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Sixtyfour AI — People & Company Intelligence · rxhxm | DecimalAI

Sales & Supportby rxhxm

Sixtyfour AI — People & Company Intelligence

People and company intelligence via the Sixtyfour AI API. AI research agents that read the live web — not static databases — to return structured, confidence-scored profiles.

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

27%91%of test cases passed +64 pts

Without skill6 of 22

With skill20 of 22

15 fixed5 passed either way1 still failing1 broke

Measured on gemini-3.6-flash8/3/2026v1

Track record

Agent runs: 0; All time

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 rxhxm-sixtyfour
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("rxhxm-sixtyfour")
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 rxhxm-sixtyfour

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/rxhxm-sixtyfour/SKILL.md https://app.decimal.ai/s/rxhxm-sixtyfour/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/rxhxm-sixtyfour/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/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/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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("rxhxm-sixtyfour")
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/rxhxm-sixtyfour/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/rxhxm-sixtyfour/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 1 other file is 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 rxhxm-sixtyfour

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/rxhxm-sixtyfour/SKILL.md https://app.decimal.ai/s/rxhxm-sixtyfour/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/rxhxm-sixtyfour/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/rxhxm-sixtyfour/SKILL.md

Read the summary as JSON

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

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/3/2026
Source: rxhxm/sixtyfour-skill ↗
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

Sixtyfour AI — People & Company Intelligence

AI research agents that investigate people and companies across the live web, returning structured, confidence-scored data. 93% accuracy — benchmarked against Clay (66%), Apollo (72%), and ZoomInfo.

Base URL: https://api.sixtyfour.ai
Auth header: x-api-key: YOUR_API_KEY (all requests)
Free tier: 50 deep researches on signup — app.sixtyfour.ai (Google sign-in, API key available immediately)
Docs: docs.sixtyfour.ai
OpenAPI spec: api.sixtyfour.ai/openapi.json

Setup

# 1. Sign up at https://app.sixtyfour.ai (Google sign-in)
# 2. Sidebar → Keys → Create new key
export SIXTYFOUR_API_KEY="your_key_here"

API Quick Reference

Endpoint	Method	Description	Sync/Async
`/enrich-lead`	POST	Full person profile from name + company	Both
`/enrich-company`	POST	Deep company research + people discovery	Both
`/find-email`	POST	Professional ($0.05) or personal ($0.20) email	Both
`/find-phone`	POST	Phone number discovery	Both
`/qa-agent`	POST	Score/qualify leads against custom criteria	Both
`/search/start-deep-search`	POST	Find people/companies via natural language	Async
`/search/start-filter-search`	POST	Structured filter search (skips LLM parsing)	Sync
`/workflows/run`	POST	Execute batch enrichment pipelines	Async

All enrichment endpoints have async variants (append -async) returning task_id for polling via GET /job-status/{task_id}. Use async for production workloads.

Enrich Lead

The core endpoint. Give it a name — get back a full profile with any fields you define.

curl -X POST "https://api.sixtyfour.ai/enrich-lead" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "lead_info": {
      "name": "Jane Doe",
      "company": "Acme Corp"
    },
    "struct": {
      "full_name": "Full name",
      "title": "Current job title",
      "seniority": "Seniority level (C-suite, VP, Director, Manager, IC)",
      "department": "Department or function",
      "email": "Work email address",
      "personal_email": "Personal email address",
      "phone": "Phone number",
      "linkedin": "LinkedIn profile URL",
      "location": "City and state",
      "company_name": "Current company",
      "company_size": {"description": "Approximate employee count", "type": "int"},
      "tech_stack": {"description": "Tools and technologies they use daily", "type": "list[str]"},
      "funding_stage": "Company latest funding stage and amount",
      "pain_points": {"description": "Likely challenges based on role and company stage", "type": "list[str]"},
      "social_profiles": {"description": "Twitter, GitHub, personal blog URLs", "type": "list[str]"},
      "recent_activity": "Notable recent posts, talks, or job changes"
    },
    "research_plan": "Check LinkedIn profile, company website about page, Twitter, GitHub, and any recent conference talks or blog posts."
  }'

Response

{
  "structured_data": {
    "full_name": "Jane Doe",
    "title": "VP of Engineering",
    "seniority": "VP",
    "department": "Engineering",
    "email": "[email protected]",
    "phone": "+1-555-0123",
    "linkedin": "https://linkedin.com/in/janedoe",
    "tech_stack": ["React", "Python", "AWS", "Terraform"],
    "funding_stage": "Series B, $45M (2025)",
    "pain_points": ["Scaling engineering team post-Series B", "Migrating legacy infrastructure"]
  },
  "notes": "Research narrative with sources...",
  "references": {
    "https://linkedin.com/in/janedoe": "LinkedIn profile",
    "https://acme.com/about": "Company about page"
  },
  "confidence_score": 9.2
}

Key Parameters

Parameter	Type	Required	Description
`lead_info`	object	Yes	Known data: `name`, `company`, `linkedin_url`, `email`, `domain`, etc.
`struct`	object	Yes	Fields to collect. Value is either a plain-English description string or `{"description": "...", "type": "str\|int\|float\|bool\|list[str]\|dict"}`
`research_plan`	string	No	Guides where the agent looks — specific sources, methodology

Timeouts: P95 ~5 min, max ~10 min. Set client timeout to 15+ min or use /enrich-lead-async.

Enrich Company

Deep company research with optional people discovery.

curl -X POST "https://api.sixtyfour.ai/enrich-company" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "target_company": {
      "company_name": "Stripe",
      "website": "stripe.com"
    },
    "struct": {
      "description": "One-line company description",
      "employee_count": {"description": "Approximate headcount", "type": "int"},
      "tech_stack": {"description": "Key technologies used", "type": "list[str]"},
      "recent_funding": "Most recent funding round, amount, and date",
      "hiring_signals": {"description": "Open roles indicating growth areas", "type": "list[str]"},
      "competitors": {"description": "Main competitors", "type": "list[str]"}
    },
    "find_people": true,
    "people_focus_prompt": "Find the VP of Engineering and CTO",
    "lead_struct": {
      "name": "Full name",
      "title": "Job title",
      "linkedin": "LinkedIn URL",
      "email": "Work email"
    }
  }'

People Discovery Parameters

Parameter	Type	Description
`find_people`	bool	Enable people discovery at the company
`people_focus_prompt`	string	Describe who to find (role, department, seniority)
`lead_struct`	object	Fields to return per person found (same format as `struct`)

Each person returned includes a score (0-10) for relevance to people_focus_prompt.

Find Email

# Professional email ($0.05 per call)
curl -X POST "https://api.sixtyfour.ai/find-email" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "lead": {"name": "Saarth Shah", "company": "Sixtyfour AI"},
    "mode": "PROFESSIONAL"
  }'

Response: {"email": [["[email protected]", "OK", "COMPANY"]], "cost_cents": 5}

# Personal email ($0.20 per call)
curl -X POST "https://api.sixtyfour.ai/find-email" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "lead": {"name": "Jane Doe", "company": "Acme Corp"},
    "mode": "PERSONAL"
  }'

Email field format: [["[email protected]", "OK|UNKNOWN", "COMPANY|PERSONAL"]]

Bulk: Use /find-email-bulk-async with {"leads": [...]} for up to 100 leads.

Find Phone

curl -X POST "https://api.sixtyfour.ai/find-phone" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "lead": {
      "name": "John Doe",
      "company": "Example Corp",
      "linkedin_url": "https://linkedin.com/in/johndoe"
    }
  }'

Provide as much context as possible (name, company, LinkedIn, email, domain) for best hit rate.

Bulk: Use /find-phone-bulk-async for up to 100 leads, or /enrich-dataframe with CSV:

curl -X POST "https://api.sixtyfour.ai/enrich-dataframe" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"csv_data": "name,company\nJohn Doe,Example Corp", "enrichment_type": "phone"}'

QA Agent — Score & Qualify Leads

Evaluate enriched data against custom criteria. Returns scores and reasoning.

curl -X POST "https://api.sixtyfour.ai/qa-agent" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "data": {
      "name": "Alex Johnson",
      "title": "VP Engineering",
      "company": "TechStartup",
      "funding": "Series B",
      "tech_stack": ["React", "AWS", "PostgreSQL"]
    },
    "qualification_criteria": [
      {"criteria_name": "Seniority", "description": "VP level or above", "weight": 10.0, "threshold": 8.0},
      {"criteria_name": "Company Stage", "description": "Series A-C, actively growing", "weight": 8.0},
      {"criteria_name": "Tech Fit", "description": "Uses modern web stack", "weight": 6.0}
    ],
    "struct": {
      "overall_score": {"description": "Composite score 0-10", "type": "float"},
      "verdict": "ACCEPT or REJECT",
      "reasoning": "Why this lead does or does not qualify"
    }
  }'

Optional: add "references": [{"url": "https://...", "description": "Company blog"}] for additional context.

Search — Find People or Companies

Deep Search (natural language, async)

# Start search
curl -X POST "https://api.sixtyfour.ai/search/start-deep-search" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "VP of Engineering at Series B SaaS startups in New York", "max_results": 100}'

# Returns: {"task_id": "abc123", "status": "queued"}

# Poll (every 10-15s)
curl "https://api.sixtyfour.ai/search/deep-search-status/abc123" \
  -H "x-api-key: $SIXTYFOUR_API_KEY"

# Download results (when status = "completed", use resource_handle_id)
curl "https://api.sixtyfour.ai/search/download?resource_handle_id=xyz789" \
  -H "x-api-key: $SIXTYFOUR_API_KEY"
# Returns signed URL (expires 15 min) → CSV download

Filter Search (structured, synchronous)

curl -X POST "https://api.sixtyfour.ai/search/start-filter-search" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"filters": {"title": "VP Engineering", "location": "New York", "company_size": "50-200"}}'
# Returns: {"resource_handle_id": "...", "total_results": 150, "exported_count": 150}

Workflows — Batch Enrichment Pipelines

Chain blocks into reusable pipelines. Trigger via API with webhook payloads.

# List available workflow blocks
curl "https://api.sixtyfour.ai/workflows/blocks" \
  -H "x-api-key: $SIXTYFOUR_API_KEY"

# Run a workflow (webhook-triggered)
curl -X POST "https://api.sixtyfour.ai/workflows/run?workflow_id=YOUR_WORKFLOW_ID" \
  -H "x-api-key: $SIXTYFOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"webhook_payload": [{"company_name": "Acme", "website": "acme.com"}]}'

# Monitor progress (poll every 5-10s)
curl "https://api.sixtyfour.ai/workflows/runs/RUN_ID/live_status" \
  -H "x-api-key: $SIXTYFOUR_API_KEY"

# Download results
curl "https://api.sixtyfour.ai/workflows/runs/RUN_ID/results/download-links" \
  -H "x-api-key: $SIXTYFOUR_API_KEY"

Block types: webhook, read_csv, enrich_company, enrich_lead, find_email, find_phone, qa_agent

Manage workflows: GET /workflows (list), POST /workflows/create_workflow, POST /workflows/update_workflow, POST /workflows/delete_workflow

Webhooks — Async Notifications

Add "webhook_url" to any async request body:

{"lead_info": {...}, "struct": {...}, "webhook_url": "https://your-server.com/hook"}

Sixtyfour POSTs {task_id, status, task_type, result} on completion. 5 retries with exponential backoff.

Common Patterns

Signup enrichment: New user email → /enrich-lead with role, tech stack, funding fields → push to CRM
CRM backfill: Export contacts → /enrich-lead-async in parallel → poll → download enriched data
Lead scoring pipeline: /enrich-lead → /qa-agent with custom ICP criteria → route by verdict
Prospect list building: /search/start-deep-search with ICP description → CSV → /find-email per lead
Account intelligence: /enrich-company with find_people: true → weekly Slack digest of changes

Error Handling & Rate Limits

Rate limit: 500 requests/min per API key
Errors: {"error": "ErrorType", "message": "Details"}
HTTP codes: 400 (bad request), 401 (invalid key), 429 (rate limited), 500 (retry)
Async polling: Use GET /job-status/{task_id} — statuses: pending → processing → completed | failed

MCP Server

For Claude Desktop, Cursor, Windsurf, or any MCP client:

{
  "mcpServers": {
    "sixtyfour": {
      "command": "npx",
      "args": ["-y", "sixtyfour-mcp"],
      "env": {"SIXTYFOUR_API_KEY": "your_key"}
    }
  }
}

Then ask naturally: "Find the email and phone number for the CTO of Stripe" — the assistant calls Sixtyfour automatically.

Support: [email protected] | docs.sixtyfour.ai | app.sixtyfour.ai

When it activates

Automatically, when a task matches. Use when you need to: (1) enrich a lead with full profile data (name, title, email, phone, LinkedIn, tech stack, funding, pain points — up to 50 custom fields), (2) research a company (team size, tech stack, funding rounds, hiring signals, key people), (3) find someone's professional or personal email address, (4) find phon

About this skill

Automatic

Sales & GTM pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/1/2026

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

rxhxm / sixtyfour-skill

SKILL.md

View on GitHub ↗

License MITImported 8/1/2026Version v1Bundled 1 file

Illustrative Want to see what rxhxm/sixtyfour would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

gooseworks-ai/lead-enrichment-sixtyfourAI-powered lead enrichment - find emails, phones, and enrich company/lead dataClose match+55 pts pass-rate lift

Related skills

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

From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

Sixtyfour AI — People & Company Intelligence

Setup

API Quick Reference

Enrich Lead

Response

Key Parameters

Enrich Company

People Discovery Parameters

Find Email

Find Phone

QA Agent — Score & Qualify Leads

Search — Find People or Companies

Deep Search (natural language, async)

Filter Search (structured, synchronous)

Workflows — Batch Enrichment Pipelines

Webhooks — Async Notifications

Common Patterns

Error Handling & Rate Limits

MCP Server

When it activates

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry