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Phylogenetics and Sequence Analysis · FreedomIntelligence | DecimalAI

Scienceby FreedomIntelligence

Phylogenetics and Sequence Analysis

Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for s

SkillScore

SkillScore64/100

Test results 64

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

0%64%of test cases passed +64 pts

Without skill0 of 22

With skill14 of 22

14 fixed0 passed either way8 still failing0 broke

Measured on gemini-3.6-flash7/26/2026v120 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-phylogenetics
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-phylogenetics")
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-phylogenetics

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

Copy SKILL.md

Save it in your project, as

.claude/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/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-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/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-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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-phylogenetics")
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-phylogenetics/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tooluniverse-phylogenetics/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-phylogenetics

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

Copy SKILL.md

Save it in your project, as

.agents/skills/tooluniverse-phylogenetics/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-phylogenetics/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tooluniverse-phylogenetics.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

Phylogenetics and Sequence Analysis

Comprehensive phylogenetics and sequence analysis using PhyKIT, Biopython, and DendroPy. Designed for bioinformatics questions about multiple sequence alignments, phylogenetic trees, parsimony, molecular evolution, and comparative genomics.

IMPORTANT: This skill handles complex phylogenetic workflows. 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 FASTA alignment files and ask about parsimony informative sites, gaps, or alignment quality
Have Newick tree files and ask about treeness, tree length, evolutionary rate, or DVMC
Ask about treeness/RCV, RCV, or relative composition variability
Need to compare phylogenetic metrics between groups (fungi vs animals, etc.)
Ask about PhyKIT functions (treeness, rcv, dvmc, evo_rate, parsimony_informative, tree_length)
Have gene family data with paired alignments and trees
Need Mann-Whitney U tests or other statistical comparisons of phylogenetic metrics
Ask about bootstrap support, branch lengths, or tree topology
Need to build trees (NJ, UPGMA, parsimony) from alignments
Ask about Robinson-Foulds distance or tree comparison

BixBench Coverage: 33 questions across 8 projects (bix-4, bix-11, bix-12, bix-25, bix-35, bix-38, bix-45, bix-60)

NOT for (use other skills instead):

Multiple sequence alignment generation → Use external tools (MUSCLE, MAFFT, ClustalW)
Maximum Likelihood tree construction → Use IQ-TREE, RAxML, or PhyML
Bayesian phylogenetics → Use MrBayes or BEAST
Ancestral state reconstruction → Use separate tools

Core Principles

Data-first approach - Discover and validate all input files (alignments, trees) before any analysis
PhyKIT-compatible - Use PhyKIT functions for treeness, RCV, DVMC, parsimony, evolutionary rate (matches BixBench expected outputs)
Format-flexible - Support FASTA, PHYLIP, Nexus, Newick, and auto-detect formats
Batch processing - Process hundreds of gene alignments/trees in a single analysis
Statistical rigor - Mann-Whitney U, medians, percentiles, standard deviations with scipy.stats
Precision awareness - Match rounding to 4 decimal places (PhyKIT default) or as requested
Group comparison - Compare metrics between taxa groups (e.g., fungi vs animals)
Question-driven - Parse exactly what is asked and return the specific number/statistic

Required Python Packages

# Core (MUST be installed)
import numpy as np
import pandas as pd
from scipy import stats
from Bio import AlignIO, Phylo, SeqIO
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

# PhyKIT (primary computation engine)
from phykit.services.tree.treeness import Treeness
from phykit.services.tree.total_tree_length import TotalTreeLength
from phykit.services.tree.evolutionary_rate import EvolutionaryRate
from phykit.services.tree.dvmc import DVMC
from phykit.services.tree.treeness_over_rcv import TreenessOverRCV
from phykit.services.alignment.parsimony_informative_sites import ParsimonyInformative
from phykit.services.alignment.rcv import RelativeCompositionVariability

# DendroPy (for advanced tree operations)
import dendropy

# ToolUniverse (for sequence retrieval)
from tooluniverse import ToolUniverse

Installation:

pip install phykit dendropy biopython pandas numpy scipy

High-Level Workflow Decision Tree

START: User question about phylogenetic data
│
├─ Q1: What type of analysis is needed?
│  │
│  ├─ ALIGNMENT ANALYSIS (FASTA/PHYLIP files)
│  │  ├─ Parsimony informative sites → phykit_parsimony_informative()
│  │  ├─ RCV score → phykit_rcv()
│  │  ├─ Gap percentage → alignment_gap_percentage()
│  │  ├─ GC content → alignment_statistics()
│  │  └─ See: references/sequence_alignment.md
│  │
│  ├─ TREE ANALYSIS (Newick files)
│  │  ├─ Treeness → phykit_treeness()
│  │  ├─ Tree length → phykit_tree_length()
│  │  ├─ Evolutionary rate → phykit_evolutionary_rate()
│  │  ├─ DVMC → phykit_dvmc()
│  │  ├─ Bootstrap support → extract_bootstrap_support()
│  │  └─ See: references/tree_building.md
│  │
│  ├─ COMBINED ANALYSIS (alignment + tree)
│  │  └─ Treeness/RCV → phykit_treeness_over_rcv()
│  │
│  ├─ TREE CONSTRUCTION (build from alignment)
│  │  ├─ Neighbor-Joining → build_nj_tree()
│  │  ├─ UPGMA → build_upgma_tree()
│  │  ├─ Parsimony → build_parsimony_tree()
│  │  └─ See: references/tree_building.md
│  │
│  ├─ GROUP COMPARISON (fungi vs animals, etc.)
│  │  ├─ Batch compute metrics per group
│  │  ├─ Mann-Whitney U test
│  │  ├─ Summary statistics (median, mean, percentiles)
│  │  └─ See: references/parsimony_analysis.md
│  │
│  └─ TREE COMPARISON
│     ├─ Robinson-Foulds distance → robinson_foulds_distance()
│     └─ Bootstrap consensus → bootstrap_analysis()
│
├─ Q2: What data format is available?
│  ├─ FASTA (.fa, .fasta, .faa, .fna)
│  ├─ PHYLIP (.phy, .phylip) - Use phylip-relaxed for long names
│  ├─ Nexus (.nex, .nexus)
│  ├─ Newick (.nwk, .newick, .tre, .tree)
│  └─ Auto-detect with load_alignment() or load_tree()
│
└─ Q3: Is this a batch analysis?
   ├─ Single gene → Run metric function once
   ├─ Multiple genes → Use batch_compute_metric()
   └─ Group comparison → Use discover_gene_files() + compare_groups()

Quick Reference: Common Metrics

Metric	Function	Input	Description
Treeness	`phykit_treeness(tree_file)`	Newick	Internal branch length / Total branch length
RCV	`phykit_rcv(aln_file)`	FASTA/PHYLIP	Relative Composition Variability
Treeness/RCV	`phykit_treeness_over_rcv(tree, aln)`	Both	Treeness divided by RCV
Tree Length	`phykit_tree_length(tree_file)`	Newick	Sum of all branch lengths
Evolutionary Rate	`phykit_evolutionary_rate(tree_file)`	Newick	Total branch length / num terminals
DVMC	`phykit_dvmc(tree_file)`	Newick	Degree of Violation of Molecular Clock
Parsimony Sites	`phykit_parsimony_informative(aln_file)`	FASTA/PHYLIP	Sites with ≥2 chars appearing ≥2 times
Gap Percentage	`alignment_gap_percentage(aln_file)`	FASTA/PHYLIP	Percentage of gap characters

See scripts/tree_statistics.py for implementation.

Common Analysis Patterns (BixBench)

Pattern 1: Single Metric Across Groups

Question: "What is the median DVMC for fungi vs animals?"

Workflow:

# 1. Discover files
fungi_genes = discover_gene_files("data/fungi")
animal_genes = discover_gene_files("data/animals")

# 2. Compute metric
fungi_dvmc = batch_dvmc(fungi_genes)
animal_dvmc = batch_dvmc(animal_genes)

# 3. Compare
fungi_values = list(fungi_dvmc.values())
animal_values = list(animal_dvmc.values())

print(f"Fungi median DVMC: {np.median(fungi_values):.4f}")
print(f"Animal median DVMC: {np.median(animal_values):.4f}")

See: references/parsimony_analysis.md for full implementation

Pattern 2: Statistical Comparison

Question: "What is the Mann-Whitney U statistic comparing treeness between groups?"

Workflow:

from scipy import stats

# Compute treeness for both groups
group1_treeness = batch_treeness(group1_genes)
group2_treeness = batch_treeness(group2_genes)

# Mann-Whitney U test (two-sided)
u_stat, p_value = stats.mannwhitneyu(
    list(group1_treeness.values()),
    list(group2_treeness.values()),
    alternative='two-sided'
)

print(f"U statistic: {u_stat:.0f}")
print(f"P-value: {p_value:.4e}")

Pattern 3: Filtering + Metric

Question: "What is the treeness/RCV for alignments with <5% gaps?"

Workflow:

# 1. Filter by gap percentage
valid_genes = []
for entry in gene_files:
    if 'aln_file' in entry:
        gap_pct = alignment_gap_percentage(entry['aln_file'])
        if gap_pct < 5.0:
            valid_genes.append(entry)

# 2. Compute metric on filtered set
results = batch_treeness_over_rcv(valid_genes)

# 3. Report
values = [r[0] for r in results.values()]  # treeness/rcv ratio
print(f"Median treeness/RCV: {np.median(values):.4f}")

Pattern 4: Specific Gene Lookup

Question: "What is the evolutionary rate for gene X?"

Workflow:

# Find gene file
gene_files = discover_gene_files("data/")
gene_entry = [g for g in gene_files if g['gene_id'] == 'X'][0]

# Compute metric
evo_rate = phykit_evolutionary_rate(gene_entry['tree_file'])

print(f"Evolutionary rate for gene X: {evo_rate:.4f}")

Choosing Methods: When to Use What

Alignment Methods

When building alignments (use external tools, not this skill):

Method	Speed	Accuracy	Use Case
ClustalW	Slow	Medium	Small datasets (<100 sequences), educational
MUSCLE	Fast	High	Medium datasets (100-1000 sequences)
MAFFT	Very Fast	Very High	Recommended - Large datasets (>1000 sequences)

For this skill: Work with pre-aligned sequences. Use load_alignment() to read any format.

Tree Building Methods

When to use which tree method:

Method	Speed	Accuracy	Use Case
Neighbor-Joining	Fast	Medium	Quick trees, large datasets, exploratory
UPGMA	Fast	Low	Assumes molecular clock, special cases only
Maximum Parsimony	Medium	Medium	Small datasets, discrete characters
Maximum Likelihood	Slow	High	Use external tools (IQ-TREE, RAxML) for production

Implementation in this skill:

# Fast distance-based trees
tree = build_nj_tree("alignment.fa")  # Neighbor-Joining
tree = build_upgma_tree("alignment.fa")  # UPGMA

# Parsimony (for small alignments)
tree = build_parsimony_tree("alignment.fa")

For production ML trees: Use IQ-TREE or RAxML externally, then analyze with this skill.

See references/tree_building.md for detailed implementations.

Batch Processing

Discovering Gene Files

# Auto-discover paired alignment + tree files
gene_files = discover_gene_files("data/")

# Result: list of dicts with 'gene_id', 'aln_file', 'tree_file'
# [
#   {'gene_id': 'gene1', 'aln_file': 'gene1.fa', 'tree_file': 'gene1.nwk'},
#   {'gene_id': 'gene2', 'aln_file': 'gene2.fa', 'tree_file': 'gene2.nwk'},
#   ...
# ]

Computing Metrics in Batch

# Tree metrics
treeness_results = batch_treeness(gene_files)
tree_length_results = batch_tree_length(gene_files)
dvmc_results = batch_dvmc(gene_files)
evo_rate_results = batch_evolutionary_rate(gene_files)

# Alignment metrics
rcv_results = batch_rcv(gene_files)
pi_results = batch_parsimony_informative(gene_files)
gap_results = batch_gap_percentage(gene_files)

# Combined metrics
treeness_rcv_results = batch_treeness_over_rcv(gene_files)

# All return dict: {gene_id: value}

Statistical Analysis

# Summary statistics
stats = summary_stats(list(treeness_results.values()))
# Returns: {'mean': ..., 'median': ..., 'std': ..., 'min': ..., 'max': ...}

# Group comparison
comparison = compare_groups(
    list(fungi_treeness.values()),
    list(animal_treeness.values()),
    group1_name="Fungi",
    group2_name="Animals"
)
# Returns: {'u_statistic': ..., 'p_value': ..., 'Fungi': {...}, 'Animals': {...}}

See references/parsimony_analysis.md for full workflow.

Answer Extraction for BixBench

Question Pattern	Extraction Method
"What is the median X?"	`np.median(values)`
"What is the maximum X?"	`np.max(values)`
"What is the difference between median X for A vs B?"	`abs(np.median(a) - np.median(b))`
"What percentage of X have Y above Z?"	`sum(v > Z for v in values) / len(values) * 100`
"What is the Mann-Whitney U statistic?"	`stats.mannwhitneyu(a, b)[0]`
"What is the p-value?"	`stats.mannwhitneyu(a, b)[1]`
"What is the X value for gene Y?"	`results[gene_id]`
"What is the fold-change in median X?"	`np.median(a) / np.median(b)`
"multiplied by 1000"	`round(value * 1000)`

Rounding Rules

PhyKIT default: 4 decimal places
Percentages: Match question format (e.g., "35%" → integer, "3.5%" → 1 decimal)
P-values: Scientific notation for very small values
U statistics: Integer (no decimals)
Always check question wording: "rounded to 3 decimal places" overrides defaults

BixBench Question Coverage

Project	Questions	Metrics
bix-4	7	DVMC analysis (fungi vs animals)
bix-11	6	Treeness analysis (median, percentages, Mann-Whitney U)
bix-12	5	Parsimony informative sites (counts, percentages, ratios)
bix-25	2	Treeness/RCV with gap filtering
bix-35	4	Evolutionary rate (specific genes, comparisons)
bix-38	5	Tree length (fold-change, variance, paired ratios)
bix-45	4	RCV (Mann-Whitney U, medians, paired differences)
bix-60	1	Average treeness across multiple trees

ToolUniverse Integration

Sequence Retrieval

from tooluniverse import ToolUniverse

tu = ToolUniverse()
tu.load_tools()

# Get sequences from NCBI
result = tu.tools.NCBI_get_sequence(accession="NP_000546")

# Get gene tree from Ensembl
tree_result = tu.tools.EnsemblCompara_get_gene_tree(gene="ENSG00000141510")

# Get species tree from OpenTree
tree_result = tu.tools.OpenTree_get_induced_subtree(ott_ids="770315,770319")

File Structure

tooluniverse-phylogenetics/
├── SKILL.md                           # This file (workflow orchestration)
├── QUICK_START.md                     # Quick reference
├── test_phylogenetics.py             # Comprehensive test suite
├── references/
│   ├── sequence_alignment.md         # Alignment analysis details
│   ├── tree_building.md              # Tree construction methods
│   ├── parsimony_analysis.md         # Statistical comparison workflows
│   └── troubleshooting.md            # Common issues and solutions
└── scripts/
    ├── format_alignment.py           # Alignment format conversion
    └── tree_statistics.py            # Core metric implementations

Completeness Checklist

Before returning your answer, verify:

Next Steps

For detailed alignment analysis workflows → See references/sequence_alignment.md
For tree construction methods → See references/tree_building.md
For statistical comparison examples → See references/parsimony_analysis.md
For common errors and solutions → See references/troubleshooting.md
For script implementations → See scripts/tree_statistics.py

Support

For issues with:

PhyKIT functions: Check PhyKIT documentation at https://jlsteenwyk.com/PhyKIT/
Biopython tree/alignment parsing: See https://biopython.org/wiki/Phylo
DendroPy operations: See https://dendropy.org/
ToolUniverse integration: Check ToolUniverse documentation

License

Same as ToolUniverse framework license.

About this skill

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Source

Imported

FreedomIntelligence / OpenClaw-Medical-Skills

skills/tooluniverse-phylogenetics/SKILL.md

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

Illustrative Want to see what tooluniverse-phylogenetics 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.

mims-harvard/tooluniverse-phylogeneticsPhylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT), parsimony-informative sites, alignment gap analysis, DVMC, long-branch detection, BUSCO orthologs. Uses PhyKIT, Biopython, DendroPy. Use to align a set of sequences, build a tree from sequences or an alignment, or for phylogenetic tree QC, multiClose match+32 pts pass-rate lift

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What it changes

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Phylogenetics and Sequence Analysis

When to Use This Skill

Core Principles

Required Python Packages

High-Level Workflow Decision Tree

Quick Reference: Common Metrics

Common Analysis Patterns (BixBench)

Pattern 1: Single Metric Across Groups

Pattern 2: Statistical Comparison

Pattern 3: Filtering + Metric

Pattern 4: Specific Gene Lookup

Choosing Methods: When to Use What

Alignment Methods

Tree Building Methods

Batch Processing

Discovering Gene Files

Computing Metrics in Batch

Statistical Analysis

Answer Extraction for BixBench

Rounding Rules

BixBench Question Coverage

ToolUniverse Integration

Sequence Retrieval

File Structure

Completeness Checklist

Next Steps

Support

License

SkillSafety

Source

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