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Gene Enrichment and Pathway Analysis · FreedomIntelligence | DecimalAI

Scienceby FreedomIntelligence

Gene Enrichment and Pathway Analysis

Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni).

SkillScore

SkillScore18/100

Test results 18

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

0%18%of test cases passed +18 pts

Without skill0 of 22

With skill4 of 22

4 fixed0 passed either way18 still failing0 broke

Measured on gemini-3.6-flash7/26/2026v110 comparable

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 tooluniverse-gene-enrichment
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-gene-enrichment")
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-gene-enrichment

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-gene-enrichment/SKILL.md https://app.decimal.ai/s/tooluniverse-gene-enrichment/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tooluniverse-gene-enrichment/SKILL.md

This gets SKILL.md only. The skill's 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/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-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/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-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment")
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-gene-enrichment/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tooluniverse-gene-enrichment/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 6 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-gene-enrichment

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-gene-enrichment/SKILL.md https://app.decimal.ai/s/tooluniverse-gene-enrichment/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tooluniverse-gene-enrichment/SKILL.md

This gets SKILL.md only. The skill's 6 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-gene-enrichment/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tooluniverse-gene-enrichment.json

This gets SKILL.md only. The skill's 6 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

Gene Enrichment and Pathway Analysis

Perform comprehensive gene enrichment analysis including Gene Ontology (GO), KEGG, Reactome, WikiPathways, and MSigDB enrichment using both Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). Integrates local computation via gseapy with ToolUniverse pathway databases for cross-validated, publication-ready results.

IMPORTANT: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.

When to Use This Skill

Apply when users:

Ask about gene enrichment analysis (GO, KEGG, Reactome, etc.)
Have a gene list from differential expression, clustering, or any experiment
Want to know which biological processes, molecular functions, or cellular components are enriched
Need KEGG or Reactome pathway enrichment analysis
Ask about GSEA (Gene Set Enrichment Analysis) with ranked gene lists
Want over-representation analysis (ORA) with Fisher's exact test
Need multiple testing correction (Benjamini-Hochberg, Bonferroni)
Ask about enrichGO, gseapy, clusterProfiler-style analyses

NOT for (use other skills instead):

Network pharmacology / drug repurposing → Use tooluniverse-network-pharmacology
Disease characterization → Use tooluniverse-multiomic-disease-characterization
Single gene function lookup → Use tooluniverse-disease-research
Spatial omics analysis → Use tooluniverse-spatial-omics-analysis
Protein-protein interaction analysis only → Use tooluniverse-protein-interactions

Input Parameters

Parameter	Required	Description	Example
gene_list	Yes	List of gene symbols, Ensembl IDs, or Entrez IDs	`["TP53", "BRCA1", "EGFR"]`
organism	No	Organism (default: human). Supported: human, mouse, rat, fly, worm, yeast, zebrafish	`human`
analysis_type	No	`ORA` (default) or `GSEA`	`ORA`
enrichment_databases	No	Which databases to query. Default: all applicable	`["GO_BP", "GO_MF", "GO_CC", "KEGG", "Reactome"]`
gene_id_type	No	Input ID type: `symbol`, `ensembl`, `entrez`, `uniprot` (auto-detected if omitted)	`symbol`
p_value_cutoff	No	Significance threshold (default: 0.05)	`0.05`
correction_method	No	Multiple testing: `BH` (Benjamini-Hochberg, default), `bonferroni`, `fdr`	`BH`
background_genes	No	Custom background gene set (default: genome-wide)	`["GENE1", "GENE2", ...]`
ranked_gene_list	No	For GSEA: gene-to-score mapping (e.g., log2FC)	`{"TP53": 2.5, "BRCA1": -1.3, ...}`

Core Principles

Report-first approach - Create report file FIRST, then populate progressively
ID disambiguation FIRST - Detect and convert gene IDs before ANY enrichment
Multi-source validation - Run enrichment on at least 2 independent tools, cross-validate
Exact p-values - Report raw p-values AND adjusted p-values with correction method
Multiple testing correction - ALWAYS apply Benjamini-Hochberg unless user specifies otherwise
Gene set size filtering - Filter by min/max gene set size to avoid trivial/overly broad terms
Evidence grading - Grade enrichment sources T1-T4
Negative results documented - "No significant enrichment" is a valid finding
Source references - Every enrichment result must cite the tool/database/library used
Completeness checklist - Mandatory section at end showing analysis coverage

Decision Tree: ORA vs GSEA

Q: Do you have a ranked gene list (with scores/fold-changes)?
  YES → Use GSEA (gseapy.prerank)
        - Input: Gene-to-score mapping (e.g., log2FC)
        - Statistics: Running enrichment score, permutation test
        - Cutoff: FDR q-val < 0.25 (standard for GSEA)
        - Output: NES (Normalized Enrichment Score), lead genes
        See: references/gsea_workflow.md

  NO  → Use ORA (gseapy.enrichr)
        - Input: Gene list only
        - Statistics: Fisher's exact test, hypergeometric
        - Cutoff: Adjusted P-value < 0.05 (or user specified)
        - Output: P-value, adjusted P-value, overlap, odds ratio
        See: references/ora_workflow.md

Decision Tree: gseapy vs ToolUniverse Tools

Q: Which enrichment method should I use?

Primary Analysis (ALWAYS):
  ├─ gseapy.enrichr (ORA) OR gseapy.prerank (GSEA)
  │  - Most comprehensive (225+ Enrichr libraries)
  │  - GO (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB
  │  - All organisms supported
  │  - Returns: P-value, Adjusted P-value, Overlap, Genes
  │  See: references/enrichr_guide.md

Cross-Validation (REQUIRED for publication):
  ├─ PANTHER_enrichment [T1 - curated]
  │  - Curated GO enrichment
  │  - Multiple organisms (taxonomy ID)
  │  - GO BP, MF, CC, PANTHER pathways, Reactome
  │
  ├─ STRING_functional_enrichment [T2 - validated]
  │  - Returns ALL categories in one call
  │  - Filter by category: Process, Function, Component, KEGG, Reactome
  │  - Network-based enrichment
  │
  └─ ReactomeAnalysis_pathway_enrichment [T1 - curated]
     - Reactome curated pathways
     - Cross-species projection
     - Detailed pathway hierarchy

Additional Context (Optional):
  ├─ GO_get_term_by_id, QuickGO_get_term_detail (GO term details)
  ├─ Reactome_get_pathway, Reactome_get_pathway_hierarchy (pathway context)
  ├─ WikiPathways_search, WikiPathways_get_pathway (community pathways)
  └─ STRING_ppi_enrichment (network topology analysis)

Quick Start Workflow

Step 1: Create Report File (IMMEDIATE)

report_path = f"{analysis_name}_enrichment_report.md"
# Write header with placeholder sections
# Update progressively as analysis proceeds

Step 2: ID Conversion and Validation

from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()

# Detect ID type
gene_list = ["TP53", "BRCA1", "EGFR"]
# Auto-detect: ENSG* = Ensembl, numeric = Entrez, pattern = UniProt, else = Symbol

# Convert if needed (Ensembl/Entrez → Symbol)
result = tu.tools.MyGene_batch_query(
    gene_ids=gene_list,
    fields="symbol,entrezgene,ensembl.gene"
)
# Extract symbols from results

# Validate with STRING
mapped = tu.tools.STRING_map_identifiers(
    protein_ids=gene_symbols,
    species=9606  # human
)
# Use preferredName for canonical symbols

See: references/id_conversion.md for complete examples

Step 3: Primary Enrichment with gseapy

For ORA (gene list only):

import gseapy

# GO Biological Process
go_bp = gseapy.enrichr(
    gene_list=gene_symbols,
    gene_sets='GO_Biological_Process_2021',
    organism='human',
    outdir=None,
    no_plot=True,
    background=background_genes  # None = genome-wide
)
go_bp_sig = go_bp.results[go_bp.results['Adjusted P-value'] < 0.05]

For GSEA (ranked gene list):

import pandas as pd

# Ranked by log2FC
ranked_series = pd.Series(gene_to_score).sort_values(ascending=False)

gsea_result = gseapy.prerank(
    rnk=ranked_series,
    gene_sets='GO_Biological_Process_2021',
    outdir=None,
    no_plot=True,
    seed=42,
    min_size=5,
    max_size=500,
    permutation_num=1000
)
gsea_sig = gsea_result.res2d[gsea_result.res2d['FDR q-val'] < 0.25]

See:

references/ora_workflow.md for complete ORA examples
references/gsea_workflow.md for complete GSEA examples
references/enrichr_guide.md for all 225+ libraries

Step 4: Cross-Validation with ToolUniverse

# PANTHER [T1 - curated]
panther_bp = tu.tools.PANTHER_enrichment(
    gene_list=','.join(gene_symbols),  # comma-separated string
    organism=9606,
    annotation_dataset='GO:0008150'  # biological_process
)

# STRING [T2 - validated]
string_result = tu.tools.STRING_functional_enrichment(
    protein_ids=gene_symbols,
    species=9606
)
# Filter by category: Process, Function, Component, KEGG, Reactome

# Reactome [T1 - curated]
reactome_result = tu.tools.ReactomeAnalysis_pathway_enrichment(
    identifiers=' '.join(gene_symbols),  # space-separated
    page_size=50,
    include_disease=True
)

See: references/cross_validation.md for comparison strategies

Step 5: Report Compilation

## Results

### GO Biological Process (Top 10)
| Term | P-value | Adj. P-value | Overlap | Genes | Evidence |
|------|---------|-------------|---------|-------|----------|
| regulation of cell cycle (GO:0051726) | 1.2e-08 | 3.4e-06 | 12/45 | TP53;BRCA1;... | [T2] gseapy |

### Cross-Validation
| GO Term | gseapy FDR | PANTHER FDR | STRING FDR | Consensus |
|---------|-----------|-------------|-----------|-----------|
| GO:0051726 | 3.4e-06 | 2.1e-05 | 1.8e-05 | 3/3 ✓ |

### Completeness Checklist
- [x] ID Conversion (MyGene, STRING) - 95% mapped
- [x] GO BP (gseapy, PANTHER, STRING) - 24 significant terms
- [x] GO MF (gseapy, PANTHER, STRING) - 18 significant terms
- [x] GO CC (gseapy, PANTHER, STRING) - 12 significant terms
- [x] KEGG (gseapy, STRING) - 8 significant pathways
- [x] Reactome (gseapy, ReactomeAPI) - 15 significant pathways
- [x] Cross-validation - 12 consensus terms (2+ sources)

See: scripts/format_enrichment_output.py for automated formatting

Evidence Grading

Tier	Symbol	Criteria	Examples
T1	[T1]	Curated/experimental enrichment	PANTHER, Reactome Analysis Service
T2	[T2]	Computational enrichment, well-validated	gseapy ORA/GSEA, STRING functional enrichment
T3	[T3]	Text-mining/predicted enrichment	Enrichr non-curated libraries
T4	[T4]	Single-source annotation	Individual gene GO annotations from QuickGO

Supported Organisms

Organism	Taxonomy ID	gseapy	PANTHER	STRING	Reactome
Human	9606	Yes	Yes	Yes	Yes
Mouse	10090	Yes (`*_Mouse`)	Yes	Yes	Yes (projection)
Rat	10116	Limited	Yes	Yes	Yes (projection)
Fly	7227	Limited	Yes	Yes	Yes (projection)
Worm	6239	Limited	Yes	Yes	Yes (projection)
Yeast	4932	Limited	Yes	Yes	Yes

See: references/organism_support.md for organism-specific libraries

Common Patterns

Pattern 1: Standard DEG Enrichment (ORA)

Input: List of differentially expressed gene symbols
Flow: ID validation → gseapy ORA (GO + KEGG + Reactome) →
      PANTHER + STRING cross-validation → Report top enriched terms
Use: When you have unranked gene list from DESeq2/edgeR

Pattern 2: Ranked Gene List (GSEA)

Input: Gene-to-log2FC mapping from differential expression
Flow: Convert to ranked Series → gseapy GSEA (GO + KEGG + MSigDB) →
      Filter by FDR < 0.25 → Report NES and lead genes
Use: When you have fold-changes or other ranking metric

Pattern 3: BixBench Enrichment Question

Input: Specific question about enrichment (e.g., "What is the adjusted p-val for neutrophil activation?")
Flow: Parse question for gene list and library → Run gseapy with exact library →
      Find specific term → Report exact p-value and adjusted p-value
Use: When answering targeted questions about specific terms

Pattern 4: Multi-Organism Enrichment

Input: Gene list from mouse experiment
Flow: Use organism='mouse' for gseapy → organism=10090 for PANTHER/STRING →
      projection=True for Reactome human pathway mapping
Use: When working with non-human organisms

See: references/common_patterns.md for more examples

Troubleshooting

"No significant enrichment found":

Verify gene symbols are valid (STRING_map_identifiers)
Try different library versions (2021 vs 2023 vs 2025)
Try relaxing significance cutoff or use GSEA instead

"Gene not found" errors:

Check ID type and convert using MyGene_batch_query
Remove version suffixes from Ensembl IDs (ENSG00000141510.16 → ENSG00000141510)

"STRING returns all categories":

This is expected; filter by d['category'] == 'Process' after receiving results

See: references/troubleshooting.md for complete guide

Tool Reference

Primary Enrichment Tools

Tool	Input	Output	Use For
`gseapy.enrichr()`	gene_list, gene_sets, organism	`.results` DataFrame	ORA with 225+ libraries
`gseapy.prerank()`	rnk (ranked Series), gene_sets	`.res2d` DataFrame	GSEA analysis

Cross-Validation Tools

Tool	Key Parameters	Evidence Grade
`PANTHER_enrichment`	gene_list (comma-sep), organism, annotation_dataset	[T1]
`STRING_functional_enrichment`	protein_ids, species	[T2]
`ReactomeAnalysis_pathway_enrichment`	identifiers (space-sep), page_size	[T1]

ID Conversion Tools

Tool	Input	Output
`MyGene_batch_query`	gene_ids, fields	Symbol, Entrez, Ensembl mappings
`STRING_map_identifiers`	protein_ids, species	Preferred names, STRING IDs

See: references/tool_parameters.md for complete parameter documentation

Detailed Documentation

All detailed examples, code blocks, and advanced topics have been moved to references/:

references/ora_workflow.md - Complete ORA examples with all databases
references/gsea_workflow.md - Complete GSEA workflow with ranked lists
references/enrichr_guide.md - All 225+ Enrichr libraries and usage
references/cross_validation.md - Multi-source validation strategies
references/id_conversion.md - Gene ID disambiguation and conversion
references/tool_parameters.md - Complete tool parameter reference
references/organism_support.md - Organism-specific configurations
references/common_patterns.md - Detailed use case examples
references/troubleshooting.md - Complete troubleshooting guide
references/multiple_testing.md - Correction methods (BH, Bonferroni, BY)
references/report_template.md - Standard report format

Helper scripts:

scripts/format_enrichment_output.py - Format results for reports
scripts/compare_enrichment_sources.py - Cross-validation analysis
scripts/filter_by_gene_set_size.py - Filter terms by size

Resources

For network-level analysis: tooluniverse-network-pharmacology For disease characterization: tooluniverse-multiomic-disease-characterization For spatial omics: tooluniverse-spatial-omics-analysis For protein interactions: tooluniverse-protein-interactions

gseapy documentation: https://gseapy.readthedocs.io/ PANTHER API: http://pantherdb.org/services/oai/pantherdb/ STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api Reactome Analysis: https://reactome.org/AnalysisService/

When it activates

Automatically, when a task matches. Use when users ask about gene enrichment, p

About this skill

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Source

Imported

FreedomIntelligence / OpenClaw-Medical-Skills

skills/tooluniverse-gene-enrichment/SKILL.md

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Imported 8/1/2026Version v1Bundled 6 files · scripts

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What it changes

Track record

Use this skill

Gene Enrichment and Pathway Analysis

When to Use This Skill

Input Parameters

Core Principles

Decision Tree: ORA vs GSEA

Decision Tree: gseapy vs ToolUniverse Tools

Quick Start Workflow

Step 1: Create Report File (IMMEDIATE)

Step 2: ID Conversion and Validation

Step 3: Primary Enrichment with gseapy

Step 4: Cross-Validation with ToolUniverse

Step 5: Report Compilation

Evidence Grading

Supported Organisms

Common Patterns

Pattern 1: Standard DEG Enrichment (ORA)

Pattern 2: Ranked Gene List (GSEA)

Pattern 3: BixBench Enrichment Question

Pattern 4: Multi-Organism Enrichment

Troubleshooting

Tool Reference

Primary Enrichment Tools

Cross-Validation Tools

ID Conversion Tools

Detailed Documentation

Resources

When it activates

SkillSafety

Source

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