Ready to supercharge your AI agents?

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

Single-Cell Genomics and Expression Matrix Analysis · FreedomIntelligence | DecimalAI

Scienceby FreedomIntelligence

Single-Cell Genomics and Expression Matrix Analysis

Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB),

Never made a case worse

SkillScore

SkillScore22/100

Test results 22

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

4%22%of test cases passed +17 pts

Without skill1 of 23

With skill5 of 23

4 fixed1 passed either way18 still failing0 broke

Measured on gemini-3.6-flash7/26/2026v122 comparable

Track record

Agent runs: 12; 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 tooluniverse-single-cell
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("tooluniverse-single-cell")
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 tooluniverse-single-cell

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/tooluniverse-single-cell/SKILL.md https://app.decimal.ai/s/tooluniverse-single-cell/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tooluniverse-single-cell/SKILL.md

This gets SKILL.md only. The skill's 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/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/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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("tooluniverse-single-cell")
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/tooluniverse-single-cell/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tooluniverse-single-cell/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 10 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 tooluniverse-single-cell

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/tooluniverse-single-cell/SKILL.md https://app.decimal.ai/s/tooluniverse-single-cell/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tooluniverse-single-cell/SKILL.md

This gets SKILL.md only. The skill's 10 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/tooluniverse-single-cell/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tooluniverse-single-cell.json

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/26/2026
Source: FreedomIntelligence/OpenClaw-Medical-Skills ↗
License: Not recorded

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

Single-Cell Genomics and Expression Matrix Analysis

Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse. Designed for both full scRNA-seq workflows (raw counts to annotated cell types) and targeted expression-level analyses (per-cell-type DE, correlation, ANOVA, clustering).

IMPORTANT: This skill handles complex multi-workflow analysis. Most implementation details have been moved to references/ for progressive disclosure. This document focuses on high-level decision-making and workflow orchestration.

When to Use This Skill

Apply when users:

Have scRNA-seq data (h5ad, 10X, CSV count matrices) and want analysis
Ask about cell type identification, clustering, or annotation
Need differential expression analysis by cell type or condition
Want gene-expression correlation analysis (e.g., gene length vs expression by cell type)
Ask about PCA, UMAP, t-SNE for expression data
Need Leiden/Louvain clustering on expression matrices
Want statistical comparisons between cell types (t-test, ANOVA, fold change)
Ask about marker genes for cell populations
Need batch correction (Harmony, combat)
Want trajectory or pseudotime analysis
Ask about cell-cell communication (ligand-receptor interactions)
Questions mention "single-cell", "scRNA-seq", "cell type", "h5ad"
Questions involve immune cell types (CD4, CD8, CD14, CD19, monocytes, etc.)

BixBench Coverage: 18+ questions across 5 projects (bix-22, bix-27, bix-31, bix-33, bix-36)

NOT for (use other skills instead):

Bulk RNA-seq DESeq2 analysis only → Use tooluniverse-rnaseq-deseq2
Gene enrichment only (no expression data) → Use tooluniverse-gene-enrichment
VCF/variant analysis → Use tooluniverse-variant-analysis
Statistical modeling (regression, survival) → Use tooluniverse-statistical-modeling

Core Principles

Data-first approach - Load, inspect, and validate data before any analysis
AnnData-centric - All data flows through anndata objects for consistency
Cell type awareness - Many questions require per-cell-type subsetting and analysis
Statistical rigor - Proper normalization, multiple testing correction, effect sizes
Scanpy standard pipeline - Follow established best practices for scRNA-seq
Flexible input - Handle h5ad, 10X, CSV/TSV, pre-processed and raw data
Question-driven - Parse what the user is actually asking and extract the specific answer
Enrichment integration - Chain DE results into GO/KEGG/Reactome enrichment when requested
Large dataset support - Efficient handling of datasets with >100k cells

Required Python Packages

# Core (MUST be installed)
import scanpy as sc
import anndata as ad
import pandas as pd
import numpy as np
from scipy import stats
from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
from scipy.spatial.distance import pdist
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.multitest import multipletests

# Enrichment (for GO/KEGG/Reactome follow-up)
import gseapy as gp

# Optional
import harmonypy  # batch correction

Installation:

pip install scanpy anndata leidenalg umap-learn harmonypy gseapy pandas numpy scipy scikit-learn statsmodels

High-Level Workflow Decision Tree

START: User question about scRNA-seq data
│
├─ Q1: What type of analysis is needed?
│  │
│  ├─ FULL PIPELINE (raw counts → annotated clusters)
│  │  └─ Workflow: QC → Normalize → HVG → PCA → Cluster → Annotate → DE
│  │     See: references/scanpy_workflow.md
│  │
│  ├─ DIFFERENTIAL EXPRESSION (per-cell-type comparison)
│  │  └─ Workflow: Load → Normalize → Per-CT DE → Report
│  │     Pattern: Most common BixBench pattern (bix-33)
│  │     See: Section "Per-Cell-Type Differential Expression" below
│  │
│  ├─ CORRELATION ANALYSIS (gene property vs expression)
│  │  └─ Workflow: Load → Filter genes → Compute correlation
│  │     Pattern: Gene length vs expression (bix-22)
│  │     See: Section "Statistical Analysis on Expression Data" below
│  │
│  ├─ CLUSTERING & PCA (expression matrix analysis)
│  │  └─ Workflow: Load → Transform → PCA/Cluster → Report
│  │     See: references/clustering_guide.md
│  │
│  ├─ CELL COMMUNICATION (ligand-receptor interactions)
│  │  └─ Workflow: Load → Get L-R pairs → Score → Identify signaling
│  │     See: references/cell_communication.md (DETAILED)
│  │
│  └─ TRAJECTORY ANALYSIS (pseudotime)
│     └─ Workflow: Load → Normalize → Trajectory → Pseudotime
│        See: references/trajectory_analysis.md
│
├─ Q2: What data format is available?
│  ├─ h5ad file → sc.read_h5ad() → Check contents (counts, metadata, clusters)
│  ├─ 10X files → sc.read_10x_mtx() or sc.read_10x_h5()
│  ├─ CSV/TSV → pd.read_csv() → Convert to AnnData (check orientation!)
│  └─ Other → See: references/scanpy_workflow.md "Data Loading"
│
└─ Q3: Are there pre-computed results to use?
   ├─ Has cell type annotations → Skip clustering, go to analysis
   ├─ Has PCA/UMAP → Skip dimensionality reduction
   ├─ Has DE results → Skip DE, analyze results
   └─ Raw counts only → Full pipeline needed

Common Analysis Patterns (BixBench)

Pattern 1: Per-Cell-Type Differential Expression

Question: "Which immune cell type has the most DEGs after treatment?"

Workflow:

import scanpy as sc

# Load and normalize
adata = sc.read_h5ad("data.h5ad")
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)

# Per-cell-type DE
cell_types = adata.obs['cell_type'].unique()
de_results = {}

for ct in cell_types:
    adata_ct = adata[adata.obs['cell_type'] == ct].copy()

    # Check sufficient cells
    n_treat = (adata_ct.obs['condition'] == 'treatment').sum()
    n_ctrl = (adata_ct.obs['condition'] == 'control').sum()
    if n_treat < 3 or n_ctrl < 3:
        continue

    # Run DE
    sc.tl.rank_genes_groups(adata_ct, groupby='condition',
                             groups=['treatment'], reference='control',
                             method='wilcoxon')
    df = sc.get.rank_genes_groups_df(adata_ct, group='treatment')

    # Count significant
    sig = df[df['pvals_adj'] < 0.05]
    de_results[ct] = {'n_sig': len(sig), 'results': df}
    print(f"{ct}: {len(sig)} DEGs")

# Answer: Which has most?
top_ct = max(de_results, key=lambda x: de_results[x]['n_sig'])
print(f"Answer: {top_ct} ({de_results[top_ct]['n_sig']} DEGs)")

BixBench: bix-33

See: references/scanpy_workflow.md "Differential Expression"

Pattern 2: Gene Property vs Expression Correlation

Question: "What is the Pearson correlation between gene length and expression in CD4 T cells?"

Workflow:

import scanpy as sc
import pandas as pd
import numpy as np
from scipy import stats
from scipy.sparse import issparse

# Load data
adata = sc.read_h5ad("data.h5ad")

# Load gene annotations
gene_info = pd.read_csv("gene_info.tsv", sep='\t', index_col=0)
common = adata.var_names.intersection(gene_info.index)
adata.var['gene_length'] = gene_info.loc[common, 'gene_length'].reindex(adata.var_names)
adata.var['gene_type'] = gene_info.loc[common, 'gene_type'].reindex(adata.var_names)

# Filter to protein-coding genes
mask = adata.var['gene_type'] == 'protein_coding'
adata_pc = adata[:, mask].copy()

# Per-cell-type correlation
cell_types = ['CD4 T cells', 'CD8 T cells', 'CD14 Monocytes']  # etc.

for ct in cell_types:
    adata_ct = adata_pc[adata_pc.obs['cell_type'] == ct]

    # Mean expression per gene
    X = adata_ct.X.toarray() if issparse(adata_ct.X) else adata_ct.X
    mean_expr = np.mean(X, axis=0)
    gene_lengths = adata_ct.var['gene_length'].values

    # Remove NaN
    valid = ~np.isnan(gene_lengths) & ~np.isnan(mean_expr)

    # Pearson correlation
    r, p = stats.pearsonr(gene_lengths[valid], mean_expr[valid])
    print(f"{ct}: r = {r:.6f}, p = {p:.2e}, n = {valid.sum()} genes")

BixBench: bix-22

See: SKILL_OLD.md "Phase 6: Statistical Analysis on Expression Data"

Pattern 3: PCA on Expression Matrix

Question: "What percentage of variance is explained by PC1 after log10 transform?"

Workflow:

import pandas as pd
import numpy as np
from sklearn.decomposition import PCA

# Load expression matrix
df = pd.read_csv("expression.csv", index_col=0)

# Orient: samples as rows, genes as columns
if df.shape[0] > df.shape[1] * 5:
    df = df.T  # Genes were rows, transpose

# Log10 transform with pseudocount
X = np.log10(df.values + 1)

# Run PCA
n_components = min(X.shape[0], X.shape[1])
pca = PCA(n_components=n_components)
pca.fit(X)

# Variance explained
print(f"PC1: {pca.explained_variance_ratio_[0]*100:.2f}% variance")
print(f"PC1+PC2: {sum(pca.explained_variance_ratio_[:2])*100:.2f}%")
print(f"Top 10 PCs: {sum(pca.explained_variance_ratio_[:10])*100:.2f}%")

BixBench: bix-27

See: references/clustering_guide.md "PCA Analysis"

Pattern 4: Statistical Comparison Between Cell Types

Question: "What is the t-statistic comparing LFCs between CD4/CD8 and other cell types?"

Workflow:

from scipy import stats

# After running per-cell-type DE (Pattern 1):
# Extract LFCs for different cell type groups

# Group 1: CD4/CD8 cells
cd4_lfc = de_results['CD4 T cells']['results']['log2FoldChange'].values
cd8_lfc = de_results['CD8 T cells']['results']['log2FoldChange'].values
cd4_cd8_lfc = np.concatenate([cd4_lfc, cd8_lfc])

# Group 2: Other cells
other_lfc = []
for ct in ['CD14 Monocytes', 'NK cells', 'B cells']:
    other_lfc.append(de_results[ct]['results']['log2FoldChange'].values)
other_lfc = np.concatenate(other_lfc)

# Welch's t-test (unequal variances)
t_stat, p_val = stats.ttest_ind(cd4_cd8_lfc, other_lfc, equal_var=False)
print(f"t-statistic: {t_stat:.4f}")
print(f"p-value: {p_val:.4e}")

BixBench: bix-31

See: SKILL_OLD.md "Phase 6.3: T-Tests Between Groups"

Pattern 5: ANOVA Across Cell Types

Question: "What is the F-statistic for miRNA expression across immune cell types?"

Workflow:

import pandas as pd
from scipy import stats

# Load miRNA expression
df = pd.read_csv("mirna_expr.csv", index_col=0)
meta = pd.read_csv("metadata.csv", index_col=0)

# Exclude PBMCs
meta_filtered = meta[meta['cell_type'] != 'PBMC']
df_filtered = df[meta_filtered.index]

# Group by cell type
cell_types = meta_filtered['cell_type'].unique()
groups = {}
for ct in cell_types:
    samples = meta_filtered[meta_filtered['cell_type'] == ct].index
    groups[ct] = df_filtered[samples].values.flatten()

# One-way ANOVA
f_stat, p_val = stats.f_oneway(*groups.values())
print(f"F-statistic: {f_stat:.4f}")
print(f"p-value: {p_val:.4e}")

BixBench: bix-36

See: SKILL_OLD.md "Phase 6.4: ANOVA Across Groups"

Pattern 6: Cell-Cell Communication Analysis

Question: "Which ligand-receptor interactions are strongest between tumor and T cells?"

Workflow:

from tooluniverse import ToolUniverse

tu = ToolUniverse()
tu.load_tools()

# Step 1: Get ligand-receptor pairs from OmniPath
result = tu.run_tool(
    "OmniPath_get_ligand_receptor_interactions",
    databases="CellPhoneDB,CellChatDB"
)
lr_pairs = pd.DataFrame(result['data']['interactions'])

# Step 2: Filter to expressed pairs
# (genes present in dataset, mean expression > 0.05)
expressed_lr = lr_pairs[
    lr_pairs['source_genesymbol'].isin(adata.var_names) &
    lr_pairs['target_genesymbol'].isin(adata.var_names)
]

# Step 3: Score communication between cell types
# (mean ligand expr in sender * mean receptor expr in receiver)
communication_scores = score_cell_communication(
    adata, expressed_lr, cell_type_col='cell_type'
)

# Step 4: Filter to tumor-T cell interactions
tumor_tcell = communication_scores[
    ((communication_scores['sender'] == 'Tumor') &
     (communication_scores['receiver'].str.contains('T cell'))) |
    ((communication_scores['receiver'] == 'Tumor') &
     (communication_scores['sender'].str.contains('T cell')))
]

# Step 5: Top interactions
top_interactions = tumor_tcell.nlargest(20, 'score')
print(top_interactions[['sender', 'receiver', 'ligand', 'receptor', 'score']])

See: references/cell_communication.md (COMPLETE workflow with all helper functions)

Scanpy vs Seurat Equivalents

For users familiar with Seurat (R):

Operation	Seurat (R)	Scanpy (Python)
Load data	`Read10X()`	`sc.read_10x_mtx()`
Normalize	`NormalizeData()`	`sc.pp.normalize_total() + sc.pp.log1p()`
Find HVGs	`FindVariableFeatures()`	`sc.pp.highly_variable_genes()`
Scale	`ScaleData()`	`sc.pp.scale()`
PCA	`RunPCA()`	`sc.tl.pca()`
Neighbors	`FindNeighbors()`	`sc.pp.neighbors()`
Cluster	`FindClusters()`	`sc.tl.leiden()` or `sc.tl.louvain()`
UMAP	`RunUMAP()`	`sc.tl.umap()`
Find markers	`FindMarkers()`	`sc.tl.rank_genes_groups()`
DE test	`FindMarkers(test.use="wilcox")`	`method='wilcoxon'`
Batch correction	`RunHarmony()`	`harmonypy.run_harmony()`

See: references/seurat_workflow.md for complete Seurat → Scanpy translation

When to Use ToolUniverse Tools

Gene Annotation and Validation

HPA_search_genes_by_query: Search for cell-type marker genes
MyGene_query_genes / MyGene_batch_query: Gene ID conversion, gene info
ensembl_lookup_gene: Get Ensembl gene details
UniProt_get_function_by_accession: Protein function lookup

Cell-Cell Communication (NEW)

OmniPath_get_ligand_receptor_interactions: Get validated L-R pairs (CellPhoneDB, CellChatDB)
OmniPath_get_signaling_interactions: Downstream signaling cascades
OmniPath_get_complexes: Multi-subunit receptor composition
OmniPath_get_cell_communication_annotations: Pathway categories

Enrichment Analysis (Post-DE)

PANTHER_enrichment: GO enrichment (BP, MF, CC) with curation
STRING_functional_enrichment: Network-based enrichment
ReactomeAnalysis_pathway_enrichment: Curated Reactome pathways

See: references/cell_communication.md for complete OmniPath integration examples

Data Loading Best Practices

Critical: Matrix Orientation

AnnData expects: cells/samples as rows (obs), genes as columns (var)

import scanpy as sc
import pandas as pd
import anndata as ad

# Load h5ad (already oriented)
adata = sc.read_h5ad("data.h5ad")

# Load CSV/TSV (check orientation!)
df = pd.read_csv("counts.csv", index_col=0)

# Heuristic: If genes > samples by 5x, transpose
if df.shape[0] > df.shape[1] * 5:
    print("Transposing: genes were rows")
    df = df.T

adata = ad.AnnData(df)

Load Metadata

meta = pd.read_csv("metadata.csv", index_col=0)
# Align indices
common = adata.obs_names.intersection(meta.index)
adata = adata[common].copy()
for col in meta.columns:
    adata.obs[col] = meta.loc[common, col]

See: references/scanpy_workflow.md "Phase 1: Data Loading"

Quality Control Checklist

# QC metrics
adata.var['mt'] = adata.var_names.str.startswith(('MT-', 'mt-'))
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)

# Filter cells
sc.pp.filter_cells(adata, min_genes=200)  # Min genes per cell
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()  # Max mito %

# Filter genes
sc.pp.filter_genes(adata, min_cells=3)  # Min cells per gene

print(f"After QC: {adata.n_obs} cells x {adata.n_vars} genes")

See: references/scanpy_workflow.md "Phase 2: Quality Control"

Differential Expression Decision Tree

Q: What type of DE analysis?

Single-Cell DE (many cells per condition):
├─ Use: sc.tl.rank_genes_groups()
├─ Methods: wilcoxon (default), t-test, logreg
├─ Best for: Per-cell-type DE, marker gene finding
└─ See: references/scanpy_workflow.md "Differential Expression"

Pseudo-Bulk DE (aggregate counts by sample):
├─ Use: DESeq2 via PyDESeq2
├─ Best for: Sample-level comparisons, replicates
└─ See: SKILL_OLD.md "Phase 5.3: DESeq2-based DE"

Statistical Tests Only:
├─ Use: scipy.stats (ttest_ind, f_oneway, pearsonr)
├─ Best for: Correlation, ANOVA, t-tests on summaries
└─ See: "Statistical Analysis on Expression Data" below

Statistical Analysis on Expression Data

For BixBench questions requiring specific statistical tests:

Pearson/Spearman Correlation

from scipy import stats

# Gene property vs expression
r, p = stats.pearsonr(gene_lengths, mean_expression)
r_s, p_s = stats.spearmanr(gene_lengths, mean_expression)

T-Tests

# Welch's t-test (unequal variance)
t_stat, p_val = stats.ttest_ind(group1, group2, equal_var=False)

# Student's t-test (equal variance)
t_stat, p_val = stats.ttest_ind(group1, group2, equal_var=True)

ANOVA

# One-way ANOVA across multiple groups
f_stat, p_val = stats.f_oneway(group1, group2, group3, ...)

Multiple Testing Correction

from statsmodels.stats.multitest import multipletests

# Benjamini-Hochberg (FDR)
reject, pvals_adj, _, _ = multipletests(pvals, method='fdr_bh')

# Bonferroni
reject, pvals_adj, _, _ = multipletests(pvals, method='bonferroni')

See: SKILL_OLD.md "Phase 6: Statistical Analysis on Expression Data" for complete examples

Marker Gene Identification

# Find marker genes for each cluster
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')

# Get results for cluster 0
markers = sc.get.rank_genes_groups_df(adata, group='0')
top_markers = markers.head(10)

# Annotate clusters using markers
marker_dict = {
    'T cells': ['CD3D', 'CD3E', 'CD8A'],
    'B cells': ['CD19', 'MS4A1', 'CD79A'],
    'Monocytes': ['CD14', 'LYZ', 'S100A9'],
}
# Score and assign cell types

See: references/marker_identification.md for complete workflow

Batch Correction with Harmony

import harmonypy

# After PCA
sc.tl.pca(adata, n_comps=50)

# Run Harmony on PCA
ho = harmonypy.run_harmony(
    adata.obsm['X_pca'][:, :30],
    adata.obs,
    'batch',  # batch column
    random_state=0
)

# Store corrected PCs
adata.obsm['X_pca_harmony'] = ho.Z_corr.T

# Re-cluster on corrected PCs
sc.pp.neighbors(adata, use_rep='X_pca_harmony')
sc.tl.leiden(adata, resolution=0.5)
sc.tl.umap(adata)

See: references/scanpy_workflow.md "Batch Correction"

Report Generation

Always extract the specific answer to the user's question:

# Example: "Which cell type has the most DEGs?"
report = f"""
# Analysis Results

## Per-Cell-Type Differential Expression

| Cell Type | Significant DEGs (padj < 0.05) |
|-----------|-------------------------------|
{chr(10).join([f"| {ct} | {res['n_sig']} |" for ct, res in de_results.items()])}

## Answer

**{top_ct}** has the highest number of significantly differentially expressed
genes with **{de_results[top_ct]['n_sig']} DEGs** (Wilcoxon test, BH-corrected
p < 0.05).
"""

Troubleshooting Common Issues

Issue	Solution
`ModuleNotFoundError: leidenalg`	`pip install leidenalg`
Sparse matrix errors	Use `.toarray()`: `X = adata.X.toarray() if issparse(adata.X) else adata.X`
Wrong matrix orientation	Check: more genes than samples? Transpose if needed
NaN in correlation	Filter: `valid = ~np.isnan(x) & ~np.isnan(y)`
Too few cells for DE	Need >= 3 cells per condition per cell type
Gene names don't match	Use MyGene for ID conversion
Memory error (large datasets)	Use `sc.pp.highly_variable_genes()` to reduce features

See: references/troubleshooting.md for detailed solutions

Reference Documentation

Core Workflows:

references/scanpy_workflow.md - Complete scanpy pipeline (QC, normalize, PCA, cluster, DE)
references/seurat_workflow.md - Seurat → Scanpy translation guide
references/clustering_guide.md - Leiden, Louvain, hierarchical, bootstrap consensus
references/marker_identification.md - Marker genes, cell type annotation
references/trajectory_analysis.md - Pseudotime, trajectory inference

Advanced Topics:

references/cell_communication.md - Complete OmniPath/CellPhoneDB workflow (L-R interactions, signaling)
references/troubleshooting.md - Common errors, package issues, data format problems

Utility Scripts:

scripts/qc_metrics.py - QC calculations, filtering thresholds
scripts/normalize_data.py - Normalization methods
scripts/find_markers.py - Marker gene identification

Complete Workflow Example

import scanpy as sc

# 1. Load data
adata = sc.read_10x_h5("filtered_feature_bc_matrix.h5")

# 2. QC
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)

# 3. Normalize
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
adata.raw = adata.copy()

# 4. HVG + PCA
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.tl.pca(adata, n_comps=50)

# 5. Cluster
sc.pp.neighbors(adata, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5)
sc.tl.umap(adata)

# 6. Find markers
sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')
markers = sc.get.rank_genes_groups_df(adata, group='0')

# 7. Annotate (manual or automatic)
# 8. Per-cell-type DE (if conditions present)
# 9. Cell communication analysis (if needed)

See: references/scanpy_workflow.md for detailed explanations of each step

Summary

This skill provides:

Complete scRNA-seq pipeline (QC → clustering → annotation → DE)
Per-cell-type differential expression with multiple methods
Gene property correlation analysis by cell type
Statistical comparisons (t-test, ANOVA, correlation)
Expression matrix clustering (hierarchical, bootstrap consensus, PCA)
Cell-cell communication analysis (OmniPath, CellPhoneDB, CellChatDB)
Batch correction (Harmony, ComBat)
Trajectory inference and pseudotime
Integration with ToolUniverse for annotation and enrichment

BixBench Coverage: 18+ questions across 5 projects (bix-22, bix-27, bix-31, bix-33, bix-36)

For detailed workflows, see references/ directory.

About this skill

Automatic

Research 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

FreedomIntelligence / OpenClaw-Medical-Skills

skills/tooluniverse-single-cell/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v1Bundled 10 files · scripts

Illustrative Want to see what tooluniverse-single-cell 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.

Related skills

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

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Single-Cell Genomics and Expression Matrix Analysis

When to Use This Skill

Core Principles

Required Python Packages

High-Level Workflow Decision Tree

Common Analysis Patterns (BixBench)

Pattern 1: Per-Cell-Type Differential Expression

Pattern 2: Gene Property vs Expression Correlation

Pattern 3: PCA on Expression Matrix

Pattern 4: Statistical Comparison Between Cell Types

Pattern 5: ANOVA Across Cell Types

Pattern 6: Cell-Cell Communication Analysis

Scanpy vs Seurat Equivalents

When to Use ToolUniverse Tools

Gene Annotation and Validation

Cell-Cell Communication (NEW)

Enrichment Analysis (Post-DE)

Data Loading Best Practices

Critical: Matrix Orientation

Load Metadata

Quality Control Checklist

Differential Expression Decision Tree

Statistical Analysis on Expression Data

Pearson/Spearman Correlation

T-Tests

ANOVA

Multiple Testing Correction

Marker Gene Identification

Batch Correction with Harmony

Report Generation

Troubleshooting Common Issues

Reference Documentation

Complete Workflow Example

Summary

SkillSafety

Source

Other skills for the same job

Related skills

More in the registry