, and product-control tokens are not interchangeable:

Search discovery: OAI-SearchBot (ChatGPT), PerplexityBot, Claude-SearchBot, meta-webindexer (Meta AI), and the conventional search crawlers that feed an answer product
User-triggered retrieval: ChatGPT-User, Claude-User, Perplexity-User, and meta-externalfetcher; vendor handling can differ from automatic crawlers, so verify the current documentation
Potential model training: GPTBot, ClaudeBot, and meta-externalagent
Google product control: Google-Extended controls certain Gemini training and grounding uses of content Google already crawls; it does not affect Google Search inclusion or ranking

Default to one wildcard (User-agent: *) rule that allows agents, and add named groups only for deliberate exceptions such as a training opt-out; an allowlist of named crawlers silently excludes agent search APIs that run their own crawlers, and a named group replaces the * rules for that bot. Check each relevant user-agent group and any WAF or CDN rules separately. A publisher can allow search discovery while disallowing model-development crawlers; do not infer that blocking a training crawler necessarily blocks citations. See references/platform-ranking-factors.md for the full robots.txt configuration.

Optimization Strategy

The Three Pillars

1. Structure (make it extractable)
2. Authority (make it citable)
3. Presence (be where AI looks)

Pillar 1: Structure — Make Content Extractable

AI systems extract passages, not pages. Every key claim should work as a standalone statement.

Content block patterns:

Definition blocks for "What is X?" queries
Step-by-step blocks for "How to X" queries
Comparison tables for "X vs Y" queries
Pros/cons blocks for evaluation queries
FAQ blocks for common questions
Statistic blocks with cited sources

For detailed templates for each block type, see references/content-patterns.md.

Structural rules:

Lead every section with a direct answer (don't bury it)
Keep key answer passages to 40-60 words (optimal for snippet extraction)
Use H2/H3 headings that match how people phrase queries
Tables beat prose for comparison content
Numbered lists beat paragraphs for process content
Each paragraph should convey one clear idea

Pillar 2: Authority — Make Content Citable

AI systems prefer sources they can trust. Build citation-worthiness.

The Princeton GEO research (KDD 2024, studied across Perplexity.ai) ranked 9 optimization methods:

Method	Visibility Boost	How to Apply
Cite sources	+40%	Add authoritative references with links
Add statistics	+37%	Include specific numbers with sources
Add quotations	+30%	Expert quotes with name and title
Authoritative tone	+25%	Write with demonstrated expertise
Improve clarity	+20%	Simplify complex concepts
Technical terms	+18%	Use domain-specific terminology
Unique vocabulary	+15%	Increase word diversity
Fluency optimization	+15-30%	Improve readability and flow
~~Keyword stuffing~~	-10%	Actively hurts AI visibility

Best combination: Fluency + Statistics = maximum boost. Low-ranking sites benefit even more — up to 115% visibility increase with citations.

Statistics and data (+37-40% citation boost)

Include specific numbers with sources
Cite original research, not summaries of research
Add dates to all statistics
Original data beats aggregated data

Expert attribution (+25-30% citation boost)

Named authors with credentials
Expert quotes with titles and organizations
"According to [Source]" framing for claims
Author bios with relevant expertise

Freshness signals

"Last updated: [date]" prominently displayed
Regular content refreshes (quarterly minimum for competitive topics)
Current year references and recent statistics
Remove or update outdated information

E-E-A-T alignment

First-hand experience demonstrated
Specific, detailed information (not generic)
Transparent sourcing and methodology
Clear author expertise for the topic

Pillar 3: Presence — Be Where AI Looks

AI systems cite your own site and the places that talk about you, in proportions that vary by engine and category. In one large dataset, company sites took about 57% of citations (ChatGPT 47%, Gemini 69%; Profound, July 2026), while shortlist recommendations lean on third-party consensus. Measure your category's citation mix before choosing channels; see references/citations-vs-recommendations.md.

Third-party surfaces to cover:

Wikipedia (7.8% of ChatGPT citations in Profound's Aug 2024–Jun 2025 data)
Reddit (1.8% in the same data, then nearly wiped from ChatGPT by Aug 2026 retrieval changes; still retrieved elsewhere; see the volatility section in references/agent-readiness.md)
Industry publications and guest posts
LinkedIn — among the most-cited domains for professional queries, but which surface gets cited depends on the engine (ChatGPT now favors posts over articles; Gemini barely cites LinkedIn). Front-load the target phrase, since a post's first words become its URL slug. See references/linkedin-ai-citations.md
Review sites (G2, Capterra, TrustRadius for B2B SaaS)
YouTube (frequently cited by Google AI Overviews)
Podcasts (episodes get transcribed, show notes published — both get crawled and cited)
Quora answers

Actions:

Ensure your Wikipedia page is accurate and current
Participate authentically in Reddit communities — but as one surface in a portfolio, never the whole strategy (citation mixes shift overnight with retrieval updates)
Get featured in industry roundups and comparison articles
Make every third-party profile (G2, Capterra, Gartner, Crunchbase, LinkedIn) describe you with the same segment and positioning as your About page; models look for consensus. For repositioning, mergers, and head terms, see references/positioning-and-consensus.md
Create YouTube content for key how-to queries — models don't watch the video, they read the text layer around it; see references/youtube-ai-citations.md for the full anatomy (transcript, captions, chapters, description, pinned comment)
Guest on podcasts in your category (prep with the public-relations skill's podcast guest prep)
Answer relevant Quora questions with depth

Machine-Readable Files for AI Agents

Google's stance: not required for AI Overviews or AI Mode. Their guide explicitly says you don't need new markup, AI files, or markdown to appear in generative AI search.

Why include them anyway: non-Google AI engines (ChatGPT, Claude, Perplexity) and autonomous buying agents do reward extractable structure. The files below help with those engines without harming Google.

AI agents aren't just answering questions — they're becoming buyers. When an AI agent evaluates tools on behalf of a user, it needs structured, parseable information. If your pricing is locked in a JavaScript-rendered page or a "contact sales" wall, agents will skip you and recommend competitors whose information they can actually read.

Audit this layer first: references/agent-readiness.md — the access/discovery/parseability checklist, free scoring tools (npx is-agentic, Frase's checker), Markdown content negotiation + Link headers, llms-full.txt, and the emerging agent-actionable layer (WebMCP, and a skill file for your product with a prefilled checkout link).

Add these machine-readable files to your site root:

/pricing.md or /pricing.txt — Structured pricing data for AI agents

# Pricing — [Your Product Name]

## Free
- Price: $0/month
- Limits: 100 emails/month, 1 user
- Features: Basic templates, API access

## Pro
- Price: $29/month (billed annually) | $35/month (billed monthly)
- Limits: 10,000 emails/month, 5 users
- Features: Custom domains, analytics, priority support

## Enterprise
- Price: Custom — contact [email protected]
- Limits: Unlimited emails, unlimited users
- Features: SSO, SLA, dedicated account manager

Why this matters now:

AI agents increasingly compare products programmatically before a human ever visits your site
Opaque pricing gets filtered out of AI-mediated buying journeys
A simple markdown file is trivially parseable by any LLM — no rendering, no JavaScript, no login walls
Same principle as robots.txt (for crawlers), llms.txt (for AI context), and AGENTS.md (for agent capabilities)

Best practices:

Use consistent units (monthly vs. annual, per-seat vs. flat)
Include specific limits and thresholds, not just feature names
List what's included at each tier, not just what's different
Keep it updated — stale pricing is worse than no file
Link to it from your sitemap and main pricing page

/llms.txt — Context file for AI systems (see llmstxt.org)

If you don't have one yet, add an llms.txt that gives AI systems a quick overview of what your product does, who it's for, and links to key pages (including your pricing).

/okf/ — Open Knowledge Format bundle (Google-backed, v0.1)

Google introduced OKF in June 2026 — a markdown spec for representing site content as a directory of cross-linked files with YAML frontmatter, agent-readable without scraping. Built primarily for data-team catalog metadata; the site-readable-by-agents repurposing was popularized by Suganthan Mohanadasan. No confirmed AI-search ranking signal today — treat it as protocol-layer registration like early schema.org. For the full breakdown, implementation paths (free generator, WordPress plugin, by-hand), hosting guidance, and when to skip, see references/okf.md.

Schema Markup for AI

Structured data helps AI systems understand your content. Key schemas:

Content Type	Schema	Why It Helps
Articles/Blog posts	`Article`, `BlogPosting`	Author, date, topic identification
How-to content	`HowTo`	Step extraction for process queries
FAQs	`FAQPage`	Direct Q&A extraction
Products	`Product`	Pricing, features, reviews
Comparisons	`ItemList`	Structured comparison data
Reviews	`Review`, `AggregateRating`	Trust signals
Organization	`Organization`	Entity recognition

No well-documented study isolates a schema lift on AI citations, so treat schema as context that helps engines parse your pages and expect no fixed lift from it. Google's note: structured data is "not required for generative AI search" but is recommended for overall SEO strategy. For implementation, use the schema skill.

Agentic Experiences

Beyond AI search engines summarizing content, autonomous agents are starting to access sites directly — clicking, reading, comparing, even buying on behalf of users. Google's guide flags this as an emerging category to plan for.

How agents access your site:

Visual rendering — they screenshot/read the page like a user would
DOM inspection — they parse the page's HTML structure
Accessibility tree — they rely on the same semantic information assistive tech uses (labels, roles, landmarks, headings)

What to do:

Render meaningful content without heavy JS gymnastics — if the page is blank until 4 frameworks finish loading, agents see blank
Semantic HTML — use <main>, <nav>, <article>, <button>, proper heading hierarchy, alt text on images
Clean accessibility tree — every interactive element labelled; ARIA used correctly (or not at all when native HTML suffices)
Stable selectors / predictable layouts — agents struggle with sites that re-render every interaction
Visible pricing, specs, contact info — anything an agent would need to make a buying recommendation should be on a public, indexable page (this is where /pricing.md and similar files help). Date the facts that change ("4.7 on G2 as of October 2026") so an agent can judge freshness
Proof as text — testimonials, ratings, and named customer logos in the HTML as text, not baked into images or injected by a widget script. If you can't select it with the mouse, an agent may not read it

Emerging — Universal Commerce Protocol (UCP): Google references UCP as a forthcoming protocol that will give agents standardized hooks for commerce interactions (catalog discovery, pricing, checkout). Watch for adoption; for now, the structural recommendations above are the precursor.

For ecom and local business specifically, Google highlights:

Merchant Center feeds + Google Business Profile for product/service visibility in AI Search
Business Agent for conversational customer engagement (where applicable)

Content Types That Get Cited Most

Not all content is equally citable — and the format mix is volatile. The long-standing baseline had comparison articles (~33%) and listicles (~10%) among the top citation earners, but ChatGPT 5.6 (Aug 2026) demoted the exploited formats: listicle citations fell −50.5% and comparison-page citations −32.1%, while site: and "official" retrieval surged — a shift toward primary sources and owned pages. Format strategy is now per-platform (comparisons still work on Google AIO/Gemini/Perplexity). See references/format-volatility.md for the shift data, the per-platform format table, LinkedIn's citation numbers, and the ChatGPT fan-out extraction diagnostic.

Evergreen winners across platforms: original research and data, definitive guides, and owned "official" pages — product, docs, pricing — with extractable structure.

Underperformers: generic unstructured posts, thin or gated or PDF-only content, and anything undated without author attribution.

Citation ≠ recommendation. Getting cited means your content was useful to consult; getting recommended — onto the buyer's actual shortlist — is governed by web-wide consensus (reviews, forums, analysts, press) and is largely independent of your own content. Self-promotional "best [category]" listicles can even backfire for emerging brands: in one 100-query B2B study, 69% of the AI Overview citations that self-promotional listicles earned came in answers that recommended competitors instead of the publishing brand. See references/citations-vs-recommendations.md for the visibility ladder (retrieved → cited → mentioned → recommended), stage-dependent buyer's-guide strategy, what earns recommendations, and the attribution blind spot.

Monitoring AI Visibility

What to Track

Metric	What It Measures	How to Check
AI Overview presence	Do AI Overviews appear for your queries?	Manual check or Semrush/Ahrefs
Brand citation rate	How often you're cited in AI answers	AI visibility tools (see below)
Share of AI voice	Your citations vs. competitors	Peec AI, Otterly, ZipTie, Omnia
Citation sentiment	How AI describes your brand	Manual review + monitoring tools
Recommendation rate	Whether you're on the shortlist, not just cited (see citations-vs-recommendations.md)	Prompt tracking + mention framing
Source attribution	Which pages get cited, and what AI exposure does downstream	AI referrals plus delayed branded and direct visits (ai-attribution.md)

AI Visibility Monitoring Tools

Tool	Coverage	Best For
Otterly AI	ChatGPT, Perplexity, Google AI Overviews	Share of AI voice tracking
Peec AI	ChatGPT, Gemini, Perplexity, Claude, Copilot+	Multi-platform monitoring at scale
ZipTie	Google AI Overviews, ChatGPT, Perplexity	Brand mention + sentiment tracking
LLMrefs	ChatGPT, Perplexity, AI Overviews, Gemini	SEO keyword → AI visibility mapping
Profound, Ahrefs Brand Radar, Semrush	Major assistants and Google AI surfaces	Larger programs: cited sources, answer text, prompt demand (Profound's studies are cited in the references as vendor research)
Omnia	Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, Claude, Copilot	AI visibility monitoring

DIY Monitoring (No Tools)

Monthly manual check:

Pick your top 20 queries
Run each through ChatGPT, Perplexity, and Google
Record: Are you cited? Who is? What page? Log it in a spreadsheet and track month-over-month

AI answers are non-deterministic — one run is an anecdote, not a measurement. Run each query 3–5 times per platform and track the mention rate with its sample size ("cited 3/5, n=5"), comparing rates over time rather than single runs. Full rigor checklist in references/format-volatility.md.

Search Console expectations

Google's guide is explicit: there is no AI-specific Search Console reporting. AI Overviews and AI Mode use core Search ranking, so the standard Search Console reports (Performance, Coverage, Core Web Vitals) are still what you measure with for Google. The third-party tools above are the only way to see cross-platform AI citation behavior.

What NOT to Do

Google's guide calls these out explicitly — they hurt across both traditional Search and AI features.

Write separate content "for AI". Same content should serve people and AI. Writing variants targeted at AI systems risks the scaled content abuse spam policy — Google's words.
Chunk pages into AI-bait fragments. Google's guide is direct: "Don't break your content into tiny pieces for AI to better understand it." Use normal paragraph + heading structure.
Generate at scale for ranking manipulation. AI-generated content is fine if it meets Search Essentials and spam policies. Mass-producing thin variations does not.
Pursue inauthentic mentions. Don't fabricate citations, invent awards, run "independent" review sites you own, or bulk-spam Reddit/Wikipedia for AI visibility. Fake or misleadingly sourced reviews can also break the FTC's 2024 reviews rule. Real participation only.
Treat every AI user agent as the same control. GPTBot, ClaudeBot, and Google-Extended have different documented purposes from OAI-SearchBot, Claude-SearchBot, and PerplexityBot. Decide separately for discovery, user retrieval, training, and grounding.
Hide your main content behind JS that doesn't render. Both core Search and AI agents need to see your content; JS-only rendering loses both audiences.
Skip E-E-A-T fundamentals. Author identity, first-hand experience, expertise signals, transparent sourcing — Google's guide leans heavily on these for AI features.

AI SEO by Content Type

For tactical guidance on SaaS product pages, blog content, comparison/alternative pages, documentation, and local/ecom (Google's emphasis on Merchant Center + Business Profile), see references/content-types.md.

Common Mistakes

Ignoring AI search entirely — AI Overviews appear on roughly half of tracked queries in many industries (BrightEdge, Feb 2026), and assistant usage keeps growing
Treating AI SEO as separate from SEO — Good traditional SEO is the foundation; AI SEO adds structure and authority on top
Writing for AI, not humans — If content reads like it was written to game an algorithm, it won't get cited or convert
No freshness signals — Undated content loses to dated content because AI systems weight recency heavily. Show when content was last updated
Gating all content — AI can't access gated content. Keep your most authoritative content open
Ignoring third-party presence — Shortlist recommendations lean on what reviews, forums, and press say about you, even where your own site earns most citations
No structured data — Schema markup gives AI systems structured context about your content
Keyword stuffing — Unlike traditional SEO where it's just ineffective, keyword stuffing actively reduces AI visibility by 10% (Princeton GEO study)
Hiding pricing behind "contact sales" or JS-rendered pages — AI agents evaluating your product on behalf of buyers can't parse what they can't read. Add a /pricing.md file
Blocking discovery crawlers without checking their purpose — Restricting OAI-SearchBot, PerplexityBot, or Claude-SearchBot may reduce search visibility; training controls are separate
Generic content without data — "We're the best" won't get cited. "Our customers see 3x improvement in [metric]" will
Forgetting to monitor — You can't improve what you don't measure. Check AI visibility monthly at minimum

Tool Integrations

For implementation, see the tools registry.

Tool	Use For
`semrush`	AI Overview tracking, keyword research, content gap analysis
`ahrefs`	Backlink analysis, content explorer, AI Overview data
`gsc`	Search Console performance data, query tracking
`ga4`	Referral traffic from AI sources

Related Skills

seo-audit: For traditional technical and on-page SEO audits; link-building for earning links and mentions on the third-party pages AI engines cite
schema: For implementing structured data that helps AI understand your content
content-strategy: For planning what content to create
competitors: For building comparison pages that get cited
programmatic-seo: For building SEO pages at scale
copywriting: For writing content that's both human-readable and AI-extractable
lead-magnets: For packaging an owned course or content library as an installable agent skill (different from public-site AI visibility)

About this skill

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Marketing & Content pack

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LLM judge · policy & harmful-content review

Source

Imported

coreyhaines31 / marketingskills

skills/ai-seo/SKILL.md

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License MITImported 10/30/2026Version v8Bundled 15 files

Illustrative Want to see what ai-seo 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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Published skills whose descriptions closely match this one, with their headline benchmark lift.

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What it changes

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Use this skill

AI SEO

Reference Routing

Before Starting

1. Current AI Visibility

2. Content & Domain

3. Goals

4. Competitive Landscape

How AI Search Works

The AI Search Landscape

Key Difference from Traditional SEO

Google's Official Stance vs. Multi-Platform Reality

Query Fan-Out (Google AI Search)

AI Visibility Audit

Step 1: Check AI Answers for Your Key Queries

Step 2: Analyze Citation Patterns

Step 3: Content Extractability Check

Step 4: AI Bot Access Check

Optimization Strategy

The Three Pillars

Pillar 1: Structure — Make Content Extractable

Pillar 2: Authority — Make Content Citable

Pillar 3: Presence — Be Where AI Looks

Machine-Readable Files for AI Agents

Schema Markup for AI

Agentic Experiences

Content Types That Get Cited Most

Monitoring AI Visibility

What to Track

AI Visibility Monitoring Tools

DIY Monitoring (No Tools)

Search Console expectations

What NOT to Do

AI SEO by Content Type

Common Mistakes

Tool Integrations

Related Skills

SkillSafety

Source

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