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Computational Biophysics & Quantitative Biology Skill · mims-harvard | DecimalAI

Scienceby mims-harvard

Computational Biophysics & Quantitative Biology Skill

Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics.

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

100%0%of checks passed -100 pts

Without skill100%

With skill0%

0 fixed0 passed either way22 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v119 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-computational-biophysics
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-computational-biophysics")
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-computational-biophysics

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

Copy SKILL.md

Save it in your project, as

.claude/skills/tooluniverse-computational-biophysics/SKILL.md

This gets SKILL.md only. The skill's 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/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-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/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-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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-computational-biophysics")
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-computational-biophysics/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tooluniverse-computational-biophysics/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 9 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-computational-biophysics

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

Copy SKILL.md

Save it in your project, as

.agents/skills/tooluniverse-computational-biophysics/SKILL.md

This gets SKILL.md only. The skill's 9 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-computational-biophysics/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tooluniverse-computational-biophysics.json

This gets SKILL.md only. The skill's 9 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 7/27/2026
Source: mims-harvard/ToolUniverse ↗
License: Apache-2.0

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

Computational Biophysics & Quantitative Biology Skill

1. Recognize the Physical Process

The single most important step: identify what physical process the problem describes. In quantitative biology, almost every problem maps to one of these:

Drug enters body → distributes → is eliminated: pharmacokinetics. Key quantities: dose, bioavailability, volume of distribution, clearance, half-life. The body is a compartment model.
Radioactive tracer decays over time: nuclear medicine. Same math as drug elimination (exponential decay) but the rate constant is a physical property of the isotope, not a patient variable.
Pathogen spreads through population: epidemiology. R₀ determines whether an epidemic grows or dies. Herd immunity threshold = 1 - 1/R₀. Every epidemic model starts here.
Ligand binds receptor: binding equilibrium. At low [ligand], binding is linear. At saturation, all sites occupied. Kd = concentration at half-maximal binding. This same curve describes enzyme kinetics, drug-receptor occupancy, and surface adsorption.
Contaminant enters environment: dilution + persistence. Two questions: what is the concentration after mixing (conservation of mass), and how long does it persist (exponential decay with environmental half-life)?
Two populations differ genetically: population genetics. Fst measures differentiation. HWE tests if mating is random. Gene flow opposes drift.
Neurons communicate in a network: computational neuroscience. Integrate-and-fire models, synaptic dynamics, balanced excitation/inhibition. Mean firing rate depends on input current relative to threshold.

Once you name the process, the mathematical structure follows. Solve algebraically first, substitute numbers second, and always check that units cancel correctly and the magnitude is physically reasonable.

2. Reasoning Patterns by Problem Type

These are not formulas. They are ways of thinking about what is happening physically.

Conservation / Dilution Problems

Something is being spread into a larger volume, or two streams are mixing. The total amount of substance is conserved. Think: amount_before = amount_after, where amount = concentration x volume. This covers serial dilutions, mixing streams, stock solution preparation, and environmental discharge into rivers.

Exponential Decay / Growth Problems

Something is disappearing (or growing) at a rate proportional to how much is currently there. The signature: "half-life" or "doubling time" appears in the problem. This single pattern covers drug clearance, radioactive decay, environmental persistence, bacterial growth, and epidemic doubling. The only things that change between applications are the rate constant and what is decaying.

Saturation / Binding Problems

Something binds to a limited number of sites. At low concentrations, binding is proportional to concentration. At high concentrations, sites fill up and adding more has diminishing effect. This covers receptor-ligand binding, enzyme kinetics, surface adsorption, and oxygen-hemoglobin curves. The shape is always hyperbolic: response = max_response x [thing] / ([thing] + half_max_constant).

Threshold / Crossover Problems

"At what point does X equal Y?" or "When does the concentration drop below the therapeutic level?" Set two expressions equal and solve. Examples: time to reach a target drug level, when an environmental concentration exceeds a safety limit, herd immunity threshold (where effective R drops to 1).

Ratio / Rate Problems

Output = input x time, or output = concentration x flow rate. Clearance, flux, dosing rate, and drip rate calculations are all just dimensional analysis: arrange the given quantities so the units work out.

Population Comparison Problems

Two groups are being compared. You need a measure of difference (Fst, odds ratio, relative risk) and a measure of whether the difference is real (p-value, confidence interval). Think: what is the effect size, and is it distinguishable from noise?

3. When to Compute vs. Estimate vs. Look Up

Compute carefully when:

The answer affects a patient (drug dosing, diagnostic interpretation)
The problem gives you exact numbers and asks for an exact answer
You need to fit a curve to data (use scipy)

Estimate and state uncertainty when:

The answer needs an order of magnitude (environmental risk, population-level)
Input values are themselves uncertain (R0 estimates, BCF from log Kow regressions)
Say: "This is approximately X, with the main uncertainty coming from Y"

Look up via ToolUniverse when:

You need a physical constant: half-life, molecular weight, Kd, log Kow, allele frequency
The user names a specific drug, compound, gene, or variant
You want to validate your calculation against a known case

Data needed	Tool to use
Molecular weight, log Kow, SMILES	`PubChem_get_CID_by_compound_name`, `PubChem_get_compound_properties_by_CID`
Drug PK properties, mechanism	`ChEMBL_get_molecule`
Binding affinity (Kd, Ki, IC50)	`BindingDB_search_by_target`
Allele frequencies	`gnomad_get_variant`, `MyVariant_query_variants`
Literature values (R0, BCF, etc.)	`EuropePMC_search_articles`

Just compute when:

The problem gives you all the numbers
No specific real-world compound/gene is named

4. Python Computation Templates

CRITICAL: When a problem gives you numbers and asks for a numerical answer, WRITE AND RUN Python code using the Bash tool. Do not try to compute in your head — write a script, execute it, and report the result. Mental arithmetic on multi-step problems introduces errors. The templates below are starting points — adapt them to the specific problem, then EXECUTE.

Answer Format Rules: Match the precision and format the question expects. If data uses 2 decimal places, round to 2. For large numbers (>10^6), use scientific notation; if the question says "in units of 10^28", give just the coefficient. For small numbers, match the question's format (e.g., "1.776 × 10^-3" not "1.8e-3"). Give ONLY the number — no units or descriptions unless explicitly asked.

# Pattern for every computation problem:
# 1. Extract ALL given values from the problem — write them down with units
# 2. Identify EXACTLY what quantity the question asks for
# 3. Write a Python script connecting givens to the unknown
# 4. Run it with: python3 -c "..."
# 5. VERIFY: substitute your answer back into the original problem — does it make sense?
#    e.g., if computing a drip rate, check: rate × time = total volume?
#    e.g., if computing vaccine coverage, check: coverage × efficacy × population > herd immunity?

Template 1: Exponential Decay / Growth

Covers: drug clearance, radioactive decay, environmental persistence, bacterial growth, epidemic doubling.

import numpy as np

def exponential_process(initial, half_life, time):
    """Amount remaining after exponential decay. For growth, use negative half_life."""
    return initial * (0.5 ** (time / half_life))

def time_to_reach(initial, target, half_life):
    """Time for exponential process to reach a target value."""
    return half_life * np.log2(initial / target)

# Examples — same math, different domains:
# Drug: 500 mg dose, t½ = 6 h, after 24 h → 31.25 mg
# Radioactive: 20 mCi Tc-99m, t½ = 6 h, after 12 h → 5 mCi
# Environmental: 100 ppm pesticide, t½ = 30 days, after 90 days → 12.5 ppm

Template 2: Conservation / Dilution / Mixing

Covers: C1V1=C2V2, stream mixing, serial dilutions. Core logic: C1*V1 = C2*V2 (pass 3 knowns, solve for 4th). For mixing n streams: final_conc = sum(Ci*Qi) / sum(Qi).

Template 3: Threshold / Equilibrium Solver

Covers: when drug drops below therapeutic level, herd immunity threshold. Use scipy.optimize.brentq(lambda x: func(x) - target, lo, hi) to find the crossover point.

Template 4: Saturation / Binding Curve

Covers: receptor binding, enzyme kinetics, adsorption, dose-response. Shape: response = Rmax * C / (C + Kd). Fit with scipy.optimize.curve_fit using p0=[median(C), max(response)].

Template 5: Statistical Comparison

Covers: HWE chi-square, contingency tables, group comparisons. Use scipy.stats.chisquare(observed, expected) for goodness-of-fit, stats.ttest_ind/ttest_rel for group comparisons.

Template 6: Rate / Dimensional Analysis

Covers: IV drip rate, clearance, flux, dosing rate. Core: rate = amount / time, mass_rate = concentration * flow_rate. Arrange units to cancel correctly.

Template 7: Compartmental Models & R0

Covers: SIR/SEIR, R0 derivation. R0 = beta * N / gamma (basic SIR). General: R0 = transmission_rate * infectious_duration * susceptible_contacts. Derive by tracing one infected individual through all compartments.

5. Multiple-Choice Strategy

Multiple-choice questions in biophysics, pharmacology, and clinical medicine are frequently answered incorrectly not because of missing knowledge but because of process errors: skipping an option, confusing a letter with the text, or committing to the first plausible-sounding choice. Use the systematic approach below every time.

The Mandatory MC Process

Read the stem twice. Identify the exact action being asked: "MOST likely", "FIRST step", "BEST describes", "EXCEPT". These qualifiers change the answer.
Force evaluation of every choice. For each option ask:
- Why would this be correct? — does it align with the core concept?
- Why would this be wrong? — does the reasoning contradict it, or is it only partially true?
Eliminate with explicit justification. Mark a choice eliminated only when you can state a factual reason (not just a feeling).
Count survivors. One survivor → that is your answer. Two or more → go back to the stem and look for the qualifier that distinguishes them.
Verify letter-to-text alignment. Before writing your answer, confirm the letter you intend to write corresponds to the option text you reasoned about. This catches the common error of reasoning "B is correct" but writing "C".
Quantitative MC: Calculate the exact answer FIRST using Python, THEN match to the closest option. Do not let the listed choices bias your computation — compute independently.
MC traps: "All/None of the above" is correct only ~25% of the time. Absolute language ("always", "never", "only") is usually wrong. The longest/most detailed option is correct more often. When two options are opposites, one is usually correct.

CRITICAL FOR BATCH PROCESSING: When answering multiple MC questions in sequence, do NOT rush. Apply the FULL elimination process to EVERY question. Common batch error: answering based on first impression without elimination. For each MC question, you MUST:

Write out at least 2 eliminated options with reasons BEFORE selecting your answer
If you cannot eliminate any options, that's a sign you need to LOOK UP information

`mc_analyzer.py` — Automated MC Scaffold

Located in skills/tooluniverse-computational-biophysics/scripts/mc_analyzer.py.

# Analysis mode: systematic elimination
python mc_analyzer.py --question "..." --choices "A:opt1,B:opt2,C:opt3,D:opt4" --reasoning "..."

# Verify mode: confirm letter-text alignment
python mc_analyzer.py --verify --answer "B" --question "..." --choices "A:opt1,B:opt2,C:opt3,D:opt4"

Analysis mode scans reasoning for elimination signals, reports survivor count. Verify mode checks letter-text alignment. Use for any scored MC question.

6. Bundled Scripts

These ready-to-run scripts live in skills/tooluniverse-computational-biophysics/scripts/. Use them via the Bash tool instead of computing by hand — they include verification steps and handle edge cases.

`epidemiology.py` — Epidemiology calculations (5 types via `--type`)

Preferred: Use ToolUniverse tools (via MCP/SDK) instead of the script:

Epidemiology_r0_herd tool -- R0 and herd immunity threshold
Epidemiology_vaccine_coverage tool -- Vaccine coverage from field data
Epidemiology_nnt tool -- Number needed to treat
Epidemiology_diagnostic tool -- Diagnostic test performance (2x2 table)
Epidemiology_bayesian tool -- Bayesian pre/post-test probability

Fallback: Pure stdlib script. Types: r0_herd, vaccine_coverage, nnt, diagnostic, bayesian.

python epidemiology.py --type r0_herd --R0 3.5 --VE 0.90
python epidemiology.py --type nnt --control_rate 0.30 --treatment_rate 0.20
python epidemiology.py --type diagnostic --tp 90 --fp 10 --tn 880 --fn 20
python epidemiology.py --type bayesian --prevalence 0.01 --sensitivity 0.95 --specificity 0.90

Key formulas: r0_herd: Hc=1-1/R0, Vc=Hc/VE. vaccine_coverage: derives VE from PCV/PPV field data. nnt: ARR=control-treatment, NNT=1/ARR. diagnostic: full 2x2 table. bayesian: pre-test odds → LR → post-test probability.

`herd_immunity.py` — Legacy vaccination threshold

python herd_immunity.py --R0 4.2 --VE 0.94

Formula: Vc = (1 - 1/R0) / VE. Prefer epidemiology.py --type r0_herd for new work.

`radioactive_decay.py` — Activity remaining / time to target / parent-daughter

Three modes: forward (--A0 --half_life --time), reverse (--A0 --half_life --target), parent-daughter (--parent_daughter with Bateman equation).

python radioactive_decay.py --A0 8 --half_life 67.3 --time 72          # forward
python radioactive_decay.py --A0 8 --half_life 67.3 --target 5         # reverse
python radioactive_decay.py --parent_daughter --half_life_parent 306.05 --half_life_daughter 40.27 --A1 1.4 --A2 2.1 --delta_t 336 --counted daughter

Formulas: A(t) = A0 * 0.5^(t/t_half). Parent-daughter: Bateman equation. --counted: daughter/both/parent.

`env_risk_assessment.py` — Environmental risk (hazard quotient)

Computes HQ for soil contaminant exposure via food pathway. Food format: "name:intake_g:bioavailability:PUF:TSCF".

python env_risk_assessment.py --total_mass_ug 1e9 --area_m2 250000 --depth_m 0.6 \
    --bulk_density 1500 --theta_w 0.35 --foc 0.03 --Koc 28.3 \
    --food "fruit:300:0.5:0.1:5" --body_weight 80 --RfD 0.02

Formulas: C_soil → Kd=foc*Koc → C_sw → DI per pathway → HQ = sum(DI)/BW/RfD.

`fluid_calculations.py` — Clinical fluid and dosing (4 types via `--type`)

Types: drip_rate, bsa_dose, maintenance, dilution. Preferred over legacy iv_drip_rate.py.

python fluid_calculations.py --type drip_rate --volume_ml 50 --time_min 60 --drop_factor 60
python fluid_calculations.py --type bsa_dose --dose_per_m2 25 --bsa 0.8
python fluid_calculations.py --type maintenance --weight_kg 22
python fluid_calculations.py --type dilution --c1 20 --v1 4.7 --v2 50

Formulas: drip_rate = (vol/time)drop_factor. bsa_dose = dose_per_m2BSA. maintenance = Holliday-Segar. dilution = C1V1=C2V2.

`enzyme_kinetics.py` — Km/Vmax, Hill, Ki (3 types via `--type`)

Preferred: use EnzymeKinetics_calculate tool (via MCP/SDK) with type and data parameters. Fallback: run enzyme_kinetics.py directly.

Pure stdlib. Types: km_vmax (Lineweaver-Burk + nonlinear), hill (cooperativity), ki (competitive/uncompetitive/noncompetitive).

python enzyme_kinetics.py --type km_vmax --substrate "1,2,5,10,20" --velocity "0.5,0.8,1.2,1.5,1.7"
python enzyme_kinetics.py --type ki --substrate "1,2,5,10,20" --velocity_no_inh "0.5,0.8,1.2,1.5,1.7" --velocity_inh "0.3,0.5,0.8,1.0,1.1" --inhibitor_conc 5 --inhibition_type competitive

`burn_fluids.py` — Burn resuscitation + maintenance

Adult: Parkland (4*kg*%TBSA). Pediatric (<30kg): Galveston (5000*BSA*%TBSA + 2000*BSA).

python burn_fluids.py --weight_kg 80 --tbsa_pct 40
python burn_fluids.py --weight_kg 25 --age_years 7 --tbsa_pct 45 --bsa_m2 0.95

Output: hourly rates (first 8h / next 16h), total volume, urine output target. 8h clock starts from burn time.

7. Combinatorics & Counting Problems

For genetics combinatorics (F2 haplotypes, genotype counts, specimen tallies) or any counting/permutation/combination problem: ALWAYS write and execute Python code. Never attempt to enumerate or count mentally — even simple-looking problems (e.g., "how many unique chromosomes from 5 SNPs") have subtleties that cause errors without code. Use itertools.product, itertools.combinations, or direct formulas, then verify the count.

8. Common Pitfalls to Flag

Unit mismatch: mg vs g, mL vs L, hours vs seconds. Always write units next to every number and verify cancellation before computing.
Mono- vs multi-exponential: Drug clearance is often biexponential (distribution + elimination phases). Simple half-life decay assumes one compartment. State this assumption.
R0 vs Re: R0 = fully susceptible population. Re = R0 x fraction_susceptible. Most real-world questions want Re.
Single-site estimates are noisy: One SNP's Fst, one patient's response, one measurement's Kd. Always note when genome-wide averages, population means, or replicate experiments would be more reliable.
Regression estimates are order-of-magnitude: BCF from log Kow, toxicity from QSAR. Flag the uncertainty explicitly.
SI units in simulations: Neuron models, diffusion, thermodynamics — always convert to SI (seconds, meters, joules, volts) before computing. Mixed ms/mV causes silent factor-of-1000 errors.

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Automatically, when a task matches. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.

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skills/tooluniverse-computational-biophysics/SKILL.md

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License Apache-2.0Imported 8/2/2026Version v1Bundled 9 files · scripts

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mims-harvard/tooluniverse-computational-biophysicsSolve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.Near-identical description+45 pts pass-rate lift

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Computational Biophysics & Quantitative Biology Skill

1. Recognize the Physical Process

2. Reasoning Patterns by Problem Type

Conservation / Dilution Problems

Exponential Decay / Growth Problems

Saturation / Binding Problems

Threshold / Crossover Problems

Ratio / Rate Problems

Population Comparison Problems

3. When to Compute vs. Estimate vs. Look Up

4. Python Computation Templates

Template 1: Exponential Decay / Growth

Template 2: Conservation / Dilution / Mixing

Template 3: Threshold / Equilibrium Solver

Template 4: Saturation / Binding Curve

Template 5: Statistical Comparison

Template 6: Rate / Dimensional Analysis

Template 7: Compartmental Models & R0

5. Multiple-Choice Strategy

The Mandatory MC Process

mc_analyzer.py — Automated MC Scaffold

6. Bundled Scripts

epidemiology.py — Epidemiology calculations (5 types via --type)

herd_immunity.py — Legacy vaccination threshold

radioactive_decay.py — Activity remaining / time to target / parent-daughter

env_risk_assessment.py — Environmental risk (hazard quotient)

fluid_calculations.py — Clinical fluid and dosing (4 types via --type)

enzyme_kinetics.py — Km/Vmax, Hill, Ki (3 types via --type)

burn_fluids.py — Burn resuscitation + maintenance

7. Combinatorics & Counting Problems

8. Common Pitfalls to Flag

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`mc_analyzer.py` — Automated MC Scaffold

`epidemiology.py` — Epidemiology calculations (5 types via `--type`)

`herd_immunity.py` — Legacy vaccination threshold

`radioactive_decay.py` — Activity remaining / time to target / parent-daughter

`env_risk_assessment.py` — Environmental risk (hazard quotient)

`fluid_calculations.py` — Clinical fluid and dosing (4 types via `--type`)

`enzyme_kinetics.py` — Km/Vmax, Hill, Ki (3 types via `--type`)

`burn_fluids.py` — Burn resuscitation + maintenance