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Performing Paste Site Monitoring for Credentials · mukul975 | DecimalAI

Securityby mukul975

Performing Paste Site Monitoring for Credentials

Monitor paste sites like Pastebin and GitHub Gists for leaked credentials, API keys, and sensitive data dumps using automated scraping and keyword matching to detect breaches early.

Never made a case worse

SkillScore

SkillScore18/100

Test results 18

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%18%of test cases passed +5 pts

Without skill3 of 22

With skill4 of 22

1 fixed3 passed either way18 still failing0 broke

Measured on gemini-3.6-flash7/31/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAn employee's internal project name and domain appear in a paste dump, but no credentials or secrets are found in the content. What severity level should be assigned?

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 performing-paste-site-monitoring-for-credentials
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("performing-paste-site-monitoring-for-credentials")
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 performing-paste-site-monitoring-for-credentials

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/performing-paste-site-monitoring-for-credentials/SKILL.md https://app.decimal.ai/s/performing-paste-site-monitoring-for-credentials/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/performing-paste-site-monitoring-for-credentials/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/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/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add performing-paste-site-monitoring-for-credentials

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("performing-paste-site-monitoring-for-credentials")
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/performing-paste-site-monitoring-for-credentials/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add performing-paste-site-monitoring-for-credentials

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/performing-paste-site-monitoring-for-credentials/SKILL.md https://app.decimal.ai/s/performing-paste-site-monitoring-for-credentials/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/performing-paste-site-monitoring-for-credentials/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/performing-paste-site-monitoring-for-credentials/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/performing-paste-site-monitoring-for-credentials.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/31/2026
Source: mukul975/Anthropic-Cybersecurity-Skills ↗
License: Apache-2.0

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Performing Paste Site Monitoring for Credentials

Overview

Paste sites (Pastebin, GitHub Gists, Ghostbin, Dpaste, Hastebin) are frequently used as staging areas for leaked credentials, database dumps, API keys, and sensitive data before wider distribution on dark web forums and Telegram channels. Monitoring these sites provides early breach detection, enabling organizations to respond before stolen data is weaponized. This skill covers building automated paste site monitors using the Pastebin Scraping API, keyword-based alerting, credential pattern matching, and integration with incident response workflows.

When to Use

When conducting security assessments that involve performing paste site monitoring for credentials
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing

Prerequisites

Python 3.9+ with requests, beautifulsoup4, regex, pymisp libraries
Pastebin PRO account with Scraping API access ($49.95/month for programmatic access)
GitHub API token for Gist monitoring
Keyword lists specific to your organization (domains, project names, internal terms)
Elasticsearch or database for paste storage and search

Key Concepts

Paste Site Threat Landscape

Over 300,000 user credentials are posted on Pastebin annually, averaging 1,000 username/password pairs per leak. Paste sites serve three primary threat intelligence purposes: early breach detection (credentials appear on paste sites before dark web), threat actor profiling (actors use paste sites for C2 configuration, data staging, tool sharing), and malware discovery (encoded payloads, configuration files, C2 addresses).

Monitoring Approaches

Active monitoring queries paste site APIs or scraping endpoints at regular intervals. The Pastebin Scraping API provides real-time access to new public pastes. For GitHub, the search API allows monitoring Gists and repository commits for exposed secrets. Passive monitoring uses services like IntelX, Dehashed, or Have I Been Pwned that aggregate paste site data.

Credential Pattern Detection

Effective monitoring uses regex patterns for email:password combinations, API keys (AWS, Azure, GCP, Stripe, Twilio), database connection strings, private keys (SSH, PGP), JWT tokens, and internal hostnames/URLs. Organization-specific keywords (domain names, product names, employee names) reduce false positives.

Workflow

Step 1: Pastebin Scraping API Monitor

import requests
import re
import json
import time
from datetime import datetime

class PastebinMonitor:
    SCRAPING_URL = "https://scrape.pastebin.com/api_scraping.php"
    RAW_URL = "https://scrape.pastebin.com/api_scrape_item.php"

    def __init__(self, keywords, output_dir="paste_alerts"):
        self.keywords = [k.lower() for k in keywords]
        self.output_dir = output_dir
        self.seen_keys = set()
        self.credential_patterns = {
            "email_password": re.compile(
                r'[\w.+-]+@[\w-]+\.[\w.]+[\s:;|,]+[\S]{6,}', re.IGNORECASE),
            "aws_key": re.compile(
                r'AKIA[0-9A-Z]{16}'),
            "aws_secret": re.compile(
                r'[0-9a-zA-Z/+=]{40}'),
            "github_token": re.compile(
                r'ghp_[0-9a-zA-Z]{36}'),
            "slack_token": re.compile(
                r'xox[baprs]-[0-9a-zA-Z-]+'),
            "private_key": re.compile(
                r'-----BEGIN (?:RSA |EC |DSA )?PRIVATE KEY-----'),
            "jwt_token": re.compile(
                r'eyJ[A-Za-z0-9-_]+\.eyJ[A-Za-z0-9-_]+\.[A-Za-z0-9-_]+'),
            "connection_string": re.compile(
                r'(?:mongodb|postgres|mysql|redis)://[^\s]+'),
            "api_key_generic": re.compile(
                r'(?:api[_-]?key|apikey|access[_-]?token)[\s]*[=:]\s*["\']?[\w-]{20,}',
                re.IGNORECASE),
        }

    def fetch_recent_pastes(self, limit=100):
        """Fetch recent public pastes from Pastebin Scraping API."""
        params = {"limit": limit}
        try:
            resp = requests.get(self.SCRAPING_URL, params=params, timeout=30)
            if resp.status_code == 200:
                pastes = resp.json()
                print(f"[+] Fetched {len(pastes)} recent pastes")
                return pastes
            else:
                print(f"[-] API error: {resp.status_code}")
                return []
        except Exception as e:
            print(f"[-] Fetch error: {e}")
            return []

    def get_paste_content(self, paste_key):
        """Get the raw content of a paste."""
        params = {"i": paste_key}
        try:
            resp = requests.get(self.RAW_URL, params=params, timeout=15)
            if resp.status_code == 200:
                return resp.text
            return ""
        except Exception:
            return ""

    def analyze_paste(self, content, paste_metadata):
        """Analyze paste content for credentials and keywords."""
        findings = {
            "keyword_matches": [],
            "credential_matches": {},
            "severity": "low",
        }

        content_lower = content.lower()

        # Check keywords
        for keyword in self.keywords:
            if keyword in content_lower:
                count = content_lower.count(keyword)
                findings["keyword_matches"].append({
                    "keyword": keyword,
                    "count": count,
                })

        # Check credential patterns
        for pattern_name, pattern in self.credential_patterns.items():
            matches = pattern.findall(content)
            if matches:
                findings["credential_matches"][pattern_name] = {
                    "count": len(matches),
                    "samples": matches[:3],
                }

        # Calculate severity
        cred_count = sum(
            m["count"] for m in findings["credential_matches"].values()
        )
        if findings["keyword_matches"] and cred_count > 0:
            findings["severity"] = "critical"
        elif findings["keyword_matches"]:
            findings["severity"] = "high"
        elif cred_count > 10:
            findings["severity"] = "high"
        elif cred_count > 0:
            findings["severity"] = "medium"

        return findings

    def monitor_loop(self, interval=120, iterations=None):
        """Continuous monitoring loop."""
        count = 0
        while iterations is None or count < iterations:
            pastes = self.fetch_recent_pastes()
            alerts = []

            for paste in pastes:
                paste_key = paste.get("key", "")
                if paste_key in self.seen_keys:
                    continue
                self.seen_keys.add(paste_key)

                content = self.get_paste_content(paste_key)
                if not content:
                    continue

                findings = self.analyze_paste(content, paste)
                if findings["severity"] != "low":
                    alert = {
                        "paste_key": paste_key,
                        "title": paste.get("title", "Untitled"),
                        "user": paste.get("user", "Anonymous"),
                        "date": paste.get("date", ""),
                        "size": paste.get("size", 0),
                        "url": f"https://pastebin.com/{paste_key}",
                        "findings": findings,
                        "detected_at": datetime.now().isoformat(),
                    }
                    alerts.append(alert)
                    print(f"  [ALERT-{findings['severity'].upper()}] "
                          f"{paste_key}: {findings['keyword_matches']}")

            if alerts:
                self._save_alerts(alerts)

            count += 1
            if iterations is None or count < iterations:
                time.sleep(interval)

        return alerts

    def _save_alerts(self, alerts):
        """Save alerts to JSON file."""
        filename = f"{self.output_dir}/alerts_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
        import os
        os.makedirs(self.output_dir, exist_ok=True)
        with open(filename, "w") as f:
            json.dump(alerts, f, indent=2)
        print(f"[+] Saved {len(alerts)} alerts to {filename}")

monitor = PastebinMonitor(
    keywords=["mycompany.com", "internal-project", "employee-name"],
)
alerts = monitor.monitor_loop(interval=120, iterations=5)

Step 2: GitHub Gist and Code Search Monitoring

class GitHubSecretMonitor:
    def __init__(self, github_token, org_keywords):
        self.token = github_token
        self.keywords = org_keywords
        self.headers = {
            "Authorization": f"token {github_token}",
            "Accept": "application/vnd.github.v3+json",
        }

    def search_code(self, query, per_page=30):
        """Search GitHub code for leaked secrets."""
        url = "https://api.github.com/search/code"
        params = {"q": query, "per_page": per_page}
        resp = requests.get(url, headers=self.headers, params=params)
        if resp.status_code == 200:
            results = resp.json().get("items", [])
            print(f"[+] GitHub code search: {len(results)} results for '{query}'")
            return results
        return []

    def search_gists(self, keyword):
        """Search public Gists for sensitive data."""
        url = "https://api.github.com/gists/public"
        params = {"per_page": 100}
        resp = requests.get(url, headers=self.headers, params=params)
        matches = []
        if resp.status_code == 200:
            gists = resp.json()
            for gist in gists:
                description = (gist.get("description") or "").lower()
                files = gist.get("files", {})
                for filename, file_info in files.items():
                    if keyword.lower() in description or keyword.lower() in filename.lower():
                        matches.append({
                            "gist_id": gist["id"],
                            "description": gist.get("description", ""),
                            "filename": filename,
                            "url": gist["html_url"],
                            "created_at": gist["created_at"],
                        })
        return matches

    def monitor_org_secrets(self, org_domain):
        """Monitor for organization secrets leaked on GitHub."""
        queries = [
            f'"{org_domain}" password',
            f'"{org_domain}" api_key',
            f'"{org_domain}" secret',
            f'"{org_domain}" token',
            f'"{org_domain}" credentials',
        ]
        all_findings = []
        for query in queries:
            results = self.search_code(query)
            for result in results:
                all_findings.append({
                    "query": query,
                    "repo": result.get("repository", {}).get("full_name", ""),
                    "path": result.get("path", ""),
                    "url": result.get("html_url", ""),
                    "score": result.get("score", 0),
                })
            time.sleep(10)  # GitHub rate limiting
        return all_findings

gh_monitor = GitHubSecretMonitor("YOUR_GITHUB_TOKEN", ["mycompany.com"])
findings = gh_monitor.monitor_org_secrets("mycompany.com")

Step 3: Alert and Incident Response Integration

def generate_credential_leak_alert(alert_data):
    """Generate incident alert for credential leak detection."""
    alert = {
        "title": f"Credential Leak Detected - {alert_data.get('severity', 'unknown').upper()}",
        "source": alert_data.get("url", ""),
        "detected_at": alert_data.get("detected_at", ""),
        "severity": alert_data.get("severity", "medium"),
        "summary": f"Paste containing organization keywords and credentials found",
        "keyword_matches": alert_data.get("findings", {}).get("keyword_matches", []),
        "credential_types": list(alert_data.get("findings", {}).get("credential_matches", {}).keys()),
        "recommended_actions": [
            "Verify if leaked credentials are valid",
            "Force password reset for affected accounts",
            "Rotate exposed API keys and tokens",
            "Check access logs for unauthorized usage",
            "Report paste for takedown",
            "Update monitoring keywords if new patterns found",
        ],
    }
    return alert

Validation Criteria

Pastebin Scraping API queried successfully with rate limiting
Credential patterns detected (email:password, API keys, private keys)
Organization-specific keywords matched with context
GitHub code search identifies exposed secrets
Alerts generated with severity classification
Integration with incident response workflow

References

About this skill

Automatic

Security & AppSec pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

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

mukul975 / Anthropic-Cybersecurity-Skills

skills/performing-paste-site-monitoring-for-credentials/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/1/2026Version v1Bundled 2 files · scripts

Illustrative Want to see what performing-paste-site-monitoring-for-credentials 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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performing-dark-web-monitoring-for-threatsDark web monitoring involves systematically scanning Tor hidden services, underground forums, paste sites, and dark web marketplaces to identify threats targeting an organization, including leaked creClose match+17 pts pass-rate lift

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SkillScore

Safety

What it changes

Track record

Use this skill

Performing Paste Site Monitoring for Credentials

Overview

When to Use

Prerequisites

Key Concepts

Paste Site Threat Landscape

Monitoring Approaches

Credential Pattern Detection

Workflow

Step 1: Pastebin Scraping API Monitor

Step 2: GitHub Gist and Code Search Monitoring

Step 3: Alert and Incident Response Integration

Validation Criteria

References

SkillSafety

Source

Other skills for the same job

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