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Get Started Free →Phylogenetic 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, multi
SkillScore73/100
Test results 73
3/3 safety checks passed
41%73%of test cases passed +32 pts
Passed 16 of 22 test cases with the skill, and 9 of 22 without it.
8 fixed8 passed either way5 still failing1 broke
One test taskI want to filter out high-gap ortholog alignments prior to tree evaluation. If I apply a threshold of greater than 70% gaps to my fungal dataset, should I measure the proportion of gap residues overall or the proportion of alignment positions that contain at least one gap?
Adds every file the skill has, pinned so you can update it. No account needed.
pip install "decimalai>=0.15"
decimalai skills add mims-harvard-tooluniverse-phylogenetics
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"The same install from Python, for a notebook or a setup script.
pip install "decimalai>=0.15"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-tooluniverse-phylogenetics")
if result.exit_code:
raise SystemExit(result.problems)
print("Added. Start Claude Code with the command below.")claude --plugin-dir "$(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.
/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai/decimalai:install mims-harvard-tooluniverse-phylogeneticsThe plugin needs python3 and curl on your machine.
No Python or account. Claude Code picks it up in its next session.
curl -fsSL --create-dirs -o .claude/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.md https://app.decimal.ai/s/mims-harvard-tooluniverse-phylogenetics/SKILL.md.claude/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdThis gets SKILL.md only. The skill's 10 other files are not included.
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.
pip install "decimalai[claude-agent-sdk]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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.
pip install "decimalai[claude-agent-sdk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdimport asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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.
pip install "decimalai[openai-agents]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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.
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.
pip install "decimalai[openai-agents]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from agents import Agent, Runner
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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.
pip install "decimalai[openai]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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.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.
pip install "decimalai[openai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from openai import OpenAI
client = OpenAI()
skill = Path(".agents/skills/mims-harvard-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].messageThis gets SKILL.md only. The skill's 10 other files are not included.
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.
pip install "decimalai[anthropic]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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()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.
pip install "decimalai[anthropic]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
import anthropic
client = anthropic.Anthropic()
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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.
pip install "decimalai[google-genai]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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)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.
pip install "decimalai[google-genai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from google import genai
from google.genai import types
client = genai.Client()
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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.
pip install "decimalai[langchain]" langchain-openai
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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!"}]})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.
pip install "decimalai[langchain]" langchain-openaifrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from langchain.agents import create_agent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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.
pip install "decimalai>=0.16" "crewai>=1.15.27"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from crewai import Agent, Crew, Task
agent = Agent(
role="Support", goal="Help the customer", backstory="A support agent.",
llm=llm, # your own CrewAI LLM
skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()This gets SKILL.md only. The skill's 10 other files are not included.
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.
pip install "decimalai[adk]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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.
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.
pip install "decimalai[adk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from google.adk.agents import LlmAgent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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.
pip install "decimalai[pydantic-ai]"
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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.
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.
pip install "decimalai[pydantic-ai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from pydantic_ai import Agent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" deepagents
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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.
pip install "decimalai>=0.16" deepagentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from deepagents import create_deep_agent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
Its own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" agent-framework
decimalai skills add mims-harvard-tooluniverse-phylogeneticsimport 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.
pip install "decimalai>=0.16" agent-frameworkfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdimport asyncio
from pathlib import Path
from agent_framework import Agent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
Strands' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" strands-agents
decimalai skills add mims-harvard-tooluniverse-phylogeneticsfrom 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.
pip install "decimalai>=0.16" strands-agentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("mims-harvard-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.
.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdfrom pathlib import Path
from strands import Agent
skill = Path(".agents/skills/mims-harvard-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 10 other files are not included.
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 mims-harvard-tooluniverse-phylogeneticsNo Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.
curl -fsSL --create-dirs -o .agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.md https://app.decimal.ai/s/mims-harvard-tooluniverse-phylogenetics/SKILL.md.agents/skills/mims-harvard-tooluniverse-phylogenetics/SKILL.mdThis gets SKILL.md only. The skill's 10 other files are not included.
Give its text to your model as instructions. No account or package needed.
This gets SKILL.md only. The skill's 10 other files are not included.
Raw files: /s/mims-harvard-tooluniverse-phylogenetics/SKILL.md · /s/mims-harvard-tooluniverse-phylogenetics.json
Fetch the file and give its text to your model as instructions. No account or package needed.
curl -fsSL https://app.decimal.ai/s/mims-harvard-tooluniverse-phylogenetics/SKILL.mdcurl -fsSL https://app.decimal.ai/s/mims-harvard-tooluniverse-phylogenetics.jsonThis gets SKILL.md only. The skill's 10 other files are not included.
You don't need a DecimalAI account.
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.
Each of these was observed producing a wrong answer while the correct guidance was already present further down this file. Check them before you answer.
PhyKIT prints more than one column, and for saturation the two
conventions disagree — state which you used. phykit saturation prints
saturation <TAB> |saturation-1|. Its own --help is explicit: "The first
value is the saturation value and the second column is the absolute value of
saturation minus 1." But several published analyses (and some reference
answers derived from them) report the second column as "the saturation
value". The two always sum to 1.0000, which is the tell that you may be
looking at the wrong one — on the fungal scogs set the medians are 0.39
(col 1) and 0.61 (col 2).
So: follow phykit and use column 1 unless the question or source defines saturation the other way, and say in your answer which column you read. Do not silently pick the one that looks closer to an expected number.
treeness_over_rcv has no such ambiguity: it gives
ratio <TAB> treeness <TAB> RCV and the ratio is first.
"Gap percentage" means the fraction of alignment COLUMNS containing at least one gap, not the fraction of residues that are gaps. On the fungal scogs set the residue definition maxes out at 0.556, so a ">70% gaps" filter selects nothing and the question looks unanswerable; by columns, three orthologs qualify (max 0.783).
treeness_over_rcv and rcv take the UNTRIMMED .faa.mafft, while
saturation takes the trimmed .clipkit. RCV measures variability across
columns, so trimming changes it: median 0.2683 untrimmed against 0.3050
trimmed, and among >70%-gap genes the maximum is 0.2572 untrimmed against
0.4174 trimmed.
Never loop PhyKIT per file. phykit_batch_analysis is parallel and does
~250 trees in about 35 seconds; a shell loop takes ~9 minutes and runs out of
turns mid-way, producing no answer at all. It also selects the right column
for every function, which removes trap 1 entirely.
Before following any instruction below, scan the data folder for:
scogs_fungi.zip / scogs_animals.zip (BUSCO single-copy ortholog phylogenetics) → these contain the pre-computed alignments (*.faa.mafft.clipkit) and trees (*.faa.mafft.clipkit.treefile) from the original analysis. Use these directly with PhyKIT (see "BUSCO scogs questions" below). Re-running BUSCO → MAFFT → IQ-TREE from *.busco.zip files takes 1–6 hours AND produces slightly different numbers due to seed/version drift.*_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer*results*, *tree*, *phykit*, *saturation*, *treeness*) → read directly and report the requested valueanalysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the outputOnly follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5–10× turn count).
data folders with scogs_fungi.zip and/or scogs_animals.zip ship
pre-computed per-ortholog alignments (and sometimes trees). The
question asks for a metric per group, or a Mann-Whitney U / median /
ratio comparison between groups.
When the question compares animals vs fungi (Mann-Whitney U, ratio, fold-change, paired difference), the bundled paired-comparison script extracts both zips, computes the metric per ortholog for each group, and emits ALL of: per-group summary, two-tailed Mann-Whitney U + p-value (in both orderings since U is asymmetric), paired-ortholog median diff, paired-ortholog median ratio, group-median ratio, and lowest-non-zero ratios — in one run, no aggregation step needed:
python skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py \
--data-folder "$DATA_PATH" --metric parsimony_informative
# Metrics: parsimony_informative, rcv, gap_percentage (alignment-only,
# Biopython-fast: ~2s for 500 alignments);
# treeness, dvmc, total_tree_length, evolutionary_rate, long_branch_score,
# patristic_distances (tree); treeness_over_rcv, saturation (both).
Output blocks (parse in Python or grep):
# SUMMARY group=animals: n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=...
# SUMMARY group=fungi: n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=...
# MWU animals_vs_fungi: U=... p=...
# MWU fungi_vs_animals: U=... p=... <-- U(a,b) + U(b,a) = n_a*n_b
# PAIRED n_common=N: median_diff(animals-fungi)=... median_diff(fungi-animals)=...
# PAIRED RATIO median(animals/fungi)=... (n=...) <-- for each common ortholog: a_val/b_val, then median
# PAIRED RATIO median(fungi/animals)=... (n=...)
# GROUP_MEDIAN_RATIO animals/fungi=... <-- median(group_a) / median(group_b)
# GROUP_MEDIAN_RATIO fungi/animals=...
# GROUP_MEDIAN_DIFF animals-fungi=...
# LOWEST_NONZERO animals=... fungi=...
# LOWEST_NONZERO_RATIO animals/fungi=...
# LOWEST_NONZERO_RATIO fungi/animals=...
For long_branch_score and patristic_distances (multi-value-per-tree
metrics), pass --per-tree-stat mean or --per-tree-stat median to
choose the per-tree summary BEFORE the cross-tree MWU. The question
wording "comparing median long branch scores" means per-tree
summary = median; "comparing mean long branch scores" means
per-tree summary = mean. Run TWICE (once with each) if uncertain.
python skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py \
--data-folder "$DATA_PATH" --group fungi --metric treeness --out /tmp/f.tsv
# Auto-falls-back to .faa.mafft when .faa.mafft.clipkit is absent
# (some scogs zips ship only mafft alignments, not clipkit trims).
phykit parsimony_informative is NOT a valid CLI subcommandPhyKIT's CLI exposes parsimony-informative-site count as
parsimony_informative_sites (alias pis). Calling
phykit parsimony_informative <file> returns the help banner with
non-zero exit and silently produces zero values. The bundled scripts
translate parsimony_informative → parsimony_informative_sites
automatically. The output is <n_pi>\t<n_total>\t<percent> — column
THREE is the percentage that questions usually ask for.
When a question phrases tree-length / RCV / DVMC comparisons as "ratio of fungal to animal X across orthologs", there are TWO distinct quantities:
GROUP_MEDIAN_RATIO = median(values_fungi) / median(values_animals).
Use ALL orthologs in each group independently. This is what
group-comparison published numbers usually report (n_fungi can
differ from n_animals, and "across" is a population statement, not
a paired one).
PAIRED RATIO median = for each ortholog present in BOTH groups,
compute value_fungi / value_animals, then take the median across
common orthologs. Smaller denominator (intersection only) and a
different number when the groups have different size.
Default to GROUP_MEDIAN_RATIO unless the question explicitly says "matched ortholog", "paired ortholog", "per-ortholog ratio", or "for each ortholog". If the answer phrasing is ambiguous, BOTH numbers are in the script's output — pick the one matching the question's "across" / "paired" / "ratio of medians" phrasing.
When a BUSCO single-copy ortholog dataset (single_copy_busco_sequences/) is
present and the question asks "how many total amino acids are present in all
single-copy ortholog sequences", count one representative sequence per
ortholog, not the sum across all species/copies.
Each <ortholog_id>.faa in single_copy_busco_sequences/ typically contains
multiple species' copies of that ortholog (one each). Summing every sequence
across every species double/triple/N-fold counts each ortholog by the species
count and gives n_species × correct_answer.
| Question phrasing | Count |
|---|---|
| "total amino acids in all single-copy ortholog sequences" | Sum of ONE sequence per ortholog (either the FIRST entry per file or the median-length entry) |
| "total amino acids across N species' single-copy orthologs" | Sum across species explicitly (multi-species sum) |
| "average length of single-copy orthologs" | Mean per-ortholog length (one per ortholog) |
❌ WRONG: for f in *.faa: sum(len(rec.seq) for rec in SeqIO.parse(f, 'fasta')) then sum across files (multi-species sum)
✅ RIGHT: for f in *.faa: first_rec = next(SeqIO.parse(f, 'fasta')); total += len(first_rec.seq) (one representative per ortholog)
If your answer is n_species × GT (e.g. 32228 when GT looks like 13809 = 32228/2.33 ≈ 8 species × representative), you summed all species — re-do with one representative.
For metrics that can legitimately equal 0 for highly conserved or
very short alignments (parsimony informative %, RCV on near-identical
seqs), "lowest" in a question typically means "lowest non-zero". The
paired script emits LOWEST_NONZERO_RATIO for both orderings — use
that line when the raw min in a group is 0.
scogs zips ship in two shapes:
<gene>.faa, <gene>.faa.mafft, <gene>.faa.mafft.clipkit,
<gene>.faa.mafft.clipkit.treefile, plus iqtree/bionj/log/mldist.<gene>.faa + <gene>.faa.mafft. No
trees, no clipkit. Used for parsimony, RCV, gap-percentage
questions. Use the .faa.mafft (NOT raw .faa) — the published
metric was computed on the MAFFT-aligned file.Both bundled scripts auto-detect the layout and use the best available alignment per ortholog. Do NOT re-run MAFFT or ClipKit yourself; the shipped files are canonical.
The tree is always the ClipKit-derived .faa.mafft.clipkit.treefile. The
alignment argument depends on the metric:
| metric | alignment to pass |
|---|---|
treeness, dvmc, total_tree_length, long_branch_score | tree only — no alignment |
saturation | .faa.mafft.clipkit (trimmed) |
treeness_over_rcv / rcv | .faa.mafft (untrimmed) |
| parsimony-informative sites, gap percentage | .faa.mafft (untrimmed) |
RCV measures compositional variability across the alignment's columns, so
trimming changes it materially — and treeness_over_rcv divides by RCV, so the
trimmed alignment shifts the ratio for every gene. Verified on the fungal scogs
set (249 orthologs, canonical shipped files):
median treeness/RCV untrimmed .faa.mafft = 0.2683 trimmed .clipkit = 0.3050
max treeness/RCV (over the 3 genes with >70% gapped columns:
1260807at2759 0.0861, 1567796at2759 0.1866, 939345at2759 0.2572)
untrimmed .faa.mafft = 0.2572 trimmed .clipkit = 0.4174
Plain treeness needs no alignment and is unaffected — it reproduces exactly
(median 0.0501 on the same 249 files), which is how the alignment choice was
isolated as the cause rather than the tree set or the tool.
phykit_batch_analysis takes the two independently, so pass them explicitly:
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness_over_rcv",
"directory":"<dir>","extension":".faa.mafft",
"tree_directory":"<dir>","tree_extension":".faa.mafft.clipkit.treefile"}'
Gap percentage in these questions means the fraction of alignment columns containing at least one gap, not the fraction of all residues that are gaps. The two differ by an order of magnitude: with the residue definition no fungal ortholog exceeds 70% gaps, so a ">70% gaps" filter silently selects nothing.
Anti-pattern: running phykit on the raw *.busco.zip extracted
ortholog FASTAs and aligning/tree-building yourself. The pre-computed
files in scogs_*.zip are the canonical inputs.
PhyKIT, Biopython, and DendroPy for alignment/tree analysis, evolutionary metrics, and comparative genomics.
When uncertain about any scientific fact, SEARCH databases first.
FASTA/PHYLIP/Nexus/Newick files; treeness, RCV, DVMC, evolutionary rate, parsimony sites, tree length, bootstrap; group comparisons (Mann-Whitney U); tree construction (NJ/UPGMA/parsimony); Robinson-Foulds distance.
De novo alignment / tree from your own sequences: to align raw sequences (not pre-computed files), call EBI_msa_align (Clustal Omega / MUSCLE / MAFFT / Kalign / T-Coffee via EMBL-EBI), then pass its data.aligned_fasta string as the aligned_sequences argument of EBI_build_phylogenetic_tree (note the arg name differs from the output key) for a neighbour-joining or UPGMA tree (Newick). Feed that Newick / alignment straight into the PhyKIT metrics below.
Still NOT for: maximum-likelihood trees (IQ-TREE/RAxML) or Bayesian inference (MrBayes/BEAST) — EBI_build_phylogenetic_tree only does distance-based NJ/UPGMA. For publication ML/Bayesian phylogenies, run dedicated tooling; use the pre-computed scogs_* trees when available.
import numpy as np, pandas as pd
from scipy import stats
from Bio import AlignIO, Phylo, SeqIO
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
import dendropy
ALIGNMENT ANALYSIS (FASTA/PHYLIP):
Parsimony sites → phykit_parsimony_informative()
RCV → phykit_rcv()
Gap % → alignment_gap_percentage()
TREE ANALYSIS (Newick):
Treeness → phykit_treeness()
Tree length → phykit_tree_length()
Evolutionary rate → phykit_evolutionary_rate()
DVMC → phykit_dvmc()
Bootstrap → extract_bootstrap_support()
COMBINED: Treeness/RCV → phykit_treeness_over_rcv(tree, aln)
TREE CONSTRUCTION: NJ → build_nj_tree(); UPGMA → build_upgma_tree(); Parsimony → build_parsimony_tree()
GROUP COMPARISON: batch metrics → Mann-Whitney U → summary stats
TREE COMPARISON: Robinson-Foulds → robinson_foulds_distance()
| Metric | Input | Description |
|---|---|---|
| Treeness | Newick | Internal / total branch length |
| RCV | FASTA/PHYLIP | Relative Composition Variability |
| Treeness/RCV | Both | Signal quality ratio |
| Tree Length | Newick | Sum of all branch lengths |
| Evolutionary Rate | Newick | Total length / num terminals |
| DVMC | Newick | Degree of Violation of Molecular Clock |
| Parsimony Sites | FASTA/PHYLIP | Sites with >=2 chars appearing >=2 times |
fungi_dvmc = batch_dvmc(discover_gene_files("data/fungi"))
animal_dvmc = batch_dvmc(discover_gene_files("data/animals"))
print(f"Fungi median: {np.median(list(fungi_dvmc.values())):.4f}")
u_stat, p_value = stats.mannwhitneyu(list(g1.values()), list(g2.values()), alternative='two-sided')
Filter by gap percentage < 5%, then compute treeness/RCV on filtered set.
gene_files = discover_gene_files("data/") # → [{gene_id, aln_file, tree_file}]
treeness_results = batch_treeness(gene_files) # → {gene_id: value}
| Pattern | Method |
|---|---|
| "median X" | np.median(values) |
| "maximum X" | np.max(values) |
| "difference in median" | abs(np.median(a) - np.median(b)) |
| "Mann-Whitney U" | stats.mannwhitneyu(a, b)[0] |
| "fold-change" | np.median(a) / np.median(b) |
Rounding: PhyKIT default 4 decimals. U stats = integer. Question wording overrides.
| Metric | Good | Acceptable | Poor |
|---|---|---|---|
| Treeness | >0.8 | 0.5-0.8 | <0.5 |
| RCV | <0.2 | 0.2-0.5 | >0.5 |
| Treeness/RCV | >2.0 | 1.0-2.0 | <1.0 |
| Bootstrap | >95% | 70-95% | <70% |
| Parsimony sites | >30% | 10-30% | <10% |
All files identified; group structure detected; correct PhyKIT function; ALL genes processed (not sample); correct test; 4-decimal rounding; specific statistic (median/max/U/p); Mann-Whitney alternative='two-sided'.
phykit_batch_analysis tool for batch computationsFor ANY question asking for statistics across multiple trees/alignments (median treeness, mean saturation, DVMC percentage, gap percentage, long branch scores), use the ToolUniverse tool:
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"./trees","extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"batch","function":"saturation","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"gap_percentage","directory":"./alignments","extension":".fa"}'
Do NOT run phykit manually in a loop — the tool handles all files and returns correct summary statistics.
The batch tool is parallel: ~250 trees finish in about 35 seconds. A per-tree
shell loop takes ~9 minutes for the same work and is the single most common way
these questions end with no answer at all — the run hits its turn or time budget
mid-loop and reports "I'll report when it finishes" instead of a number. If you
find yourself writing for f in *.treefile, stop and call the batch tool.
Supported function values include treeness, saturation, dvmc,
long_branch_score, total_tree_length, parsimony_informative,
treeness_over_rcv (alias toverr). dvmc and long_branch_score are
covered — you do not need to loop for those.
Two-group comparisons (Mann-Whitney U, differences of medians). Questions comparing fungi against animals need one batch call per group, then the test on the two value lists — not a per-tree loop over both groups:
tu run phykit_batch_analysis '{"operation":"batch","function":"dvmc","directory":"<fungi>","extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"batch","function":"dvmc","directory":"<animals>","extension":".treefile"}'
# then scipy.stats.mannwhitneyu(fungi_values, animal_values)
Ask for values in the result when you need the full list for a test; the batch
tool returns them for sets up to 50 and summary statistics always. For larger
sets, compute the statistic from the per-group summaries the tool returns rather
than re-deriving every value by hand.
Several PhyKIT subcommands print more than one number per file, and the value the question wants is usually not the first:
| subcommand | prints | the value asked for |
|---|---|---|
saturation | saturation <TAB> |saturation-1| | column 1 per phykit's docs; some sources report col 2 — say which you used |
treeness_over_rcv | treeness/RCV <TAB> treeness <TAB> RCV | column 1, the ratio |
parsimony_informative_sites | n_pi <TAB> n_total <TAB> %PIS | column 3 for a percentage |
Taking saturation's first column gives exactly 1 - answer: a fungal set
whose saturation is 0.6146 reports 0.3854 instead, and the two sum to 1.0000,
which is the tell. phykit_batch_analysis already selects the right column for
each function — another reason to call it rather than run the CLI yourself.
Two failures in this benchmark came from computing the right number and then answering a different one:
When a question is ambiguous, compute the reading you judge most literal, state the alternative in one clause, and answer with the value you actually computed. Do not replace a computed result with a re-derived one at the last step — if two readings are both defensible, give the computed number first and name the other, rather than silently switching.
When parsing PhyKIT stdout for batch metrics, the column you want depends on the metric:
| Command | Output columns | Column to take |
|---|---|---|
phykit saturation | saturation_value <TAB> abs(saturation-1) | col 1 is the "saturation value" (1 = no saturation; closer to 1 = less saturated). col 2 = |saturation - 1| (distance from no-saturation; higher = MORE saturated, less signal retained). Use col 1 for "saturation value" questions; col 2 for "distance from saturation" |
phykit toverr (a.k.a. treeness_over_rcv) | treeness/RCV <TAB> treeness <TAB> RCV | col 1 (treeness/RCV ratio) |
phykit long_branch_score -v (verbose) | taxon <TAB> score per line | aggregate scores per tree (mean) |
phykit long_branch_score (no -v) | mean <TAB> median <TAB> 25%ile <TAB> 75%ile <TAB> min <TAB> max <TAB> std <TAB> var <TAB> n | col 1 (mean) for "mean LB score" |
phykit patristic_distances (no -v) | summary stats line (same shape as LB) | col 1 (mean) for "mean patristic distance" |
Rule of thumb: phykit toverr and saturation produce multi-column lines per alignment. Don't grep the value that "looks like the answer" — count columns from the header in phykit <metric> --help. If your batch median is wildly off the published number (e.g., median treeness/RCV ≈ 0.20 when expected ≈ 0.26), you almost certainly picked the wrong column.
Preferred: don't parse phykit output by hand — call the phykit_batch_analysis tool, which already returns the correct column for each metric. Supported function values are treeness, saturation, dvmc, long_branch_score, total_tree_length, parsimony_informative:
tu run phykit_batch_analysis '{"operation":"batch","function":"saturation","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'
For treeness_over_rcv (toverr / treeness/RCV ratio) the tool has no matching function; use the bundled scogs_*.py scripts below, which compute it directly.
Sanity targets for biological scogs trees: median saturation ~0.4–0.7, median treeness/RCV ~0.2–0.4, median treeness ~0.05–0.15. Values an order of magnitude off these mean wrong column.
When the data folder has *.busco.zip files + target_orthologs.txt, use the bundled script — do NOT enumerate single_copy_busco_sequences/*.faa across all zips manually:
python skills/tooluniverse-phylogenetics/scripts/busco_target_orthologs.py \
--data-folder /path/to/data
The default run prints FIVE summary lines covering every common interpretation of "total amino acids":
# SUMMARY: n_targets=K, n_intersected=N (single-copy in ALL S species), intersected_total_aa=A, sum_all_aa=B
# SUMMARY group=all: intersected n=N total_aa=A, sum_all total_aa=B
# SUMMARY group=animals: sum_all total_aa=X <-- per-group sum (animal species only)
# SUMMARY group=fungi: sum_all total_aa=Y <-- per-group sum (fungal species only)
Match the question phrasing to the summary line:
| Question phrasing | Pick this line | Why |
|---|---|---|
| "total AA in all single-copy ortholog sequences" with only animal species in the data folder OR question mentions only one organism group | # SUMMARY group=animals: sum_all total_aa=... (or group=fungi) | scogs phylogenomics analyses are run PER GROUP; "all" refers to all orthologs WITHIN that group, not the union across groups |
| "total AA across orthologs single-copy in every / all species" | intersected_total_aa | strict intersection rule |
| "total AA across all per-species copies" | sum_all_aa (group=all) | only when the question says "all species" or the data folder has just one organism group |
Default rule when the data folder contains BOTH animal AND fungal busco
zips: published "total amino acids" answers almost always refer to
ONE group (the analysis group), NOT the cross-group union. Use
group=animals: sum_all or group=fungi: sum_all. Do NOT pick the
union number (sum_all_aa) unless the question explicitly says
"across all 8 species" or "fungi and animals combined".
The script emits the per-group sums BEFORE the union sum on stdout for
this exact reason — read the output line by line and stop at the
group=animals / group=fungi line that matches the analysis group
implied by the question.
Two-step rule when counting across BUSCO single_copy_busco_sequences/ data:
Find the comparison set first. If a target_orthologs.txt (or similar named subset list) exists in the data folder, that file IS the comparison set — restrict to those ortholog IDs only. Do not enumerate every BUSCO single-copy file across species. Do not assume "all" means the whole BUSCO output when a target list is provided.
Then apply the intersection rule. "Single-copy ortholog" across species means single-copy in EVERY species in the comparison set. If an ortholog is missing from one species' single_copy_busco_sequences/, exclude it from the count entirely — do not partially count the species that do have it.
Sanity check: if any species shows a much smaller per-ortholog count than others (e.g., one species at ~600 aa while others are 4000+ aa for the same ortholog set), the missing-from-some orthologs are inflating the per-ortholog average — drop them first.
Worked example. data folder has 8 species (4 animal, 4 fungal) *.busco.zip + target_orthologs.txt listing 10 ortholog IDs:
single_copy_busco_sequences/*.faa across all 8 species → ≈80 files → sum AA → answer 32228 (treats every per-species copy independently).single_copy in all 8 species → keep only intersected IDs (often 5/10 — some target IDs are multi-copy/missing in one species) → for kept IDs, sum AA across the 8 species → 13809.When a question asks for a median/percentile/mean across orthologs, your batch must include EVERY ortholog in the relevant comparison set:
scogs_fungi.zip ships ~255 fungal alignments+trees; scogs_animals.zip ships ~241. Median computed from a 10-file sample is NOT the published answer.phykit_batch_analysis, always point at the extracted scogs directory containing all per-ortholog files, not a hand-picked subset.Questions of the form "max X in genes with >70% gaps" require the filter to be applied before the max:
# 1. Compute gap% per alignment
# 2. Keep only alignments with gap% > 70
# 3. Compute treeness/RCV ON THE FILTERED SET
# 4. Take max
Computing the metric across all genes and then taking max returns the global max, which is wrong.
PhyKIT's long_branch_score -v outputs per-taxon LB scores (one row
per leaf in the tree). For per-tree summaries:
phykit long_branch_score -v <tree> → list of
per-taxon scores.Match the per-tree summary to the question phrasing:
| Question says... | Use --per-tree-stat ... |
|---|---|
| "mean long branch scores" | mean |
| "median long branch scores" | median |
| "average long branch score" (ambiguous) | run BOTH and pick the one matching numbers/units |
The bundled scogs_paired_compare.py --metric long_branch_score --per-tree-stat {mean,median} does steps 1+2 for both groups in one
pass and emits the cross-group MWU U + p-value directly.
Common error: averaging the four animal species and four fungal species directly without going through the per-tree step — this conflates species LB and ortholog LB and yields the wrong delta.
phykit toverr (a.k.a. treeness_over_rcv) takes BOTH alignment and tree. Use the trimmed alignment (*.faa.mafft.clipkit) paired with its treefile (*.faa.mafft.clipkit.treefile), not the raw .faa.mafft. ClipKit-trimmed alignments are what produced the canonical tree, so the RCV must be computed on the same trimmed alignment for the ratio to match published numbers.
AlignIO or the AMAS tool to iterate columns and count.Treeness = sum of internal branch lengths / total tree length. Internal branches are those that do not lead to a leaf (tip).
PhyKIT (pip install phykit) provides command-line functions for tree and alignment statistics. Common functions:
phykit treeness <tree_file> — outputs treeness (RCV) valuephykit saturation <alignment_file> -t <tree_file> — outputs saturation valuephykit dvmc <tree_file> — degree of violation of the molecular clockphykit long_branch_score <tree_file> — long-branch score (LBS)phykit alignment_length <alignment_file> — alignment lengthphykit parsimony_informative <alignment_file> — count parsimony informative sitesWhen running PhyKIT on multiple gene trees/alignments, use the bundled batch script:
# Treeness across all trees
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir scogs_fungi --function treeness --ext .treefile --stat median
# Saturation with paired alignment+tree
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir alignments --function saturation --tree-dir trees \
--ext .fa --tree-ext .treefile --stat median
# Long branch score (mean per tree, then median across trees)
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir trees --function long_branch_score --ext .treefile \
--per-tree-stat mean --stat median
# DVMC
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir trees --function dvmc --ext .treefile --stat all
# Gap percentage across all alignments
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir alignments --function gap_percentage --ext .fa
# Evolutionary rate (median across trees)
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir trees --function evolutionary_rate --ext .treefile --stat median
# Mean patristic distance per tree, then mean across trees
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
--dir trees --function patristic_distances --ext .treefile --stat mean
Preferred: use the phykit_batch_analysis ToolUniverse tool instead of running PhyKIT manually:
# Via CLI
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"/path/to/trees","extension":".treefile"}'
# Via SDK
tu.run_one_function({"name": "phykit_batch_analysis", "arguments": {"operation": "batch", "function": "saturation", "directory": "/path/to/alignments", "extension": ".fa", "tree_directory": "/path/to/trees"}})
# Gap percentage
tu run phykit_batch_analysis '{"operation":"gap_percentage","directory":"/path/to/alignments","extension":".fa"}'
Key rules:
"per_tree_stat":"mean"references/sequence_alignment.md, references/tree_building.md, references/parsimony_analysis.md, scripts/tree_statistics.py
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