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Antibody Humanizer · leoyeai | DecimalAI

Scienceby leoyeai

Antibody Humanizer

Humanize murine antibody sequences using CDR grafting and framework optimization to reduce immunogenicity while preserving antigen binding. Predicts optimal human germline frameworks and identifies critical back-mutations for therapeutic antibody development.

SkillScore

SkillScore96/100

Test results 96

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

65%96%of test cases passed +30 pts

Without skill15 of 23

With skill22 of 23

8 fixed14 passed either way0 still failing1 broke

Measured on gemini-3.6-flash8/6/2026v122 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWrite the precise CLI command to run `scripts/main.py` on heavy chain sequence `EVQLS...` and light chain sequence `DIVMT...` under the IMGT scheme for candidate `mAb-402`, requesting output saved to `results.json`.

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

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 leoyeai-antibody-humanizer
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("leoyeai-antibody-humanizer")
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 leoyeai-antibody-humanizer

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/leoyeai-antibody-humanizer/SKILL.md https://app.decimal.ai/s/leoyeai-antibody-humanizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-antibody-humanizer/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/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/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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("leoyeai-antibody-humanizer")
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/leoyeai-antibody-humanizer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-antibody-humanizer/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 1 other file is 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 leoyeai-antibody-humanizer

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/leoyeai-antibody-humanizer/SKILL.md https://app.decimal.ai/s/leoyeai-antibody-humanizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-antibody-humanizer/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/leoyeai-antibody-humanizer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-antibody-humanizer.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

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

Antibody Humanizer

Overview

Bioinformatics platform for converting murine antibodies into humanized variants by grafting complementarity-determining regions (CDRs) onto human framework templates while preserving antigen-binding affinity and reducing immunogenicity risk.

Key Capabilities:

CDR Identification: Automatic CDR boundary detection (Kabat/Chothia/IMGT schemes)
Framework Matching: Database search for optimal human germline templates
Humanization Scoring: Multi-parameter immunogenicity risk assessment
Back-Mutation Prediction: Identify critical framework residues for retention
Batch Processing: Humanize multiple antibody candidates efficiently
Immunogenicity Assessment: T-cell epitope and humanness scoring

When to Use

✅ Use this skill when:

Converting murine hybridoma antibodies to therapeutic candidates
Reducing immunogenicity risk of rodent-derived antibodies
Selecting human framework templates for CDR grafting
Identifying critical framework residues for antigen binding
Comparing multiple humanization strategies for lead optimization
Preparing antibody sequences for patent filings
Teaching antibody engineering principles

❌ Do NOT use when:

Fully human antibody generation from phage display → Use phage-display-library
De novo antibody design → Use antibody-design-ai
Affinity maturation → Use affinity-maturation-predictor
ADCC/CDC optimization → Use fc-engineering-toolkit
Final therapeutic candidate selection → Requires experimental validation

Integration:

Upstream: antibody-sequencer (VH/VL sequence determination), cdr-grafting-validator (structural assessment)
Downstream: protein-struct-viz (3D visualization), immunogenicity-predictor (T-cell epitope analysis)

Core Capabilities

1. CDR Region Identification

Parse antibody sequences and identify CDR boundaries:

from scripts.humanizer import AntibodyHumanizer

humanizer = AntibodyHumanizer()

# Analyze antibody sequence
analysis = humanizer.analyze_sequence(
    vh_sequence="QVQLQQSGPELVKPGASVKISCKASGYTFTDYYMHWVKQSHGKSLEWIGYINPSTGYTEYNQKFKDKATLTVDKSSSTAYMQLSSLTSEDSAVYYCAR...",
    vl_sequence="DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYKASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYPYT...",
    scheme="chothia"  # Options: kabat, chothia, imgt
)

# Output CDR locations
print(analysis.cdr_regions)
# {
#   "VH_CDR1": {"start": 26, "end": 32, "seq": "GYTFTDY"},
#   "VH_CDR2": {"start": 52, "end": 58, "seq": "INPSTGY"},
#   ...
# }

Numbering Schemes:

Scheme	VH CDR1	VH CDR2	VH CDR3	Best For
Chothia	26-32	52-56	95-102	Structural analysis
Kabat	31-35	50-65	95-102	Sequence-based work
IMGT	27-38	56-65	105-117	Standardized analysis

2. Human Framework Matching

Identify optimal human germline templates:

# Match against human germline database
matches = humanizer.find_human_frameworks(
    vh_framework=analysis.vh_frameworks,
    vl_framework=analysis.vl_frameworks,
    top_n=5,
    criteria=["homology", "canonical_structure", "vernier_similarity"]
)

# Evaluate each candidate
for match in matches:
    print(f"Template: {match.germline_genes}")
    print(f"Homology: {match.homology:.2%}")
    print(f"Vernier Score: {match.vernier_score:.1f}")
    print(f"Risk Level: {match.immunogenicity_risk}")

Matching Criteria:

Sequence Homology: Percent identity to human germline
Canonical Structure: Loop conformation compatibility
Vernier Region: Framework residues contacting CDRs
Interface Residues: Packing interactions with CDRs

3. Humanization Scoring

Assess immunogenicity risk of candidates:

# Score humanization candidates
scores = humanizer.score_candidates(
    murine_antibody=analysis,
    human_templates=matches,
    scoring_methods=["t20", "h_score", "germline_deviation", "paratope_diversity"]
)

# Rank by overall score
ranked = scores.rank_by_composite_score(
    weights={"humanness": 0.4, "binding_retention": 0.4, "developability": 0.2}
)

Scoring Methods:

Method	Description	Target
T20 Score	20-mer peptide humanization	>80% human
H-Score	Hummerblind germline distance	<15 mutations
Paratope Diversity	CDR germline gene diversity	Low diversity
Developability	Aggregation/pH stability prediction	High score

4. Back-Mutation Prediction

Identify critical residues to retain from murine framework:

# Predict back-mutations
back_mutations = humanizer.predict_back_mutations(
    murine_vh=analysis.vh_sequence,
    human_vh=matches[0].human_template,
    cdr_regions=analysis.cdr_regions,
    rationale_required=True
)

# Output shows position-specific recommendations
for mutation in back_mutations:
    print(f"Position {mutation.position}: {mutation.human_aa} → {mutation.murine_aa}")
    print(f"Rationale: {mutation.reason}")  # e.g., "Vernier region contact"
    print(f"Priority: {mutation.priority}")  # Critical/Important/Optional

Critical Residue Classes:

Vernier Positions: Framework residues contacting CDRs (VH 24, 71, 94)
Interface Packs: Residue packing between VH and VL
Canonical Anchors: Cysteines and conserved framework positions
** Buried Positions**: Core packing residues affecting stability

Common Patterns

Pattern 1: Standard Therapeutic Humanization

Scenario: Convert murine anti-tumor antibody to therapeutic candidate.

# Humanize single antibody
python scripts/main.py \
  --vh "QVQLQQSGPELVKPGASVKISCKAS..." \
  --vl "DIQMTQSPSSLSASVGDRVTITCRAS..." \
  --name "Anti-HER2-Murine-1" \
  --scheme chothia \
  --top-n 3 \
  --output humanization_report.json

# Review top candidates
cat humanization_report.json | jq '.candidates[0]'

Workflow:

Input murine VH/VL sequences
Identify CDRs using Chothia scheme
Match to human germline database
Score top 3 candidates
Identify required back-mutations
Output humanized sequences

Pattern 2: Batch Humanization Screening

Scenario: Screen multiple murine clones from hybridoma campaign.

# Process multiple antibodies
antibodies = [
    {"name": "Clone-A", "vh": "...", "vl": "..."},
    {"name": "Clone-B", "vh": "...", "vl": "..."},
    {"name": "Clone-C", "vh": "...", "vl": "..."}
]

results = humanizer.batch_humanize(
    antibodies=antibodies,
    ranking_criteria="composite_score",
    min_humanness=0.85
)

# Rank by developability
ranked = results.rank_by(criteria=["humanness", "binding_retention", "stability"])

Selection Criteria:

Highest humanness score (>85%)
Fewest back-mutations required (<6)
Low immunogenicity risk
Good developability profile

Pattern 3: Framework Template Comparison

Scenario: Compare different humanization strategies for lead candidate.

# Test multiple framework combinations
strategies = [
    {"vh": "IGHV1-2*02", "vl": "IGKV1-12*01", "name": "Template-A"},
    {"vh": "IGHV3-23*01", "vl": "IGKV3-20*01", "name": "Template-B"},
    {"vh": "IGHV4-34*01", "vl": "IGKV1-5*01", "name": "Template-C"}
]

comparison = humanizer.compare_strategies(
    murine_antibody=analysis,
    strategies=strategies,
    metrics=["homology", "back_mutations", "immunogenicity", "paratope_structure"]
)

comparison.generate_report("framework_comparison.pdf")

Comparison Metrics:

Sequence identity to human germline
Number and location of back-mutations
Predicted immunogenicity risk
CDR conformation preservation

Pattern 4: Intellectual Property Analysis

Scenario: Assess humanization for patent landscape analysis.

# Generate humanized variants
python scripts/main.py \
  --input murine_lead.json \
  --generate-variants 10 \
  --include-back-mutations \
  --output variants_for_ip.json

# Check novelty against patent databases
python scripts/patent_check.py \
  --sequences variants_for_ip.json \
  --databases [USPTO, EPO, WIPO] \
  --output novelty_report.pdf

IP Considerations:

Human framework combinations may be patented
CDR sequences determine antigen specificity
Back-mutation positions may be prior art
Document humanization rationale for filings

Complete Workflow Example

From murine hybridoma to therapeutic candidate:

# Step 1: Sequence analysis and CDR identification
python scripts/main.py \
  --vh $VH_SEQUENCE \
  --vl $VL_SEQUENCE \
  --scheme chothia \
  --output step1_analysis.json

# Step 2: Find best human frameworks
python scripts/main.py \
  --input step1_analysis.json \
  --find-frameworks \
  --top-n 5 \
  --output step2_frameworks.json

# Step 3: Score and rank candidates
python scripts/main.py \
  --input step2_frameworks.json \
  --score-candidates \
  --include-immunogenicity \
  --output step3_scored.json

# Step 4: Predict back-mutations
python scripts/main.py \
  --input step3_scored.json \
  --predict-back-mutations \
  --rationale \
  --output step4_backmutations.json

# Step 5: Generate final humanized sequences
python scripts/main.py \
  --input step4_backmutations.json \
  --generate-sequences \
  --format fasta \
  --output humanized_antibody.fasta

Python API:

from scripts.humanizer import AntibodyHumanizer
from scripts.scoring import HumanizationScorer
from scripts.backmutation import BackMutationPredictor

# Initialize pipeline
humanizer = AntibodyHumanizer()
scorer = HumanizationScorer()
bm_predictor = BackMutationPredictor()

# Step 1: Parse and analyze
antibody = humanizer.analyze_sequence(
    vh_sequence=murine_vh,
    vl_sequence=murine_vl,
    scheme="chothia"
)

# Step 2: Find human frameworks
candidates = humanizer.find_human_frameworks(
    antibody,
    top_n=5
)

# Step 3: Score candidates
for candidate in candidates:
    scores = scorer.calculate_scores(
        murine=antibody,
        humanized=candidate
    )
    candidate.composite_score = scores.weighted_score()

# Step 4: Select best and predict back-mutations
best = max(candidates, key=lambda x: x.composite_score)
back_mutations = bm_predictor.predict(
    murine=antibody,
    human_template=best
)

# Step 5: Generate final sequence
final_sequence = humanizer.generate_humanized_sequence(
    template=best,
    back_mutations=back_mutations,
    cdrs=antibody.cdr_regions
)

print(f"Humanized antibody generated:")
print(f"- Humanness: {best.humanness:.1%}")
print(f"- Back-mutations: {len(back_mutations)}")
print(f"- Risk level: {best.immunogenicity_risk}")

Quality Checklist

Input Quality:

VH and VL sequences complete (110-130 aa typical)
No ambiguous residues (B, Z, X)
Signal peptide removed
Constant region removed (variable region only)

Humanization Assessment:

CDR boundaries correctly identified
Human framework homology >80%
T20 score >75 (high humanness)
Vernier positions analyzed for back-mutations
Interface residues checked for packing

Output Validation:

Humanized sequence valid (no stop codons)
CDRs preserved exactly
Framework length conserved
Back-mutations documented with rationale
CRITICAL: Immunogenicity risk assessed

Before Experimental Work:

CRITICAL: Top 2-3 candidates selected for expression
Binding affinity to be tested (ELISA/Biacore)
Stability assessed (thermal/aggregation)
Immunogenicity in vitro assays planned

Common Pitfalls

Sequence Issues:

❌ Incomplete sequences → Missing framework regions
- ✅ Ensure full VH/VL variable domains provided
❌ Wrong numbering scheme → CDR boundaries incorrect
- ✅ Verify scheme matches experimental data source
❌ Non-standard residues → Unusual amino acids
- ✅ Clean sequences; remove signal peptides

Design Issues:

❌ Over-humanization → Losing antigen binding
- ✅ Don't exceed 85-90% humanness; retain critical residues
❌ Ignoring back-mutations → Assuming 100% human framework works
- ✅ Always predict and test back-mutations
❌ Single candidate only → No backup options
- ✅ Generate 2-3 candidates with different frameworks

Experimental Issues:

❌ Skipping binding validation → Assuming in silico = in vivo
- ✅ Always confirm antigen binding experimentally
❌ Ignoring developability → Aggregation or instability
- ✅ Check for problematic residues (unpaired cysteines, hydrophobic patches)

References

Available in references/ directory:

imgt_germline_database.md - Human germline gene reference sequences
cdr_numbering_schemes.md - Kabat, Chothia, IMGT comparison
humanization_case_studies.md - Successful therapeutic examples
vernier_positions_guide.md - Critical framework residues
immunogenicity_assessment.md - T-cell epitope prediction methods
patent_landscape.md - Humanization IP considerations

Scripts

Located in scripts/ directory:

main.py - CLI interface for humanization
humanizer.py - Core humanization engine
cdr_parser.py - CDR identification and numbering
framework_matcher.py - Human germline database search
scoring.py - Humanization quality assessment
backmutation.py - Critical residue prediction
batch_processor.py - Multiple antibody screening
structure_predictor.py - CDR conformation analysis

Limitations

Binding Prediction: Cannot accurately predict impact on antigen affinity
Developability: Limited prediction of aggregation or stability issues
Immunogenicity: In silico T-cell epitope prediction has false positives
Non-Standard Antibodies: May not handle camelid, shark, or engineered scaffolds
Experimental Validation Required: All predictions must be confirmed in vitro/vivo
Intellectual Property: Does not check for existing patent claims on sequences

Parameters

Parameter	Type	Default	Required	Description
`--vh`	string	-	No	Murine VH sequence (amino acids)
`--vl`	string	-	No	Murine VL sequence (amino acids)
`--input`, `-i`	string	-	No	Input JSON file path
`--name`, `-n`	string	""	No	Antibody name
`--output`, `-o`	string	-	No	Output file path
`--format`, `-f`	string	json	No	Output format (json, fasta, csv)
`--scheme`, `-s`	string	chothia	No	Numbering scheme (kabat, chothia, imgt)
`--top-n`	int	3	No	Number of best candidates to return

Usage

Basic Usage

# Humanize with direct sequence input
python scripts/main.py --vh "QVQLQQSGPELVKPGASVKMSCKAS..." --vl "DIQMTQSPSSLSASVGDRVTITC..." --name "MyAntibody"

# Use JSON input file
python scripts/main.py --input antibody.json --output results.json

# Use IMGT numbering scheme
python scripts/main.py --vh "SEQUENCE" --vl "SEQUENCE" --scheme imgt

Input JSON Format

{
  "vh_sequence": "QVQLQQSGPELVKPGASVKMSCKAS...",
  "vl_sequence": "DIQMTQSPSSLSASVGDRVTITC...",
  "name": "MyAntibody",
  "scheme": "chothia"
}

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python script executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Low
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output may contain proprietary sequences	Medium

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access (../)
Input validation for sequences
Prompt injection protections in place
Error messages sanitized
Output directory restricted to workspace
Script execution in sandboxed environment

Prerequisites

# Python 3.7+
# No external packages required (uses standard library)

Evaluation Criteria

Success Metrics

Successfully parses antibody sequences
Identifies CDR regions correctly
Matches human germline frameworks
Predicts back-mutations
Generates valid humanized sequences

Test Cases

Basic Functionality: Humanize valid VH/VL sequences → Returns candidates
Edge Case: Invalid sequence characters → Graceful error message
File Input: Process JSON input → Correctly parses and outputs

Lifecycle Status

Current Stage: Draft
Next Review Date: 2026-03-06
Known Issues: None
Planned Improvements:
- Add T20 score database integration
- Support for camelid and shark antibodies
- Structure-based CDR prediction

🔬 Critical Note: Computational humanization is a design tool, not a substitute for experimental validation. Always express and test humanized candidates for binding affinity, specificity, stability, and immunogenicity before therapeutic development.

About this skill

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/antibody-humanizer/SKILL.md

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License MITImported 8/2/2026Version v1Bundled 1 file · scripts

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Antibody Humanizer

Overview

When to Use

Core Capabilities

1. CDR Region Identification

2. Human Framework Matching

3. Humanization Scoring

4. Back-Mutation Prediction

Common Patterns

Pattern 1: Standard Therapeutic Humanization

Pattern 2: Batch Humanization Screening

Pattern 3: Framework Template Comparison

Pattern 4: Intellectual Property Analysis

Complete Workflow Example

Quality Checklist

Common Pitfalls

References

Scripts

Limitations

Parameters

Usage

Basic Usage

Input JSON Format

Risk Assessment

Security Checklist

Prerequisites

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

SkillSafety

Source

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