Ready to supercharge your AI agents?

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

Expired Domain Opportunity Finder · varnan-tech | DecimalAI

Marketingby varnan-tech

Expired Domain Opportunity Finder

Evaluates expired domain candidates against a target niche, scores them by topical relevance, historical activity level, and history cleanliness, then outputs a ranked shortlist with explainable reasoning and risk flags.

Maintained

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%68%of test cases passed +32 pts

Without skill8 of 22

With skill15 of 22

9 fixed6 passed either way5 still failing2 broke

Measured on gemini-3.6-flash8/18/2026v117 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIn the 6-dimension scoring framework, what are the maximum point allocations for Topical Relevance and Historical Activity 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 varnan-tech-domain-expired-opportunity-finder
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("varnan-tech-domain-expired-opportunity-finder")
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 varnan-tech-domain-expired-opportunity-finder

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/varnan-tech-domain-expired-opportunity-finder/SKILL.md https://app.decimal.ai/s/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/varnan-tech-domain-expired-opportunity-finder/SKILL.md

This gets SKILL.md only. The skill's 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/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/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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("varnan-tech-domain-expired-opportunity-finder")
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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/varnan-tech-domain-expired-opportunity-finder/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 4 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 varnan-tech-domain-expired-opportunity-finder

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/varnan-tech-domain-expired-opportunity-finder/SKILL.md https://app.decimal.ai/s/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/varnan-tech-domain-expired-opportunity-finder/SKILL.md

This gets SKILL.md only. The skill's 4 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/varnan-tech-domain-expired-opportunity-finder/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/varnan-tech-domain-expired-opportunity-finder.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/18/2026
Source: varnan-tech/opendirectory ↗
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

Expired Domain Opportunity Finder

Evaluate expired domain candidates for a specific niche. Score them on topical fit, historical activity level, history cleanliness, and redirect suitability. Output a conservative, explainable shortlist for human review.

Critical rule: Every recommendation must include BOTH a positive rationale (why_selected) AND a caution rationale (why_risky). Never output a bare score without explanation.

Conservative-by-default rule: When signals are incomplete or contradictory, lower the confidence level. Do not surface ambiguous candidates as strong opportunities. Missing data reduces confidence, never inflates it.

Anti-abuse rule: Never encourage unrelated redirects, PBN construction, or domain repurposing where the historical topic does not match the target niche. Read references/guardrails.md for the full anti-abuse policy.

Step 1: Setup Check

Check the environment before doing anything else.

Verify that curl and python3 (or python) are available:

curl --version > /dev/null 2>&1 && echo "curl: available" || echo "curl: MISSING"
python3 --version 2>/dev/null || python --version 2>/dev/null || echo "python: MISSING"

Check for an optional LLM API key for enhanced niche-relevance scoring:

echo "LLM_API_KEY: ${LLM_API_KEY:+set}"

If curl or python is missing: Stop. Tell the user: "This skill requires curl and Python 3.10+. Please install them and try again."

If LLM_API_KEY is not set: Continue. The skill will use rule-based scoring only (domain string matching, Wayback title analysis, keyword overlap). Note to the user: "Running in rule-based-only mode. Set LLM_API_KEY for enhanced niche-relevance scoring."

If LLM_API_KEY is set: The skill will use LLM-enhanced scoring for topical relevance analysis. This provides deeper contextual assessment of niche fit.

QA: State the scoring mode (llm-enhanced or rule-based-only) and confirm tools are available.

Step 2: Input Collection

Collect the required and optional inputs from the user.

Required:

target_niche (string): The core niche to evaluate against. Examples: "developer tools", "AI SaaS", "cybersecurity", "fintech".

Optional (ask only if not provided):

seed_keywords (array): Keywords to refine topical matching. If not provided, extract 3–5 keywords from the niche name automatically.
candidate_domains (array): Specific domains to evaluate. If not provided, prompt the user.
discovery_source (string): Where candidates came from — manual, expireddomains-net, external-feed.
min_snapshots (integer): Minimum historical snapshot threshold. Default: 10.
max_risk_level (string): low, medium, or high. Controls how aggressively risky candidates are filtered. Default: medium.
intended_use (string): rebuild, redirect, or either. Default: either.

If no candidate_domains are provided: Ask: "Please provide a list of expired domain candidates to evaluate. You can:

Paste domain names (one per line or comma-separated)
Provide a file path to a text file with one domain per line
Say 'example' to run with a built-in demo set for the 'developer tools' niche"

If the user says 'example': Use this demo set:

devtoolsweekly.com
codeshipnews.io
stackforgeapp.com
quickseorank.net
bestcheaphosting247.com
cloudbuildpro.dev
reactwidgetlib.com
megadealsshop.xyz

After collecting all inputs, confirm: "Target niche: [niche]. Evaluating [N] candidate domains. Scoring mode: [mode]. Intended use: [use]."

Step 3: Candidate Normalization

Clean and validate the candidate list before scoring.

python3 -c "
import sys, re

domains = '''CANDIDATE_LIST_HERE'''.strip().split('\n')
seen = set()
valid = []
invalid = []

for d in domains:
    d = d.strip().lower()
    # Strip protocols and paths
    d = re.sub(r'^https?://', '', d)
    d = d.split('/')[0]
    d = d.strip('.')

    if not d:
        continue

    # Basic TLD validation
    if '.' not in d or len(d) < 4:
        invalid.append(d)
        continue

    # Deduplicate
    if d in seen:
        continue
    seen.add(d)
    valid.append(d)

print(f'Valid candidates: {len(valid)}')
print(f'Removed (invalid/duplicate): {len(invalid)}')
for v in valid:
    print(f'  ✓ {v}')
for i in invalid:
    print(f'  ✗ {i} (invalid format)')
"

Replace CANDIDATE_LIST_HERE with the actual domain list from Step 2.

State: "[N] valid candidates after normalization. [M] removed (invalid/duplicate)."

If 0 valid candidates remain, stop and tell the user: "No valid domain candidates found. Please provide domain names in the format 'example.com'."

Step 4: Signal Collection

For each valid candidate, collect signals from free public sources. Run these checks sequentially per domain.

4a: Wayback CDX API — History Snapshots

Query the Wayback Machine for all historical snapshots. We use limit=100000 and explicit from/to parameters are intentionally omitted so that CDX returns snapshots from the full lifetime of the domain. The results are sorted ascending by timestamp (oldest first) so first_capture and last_capture are accurate:

curl -s "https://web.archive.org/cdx/search/cdx?url=DOMAIN_HERE&output=json&fl=timestamp,statuscode&collapse=timestamp:6&limit=100000" \
  | python3 -c "
import sys, json

try:
    data = json.load(sys.stdin)
    if len(data) <= 1:
        print(json.dumps({'domain': 'DOMAIN_HERE', 'snapshots': 0, 'first_capture': None, 'last_capture': None, 'status_codes': {}, 'years_active': 0}))
    else:
        rows = data[1:]  # skip header row
        timestamps = [r[0] for r in rows]  # already ascending (oldest first)
        statuses = [r[1] for r in rows]
        status_counts = {}
        for s in statuses:
            status_counts[s] = status_counts.get(s, 0) + 1
        first_year = int(timestamps[0][:4])
        last_year = int(timestamps[-1][:4])
        print(json.dumps({
            'domain': 'DOMAIN_HERE',
            'snapshots': len(rows),
            'first_capture': timestamps[0],
            'last_capture': timestamps[-1],
            'status_codes': status_counts,
            'years_active': last_year - first_year + 1
        }))
except:
    print(json.dumps({'domain': 'DOMAIN_HERE', 'snapshots': 0, 'error': 'wayback_api_failed'}))
"

Rate limiting: Wait 2 seconds between Wayback API calls to be polite to the service.

4b: Wayback Content Sampling — Historical Page Titles

For candidates with > 0 snapshots, fetch the most recent snapshot to extract the page title (used for topical relevance scoring):

curl -s -L "https://web.archive.org/web/LATEST_TIMESTAMP/http://DOMAIN_HERE" \
  | python3 -c "
import sys, re
html = sys.stdin.read()[:50000]
title_match = re.search(r'<title[^>]*>(.*?)</title>', html, re.IGNORECASE | re.DOTALL)
title = title_match.group(1).strip() if title_match else 'no title found'
# Extract meta description too
meta_match = re.search(r'<meta[^>]*name=[\"']description[\"'][^>]*content=[\"'](.*?)[\"']', html, re.IGNORECASE)
desc = meta_match.group(1).strip() if meta_match else 'no description found'
print(f'Title: {title}')
print(f'Description: {desc}')
"

Replace LATEST_TIMESTAMP with the most recent timestamp from Step 4a.

4c: RDAP Lookup — Registration Status

Use the cross-platform HTTP-based RDAP standard (replaces OS-dependent WHOIS). An HTTP 404 from RDAP means the domain is not registered (i.e. it is genuinely available or untracked) — that is distinct from a network failure. Handle both cases explicitly:

python3 -c "
import urllib.request, urllib.error, json

domain = 'DOMAIN_HERE'
try:
    req = urllib.request.Request(
        f'https://rdap.org/domain/{domain}',
        headers={'User-Agent': 'Mozilla/5.0'}
    )
    with urllib.request.urlopen(req, timeout=10) as response:
        data = json.loads(response.read().decode())

    registrar = 'unknown'
    created = 'unknown'

    for entity in data.get('entities', []):
        if 'registrar' in entity.get('roles', []):
            try:
                registrar = entity.get('vcardArray', [[]])[1][0][3]
            except Exception:
                pass

    for event in data.get('events', []):
        if event.get('eventAction') == 'registration':
            created = event.get('eventDate', 'unknown')

    print(json.dumps({
        'domain': domain,
        'status': 'registered',
        'registrar': registrar,
        'created': created
    }))
except urllib.error.HTTPError as e:
    if e.code == 404:
        # Domain has no RDAP object — likely unregistered or not in RDAP coverage
        print(json.dumps({'domain': domain, 'status': 'unregistered_or_no_rdap_object'}))
    else:
        print(json.dumps({'domain': domain, 'error': f'rdap_http_error_{e.code}'}))
except Exception:
    print(json.dumps({'domain': domain, 'error': 'rdap_lookup_failed'}))
"

4d: Domain String Analysis — Keyword Matching

Score keyword overlap between the domain name and the target niche / seed keywords:

python3 -c "
import re, json

domain = 'DOMAIN_HERE'
niche = 'NICHE_HERE'
seeds = SEEDS_JSON_HERE  # e.g., ['devops', 'ci/cd', 'code editor']

# Extract words from domain
domain_base = domain.rsplit('.', 1)[0]  # remove TLD
domain_words = re.split(r'[-_.]', domain_base.lower())

# Check niche words
niche_words = niche.lower().split()
all_keywords = set(niche_words + [s.lower() for s in seeds])

matches = [w for w in domain_words if any(kw in w or w in kw for kw in all_keywords)]
match_ratio = len(matches) / max(len(domain_words), 1)

print(json.dumps({
    'domain': domain,
    'domain_words': domain_words,
    'keyword_matches': matches,
    'match_ratio': round(match_ratio, 2)
}))
"

4e: Gemini LLM Niche-Relevance Assessment (if LLM_API_KEY is set)

If the LLM API key is configured, batch all candidates with their collected signals and ask for a contextual niche-relevance assessment.

Note: The request/response format below uses the Gemini API (generateContent format). It is not compatible with OpenAI-style endpoints without modification. If you use a different provider, you must adapt the JSON body and response parsing.

cat > /tmp/domain-relevance-request.json << 'ENDJSON'
{
  "system_instruction": {
    "parts": [{
      "text": "You are an SEO research analyst. For each expired domain candidate provided, assess its topical relevance to the specified target niche. Consider the domain name, historical page title, and meta description. For each domain, output a JSON object with: domain (string), relevance_score (integer 1-10), relevance_rationale (one sentence explaining the score), redirect_plausibility (integer 1-10), redirect_rationale (one sentence). Output only a JSON array. No commentary before or after."
    }]
  },
  "contents": [{
    "parts": [{
      "text": "DOMAIN_SIGNALS_AND_NICHE_CONTEXT_HERE"
    }]
  }],
  "generationConfig": {
    "temperature": 0.2,
    "maxOutputTokens": 2048
  }
}
ENDJSON

Replace DOMAIN_SIGNALS_AND_NICHE_CONTEXT_HERE with:

The target niche and seed keywords
For each candidate: domain name, historical title, description, keyword match data

Send the request to the Gemini API:

curl -s -X POST \
  "${LLM_API_ENDPOINT:-https://generativelanguage.googleapis.com/v1beta}/models/${LLM_MODEL:-gemini-2.0-flash}:generateContent?key=$LLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/domain-relevance-request.json \
  | python3 -c "
import sys, json
try:
    d = json.load(sys.stdin)
    text = d['candidates'][0]['content']['parts'][0]['text']
    print(text)
except (KeyError, IndexError, json.JSONDecodeError) as e:
    print(json.dumps({'error': 'llm_response_parse_failed', 'detail': str(e)}))
"

If the LLM call or response parsing fails, log the error and continue with rule-based scoring only. Do not stop the workflow.

After all signal collection, state: "Signal collection complete for [N] candidates. [M] Wayback hits, [K] RDAP lookups succeeded."

Step 5: Scoring & Classification

Read references/scoring-model.md for the full scoring framework.

For each candidate, compute scores across the 6 dimensions:

Topical Relevance (0–30): Combine domain keyword match ratio, historical title/description analysis, and LLM relevance score (if available). Without LLM: use keyword match ratio × 15 + title keyword overlap × 15. With LLM: use LLM relevance_score × 3.
Historical Activity Level (0–25): Based on Wayback snapshot diversity and frequency. More snapshots consistently captured across multiple years indicates higher sustained activity and inferred legitimacy.
Historical Content Quality (0–15): Derived from historical page title and meta description analysis, checking for natural phrasing versus keyword stuffing. Without LLM: base score of 8/15 adjusted by exact-match density.
History Cleanliness (0–15): Based on Wayback snapshot count, years active, status code consistency, and absence of parking page indicators.
Redirect Suitability (0–10): Based on topic continuity between historical content and target niche. Use LLM redirect_plausibility score if available; otherwise use keyword overlap ratio.
Signal Completeness (0–5): Count how many data sources returned usable data (Wayback, RDAP, domain analysis, LLM if configured).

Compute:

opportunity_score = sum of all dimension scores (0–100)
confidence = based on how many dimensions have strong data (see scoring-model.md)
recommended_action = based on score + confidence + risk flags (see Step 6)

Step 6: Risk Flagging & Filtering

Read references/risk-flags.md for the complete flag definitions.

Apply risk flags to each candidate:

Check	Flag Applied
Historical topic overlap < 30% with target niche	`topic_mismatch`
Domain active < 1 year before expiry	`short_history`
< 3 Wayback snapshots or all parking pages	`unclear_history`
Sudden Wayback drop-off after years of activity	`possible_deindex`
Snapshot count below `min_snapshots`	`weak_historical_activity`
Redirect suitability < 4/10	`redirect_mismatch`

Apply recommendation logic:

Score + Flags	Recommendation
Score ≥ 75 AND confidence `high` AND no High-severity flags	`high-priority-review`
Score ≥ 55 AND confidence ≥ `medium`	`review`
Score ≥ 55 BUT redirect_suitability < 4/10	`rebuild-only-review`
Score < 55 OR any critical High-severity flag	`reject`

Apply max_risk_level filter:

If max_risk_level = low: exclude any candidate with Medium or High flags
If max_risk_level = medium: exclude candidates with High flags only
If max_risk_level = high: include all candidates (no filter)

Step 7: Output & Save

Read references/output-format.md for the exact JSON schema. Read references/guardrails.md for the required disclaimer text.

Default: Shortlist mode. Show only candidates with recommended_action of high-priority-review, review, or rebuild-only-review.

If the user requested audit mode, show ALL candidates with full dimension breakdowns including rejection reasons.

Present the output:

## Expired Domain Opportunity Finder — [YYYY-MM-DD]

**Target niche:** [niche]
**Seed keywords:** [keywords]
**Intended use:** [rebuild/redirect/either]
**Candidates evaluated:** [N]
**Shortlisted:** [M]
**Rejected:** [K]
**Scoring mode:** [llm-enhanced / rule-based-only]

---

### 1. [domain.com] — Score: [N]/100 | Confidence: [level] | Action: [recommendation]

**Topical fit:** [summary]
**Activity level:** [summary]
**Content quality:** [summary]
**History:** [summary]
**Redirect suitability:** [level]
**Risk flags:** [flags or "none"]

**Why selected:** [rationale]
**Why risky:** [rationale]

---

[repeat for each shortlisted domain, ranked by opportunity_score descending]

---

**Disclaimer:** These results are research recommendations, not guarantees
of SEO value. Redirect analysis should only be considered when strong
topic continuity exists between the expired domain and your target site.
Search engine algorithms change frequently. Always perform manual due
diligence — including checking current index status, reviewing the full
backlink profile with a commercial tool, and verifying domain history —
before making any acquisition decision. This skill does not endorse or
facilitate manipulative SEO practices.

Save the structured JSON output:

mkdir -p docs/expired-domain-intel
OUTFILE="docs/expired-domain-intel/$(date +%Y-%m-%d).json"
cat > "$OUTFILE" << 'EOF'
JSON_OUTPUT_HERE
EOF
echo "Saved to $OUTFILE"

If 0 candidates pass the shortlist: "No candidates met the shortlist criteria for the '[niche]' niche with the current risk tolerance. This is a normal outcome — it means the evaluated domains were not strong enough matches. Try:

Providing different candidate domains
Widening seed keywords
Setting max_risk_level to 'high' to see borderline candidates
Running in audit mode to see why candidates were rejected"

Self-QA Checklist

Run every check before presenting output:

Every shortlisted domain has both why_selected AND why_risky
No shortlisted domain has a High-severity risk flag AND high-priority-review action
Domains with redirect_mismatch are labeled rebuild-only-review (not review)
The guardrails disclaimer is present at the end of output
No hype language: no "guaranteed", "easy win", "safe to redirect", "SEO hack"
scoring_mode correctly reflects whether LLM was used
Candidates are ranked by opportunity_score descending
JSON output saved to docs/expired-domain-intel/YYYY-MM-DD.json
All Wayback API calls were rate-limited (2s between calls)

Fix any violation before presenting.

What Good Output Looks Like

Every domain has a score, confidence, action, and risk assessment
Summaries are 1–2 sentences each, specific to the candidate (not generic)
Risk flags are present and explained in why_risky
The shortlist is small (quality over quantity) — typically 2–5 domains from a batch of 10–20
Conservative: when in doubt, reject or lower confidence
The user can understand exactly why each domain was selected or rejected

What Bad Output Looks Like

Bare scores without explanation
Generic summaries like "this domain has good metrics" (must be specific)
High-priority recommendations for domains with serious risk flags
Redirect recommendations for topic-mismatched domains
No disclaimer at the end
Hype language promising SEO outcomes
Too many shortlisted domains (the skill should be selective, not permissive)

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

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

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

varnan-tech / opendirectory

skills/domain-expired-opportunity-finder/SKILL.md

View on GitHub ↗

License MITImported 10/17/2026Version v1Bundled 4 files

Illustrative Want to see what varnan-tech/domain-expired-opportunity-finder would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Related skills

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

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Expired Domain Opportunity Finder

Step 1: Setup Check

Step 2: Input Collection

Step 3: Candidate Normalization

Step 4: Signal Collection

4a: Wayback CDX API — History Snapshots

4b: Wayback Content Sampling — Historical Page Titles

4c: RDAP Lookup — Registration Status

4d: Domain String Analysis — Keyword Matching

4e: Gemini LLM Niche-Relevance Assessment (if LLM_API_KEY is set)

Step 5: Scoring & Classification

Step 6: Risk Flagging & Filtering

Step 7: Output & Save

Present the output:

Self-QA Checklist

What Good Output Looks Like

What Bad Output Looks Like

SkillSafety

Source

Related skills

More in the registry