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Genomic Intelligence — DNA Sequence Models · k-dense-ai | DecimalAI

Scienceby k-dense-ai

Genomic Intelligence — DNA Sequence Models

Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow.

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%95%of test cases passed +82 pts

Without skill3 of 22

With skill21 of 22

18 fixed3 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/9/2026v1 · latest is v3

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhat HTTP authorization header format and environment variable name are required when making requests to https://api.genomicintelligence.ai/v1?

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 k-dense-ai-genomic-intelligence
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("k-dense-ai-genomic-intelligence")
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 k-dense-ai-genomic-intelligence

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/k-dense-ai-genomic-intelligence/SKILL.md https://app.decimal.ai/s/k-dense-ai-genomic-intelligence/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/k-dense-ai-genomic-intelligence/SKILL.md

This gets SKILL.md only. The skill's 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/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/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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("k-dense-ai-genomic-intelligence")
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/k-dense-ai-genomic-intelligence/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/k-dense-ai-genomic-intelligence/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 5 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 k-dense-ai-genomic-intelligence

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/k-dense-ai-genomic-intelligence/SKILL.md https://app.decimal.ai/s/k-dense-ai-genomic-intelligence/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/k-dense-ai-genomic-intelligence/SKILL.md

This gets SKILL.md only. The skill's 5 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/k-dense-ai-genomic-intelligence/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/k-dense-ai-genomic-intelligence.json

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3 (v1 tested 8/9/2026)
Source: k-dense-ai/scientific-agent-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

Genomic Intelligence — DNA Sequence Models

Genomic Intelligence (GI) serves transformer DNA language models over six sequence-analysis tasks on managed GPUs. Give it a gene symbol, a genomic region, or a DNA/FASTA sequence; it returns structured predictions — promoter regions, splice sites, enhancer activity, chromatin state, expression (log TPM), and de-novo gene annotation. Nothing runs locally: no model weights, no GPU, no heavy Python stack. It is a thin client over a hosted, versioned inference API.

Official docs: docs.genomicintelligence.ai · REST contract at api.genomicintelligence.ai/v1/openapi.json · hosted MCP server at https://mcp.genomicintelligence.ai/mcp

When to use this skill

Use GI when the user has DNA and wants a model prediction:

Find promoters in a genomic region (promoter)
Predict splice donor/acceptor sites (splice)
Score enhancer activity — developmental & housekeeping (enhancer)
Annotate chromatin state across hundreds of tracks (chromatin)
Predict expression as log(TPM+1) from a sequence + cell-type context (expression)
Annotate genes/transcripts de novo, no reference needed (annotation)
Find the genes in a region and predict each one's expression (composite)

Not for local alignment, variant calling, or file I/O — use a local tool (BioPython, bcftools) for those. GI is for model inference from sequence.

Research and development use. Not for clinical or diagnostic decisions.

Two ways to call GI

Hosted MCP server (keyless; preferred on MCP hosts)

GI hosts an MCP server at https://mcp.genomicintelligence.ai/mcp (Streamable HTTP). When your agent host supports MCP, prefer it: it works keyless against a rate- and concurrency-limited public demo tier, and an optional gi_ bearer key raises those limits. It exposes acquisition tools that return a sequence handle (sequence_ref) and predict_* tools that take that handle, so large sequences stay out of the context. See MCP workflow below and references/mcp.md.

REST API (universal)

Plain HTTP with requests against https://api.genomicintelligence.ai/v1. The REST path requires a GI_API_KEY (a gi_ bearer). Use it on any host, in scripts, or when you need the raw envelope. See Core REST workflow.

Access and authentication

The hosted MCP demo is keyless — try it with nothing set.
REST prediction and job operations need a key, sent as Authorization: Bearer <key>. Public GET /v1/tasks/{task}/models discovery needs no key and is rate-limited by source IP; inspect model windows and bounds before requesting access. See the current authentication contract. Request a prediction key at [email protected].
Never hardcode the key. Read it from the GI_API_KEY environment variable (or a .env via python-dotenv). Never commit keys.

export GI_API_KEY="gi_yourkeyhere"     # optional for MCP; required for REST
export GI_BASE_URL="https://api.genomicintelligence.ai"   # override for staging

Keys are scoped to a partner tier with concurrency and per-minute caps. A 429 means you hit a cap — back off and retry, or ask GI to raise your tier.

The six tasks

Each task is its own published operation with its own request schema, its own minimum length, and its own closed options object — POST /v1/tasks/promoter/predict, /v1/tasks/splice/predict, /v1/tasks/enhancer/predict, /v1/tasks/chromatin/predict, /v1/tasks/annotation/predict, /v1/tasks/expression/predict. Each path is a literal string, so nothing needs to be constructed, and there is no shared PredictRequest schema. Body is {sequence, sequence_name?, model?, options?}, returning a {data, meta} envelope. What differs per task:

Task	Recommended mode	Accepted length	`context_window_bp`	Notes
`promoter`	sync	300–500,000 bp	2,000 bp	sliding-window promoter regions
`splice`	sync	100–500,000 bp	15,000 bp	donor/acceptor sites (long-context BigBird); strand-specific — feed transcript orientation
`enhancer`	sync	50–500,000 bp	249 bp	dev + housekeeping scores (DeepSTARR, Drosophila)
`chromatin`	sync	200–500,000 bp	1,000 bp	hundreds of tracks (DeepSEA)
`expression`	sync	9,198–500,000 bp	n/a (`trained_window_bp` 9,198)	log(TPM+1); needs `tss_index` unless exactly 9,198 bp, plus a cell-type `description`
`annotation`	async	1,000–500,000 bp	n/a	de-novo transcripts; submit + poll; sync JSON above 200,000 bp is `413 sync_too_large`

Recommended mode is guidance, not a constraint — every task accepts both. Omit Prefer for a synchronous 200; send Prefer: respond-async for a 202 plus GET /v1/tasks/jobs/{job_id}. The one enforced limit is per operation: where /v1/openapi.json publishes x-sync-limit-bp on a POST, a synchronous JSON request above that length is 413 sync_too_large — 200,000 bp on annotation and 50,000 bp on the composite workflow in contract revision 16. Read the field rather than memorising the numbers. Annotation BED/GFF3 stays synchronous at any admitted length and can time out; the other five predict tasks have no hard sync cap.

The minimum is admission control, not regime. A request above the floor but shorter than the selected model's bio_spec.context_window_bp is accepted and scored — against a window padded out to the context window. Enhancer is the sharp case: the floor is 50 bp but the context window is 249 bp, so 50–248 bp is scored mostly on padding. Compare your length against context_window_bp from GET /v1/tasks/{task}/models to know whether the model saw real sequence. Longer-than-context input is fine — the scanner steps a prediction window at a time and pads only the final partial window.

Under the floor and over the 500,000 bp cap are both 422 validation_failed at loc ["body","sequence"]; over-length is not a 413. All lengths are measured after whitespace is stripped, so a line-wrapped FASTA body can be pasted verbatim (a > header line still fails the alphabet check).

options is typed and closed (additionalProperties: false) per task — an unknown key is a hard 422 validation_failed with type: "extra_forbidden", never ignored:

Task	`options` keys
promoter	`threshold` (0–1, default 0.5)
splice	`threshold` (0–1, default 0.5), `site_types` (subset of `["donor","acceptor"]`, default both)
enhancer	(none)
chromatin	`threshold` (0–1, default 0.5)
annotation	`batch_size` (1–128, default 8), `shift_coordinates`, `reverse_complement` (default true)
expression	`description` — required, and the only key

Prefer: respond-async is a declared header on all six predict operations and on the composite, not just annotation — see Async.

Omit model and the API uses the task's default — that is the recommended call. Default model IDs are intentionally not documented here: defaults change and retired IDs fail hard, so never hardcode one. To pin a model, or to pick a non-human one (Drosophila, yeast, and Arabidopsis models exist for several tasks), discover IDs at call time with GET /v1/tasks/{task}/models (REST) or list_models (MCP) — and never invent one. Full per-task output shapes are in references/tasks.md.

expression is the strictest of the six: alone among them its schema requires options as well as sequence. Three hard rules it enforces — every violation is a 422, nothing is padded or clamped, and there is no opt-out flag, header, or query parameter:

It always scores exactly one 9,198 bp TSS-centred window — sequence[tss_index-4599 : tss_index+4599]. The endpoint itself accepts 9,198–500,000 bp; anything below 9,198 bp is rejected outright.
tss_index is required unless the sequence is exactly 9,198 bp. It is the 0-based TSS offset into the whitespace-stripped sequence, bounded by 4599 ≤ tss_index ≤ len(sequence) − 4599. At exactly 9,198 bp it defaults to 4,599, the only legal value there. So you may submit a whole locus (up to 500 kb) and let the server cut the window — but the server does not discover the TSS for you (that is the composite workflow's job), and does not reverse-complement: submit gene-sense sequence.
options.description — a cell-type / assay string (e.g. "K562 cells") — is required, and is the only key expression accepts inside options. Unknown top-level body fields are rejected too.

Note: the legal tss_index range is wide, so an offset that is merely wrong (counted over raw FASTA characters including newlines, or relative to a locus start rather than the submitted slice) does not error — it returns a confident 200 for the wrong window. Assert on meta.task_specific_counts.scored_window / .tss_index in the response. The submitted length is meta.sequence_length; the scored width is always 9,198, i.e. scored_window[1] - scored_window[0]. In revision 16, data.input contains only sequence_name, description, and tss_index; it does not contain the submitted length or scored window.

Both tss_index violations — "required unless exactly 9,198 bp" and the range check — come from a whole-model validator, so they surface at the body level rather than under tss_index. Match on error.code == "validation_failed" and use the message for display only. Any loc tuple quoted in this skill is illustrative of that shape, not part of the contract: it is not published in the schema and must not be branched on.

Sequence acquisition

You rarely start from a raw 9,198 bp string. Acquire sequence first:

From a gene symbol → MCP fetch_ensembl_sequence(gene=...); from coordinates → fetch_region(region=...). Both acquire public reference sequence (no key), using a bundled coordinate catalog, cache, UCSC, or Ensembl; retain the returned provenance. REST users can query Ensembl REST directly. (find_genes is the annotation task, not an acquisition tool.)
For expression → use the TSS-centred fetch so the window is exactly 9,198 bp. MCP: fetch_gene_for_expression (handles the centring). Otherwise fetch a wider locus and pass the TSS as tss_index so the server cuts the window — but compute that offset on the stripped nucleotide string, not on file characters.
From a local FASTA → MCP store_inline_sequence, or read the file yourself for REST. (load_local_fasta exists only in local deployments, not on the hosted server.)
A demo sequence → MCP load_demo_sequence(name=...) returns a ready handle for a keyless smoke test; name is required.

See references/sequence-acquisition.md for the exact Ensembl calls and the expression-window math.

Core REST workflow

The following transport recipe was tested with mocked responses, not authenticated inference. Supply a task-appropriate seq before calling it. Use the exact expression-context wording consistently when comparing predictions.

import os
import time
import requests

BASE = os.environ.get("GI_BASE_URL", "https://api.genomicintelligence.ai").rstrip("/")
HEADERS = {"Authorization": f"Bearer {os.environ['GI_API_KEY']}"}
TASKS = {"promoter", "splice", "enhancer", "chromatin", "annotation", "expression"}

def predict(task, sequence, sequence_name, model=None, options=None, tss_index=None):
    if task not in TASKS:
        raise ValueError("Unknown GI task")
    body = {"sequence": sequence, "sequence_name": sequence_name}
    if model is not None:
        body["model"] = model
    if options is not None:
        body["options"] = options
    if tss_index is not None:
        if task != "expression":
            raise ValueError("tss_index is expression-only")
        body["tss_index"] = tss_index
    r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS,
                      json=body, timeout=(10, 300))
    r.raise_for_status()
    if r.status_code != 200:
        raise RuntimeError(f"Unexpected prediction status {r.status_code}")
    return r.json()

# After acquiring and checking an appropriate promoter sequence:
# out = predict("promoter", seq, "TP53_region")
# print(out["meta"]["task_specific_counts"]["regions_found"])

# A validated gene-sense expression window, or longer locus with known TSS:
# out = predict("expression", locus_seq, "HBB", tss_index=tss_offset,
#               options={"description": "polyA plus RNA-seq; Homo sapiens K562"})
# assert out["meta"]["sequence_length"] == len("".join(locus_seq.split()))
# assert out["meta"]["task_specific_counts"]["scored_window"] == [tss_offset-4599, tss_offset+4599]
# print(out["data"]["prediction"]["expression_log_tpm"])

data.summary is for display: its keys may change without a contract revision. Use the declared fields in data and meta.task_specific_counts for computation. A timeout or proxy error may have a non-JSON body; it does not establish that the inference never ran. Preserve the request ID and avoid blind POST resubmission.

Async (any task; recommended for annotation)

Send Prefer: respond-async on any of the six tasks or the composite. A 202 is {data: {job_id, status: "accepted", links}, meta}. Content-Location and X-Job-Id identify the same job. Async is JSON-only; text format plus async is 400. Save the job ID before polling. This bounded polling example surfaces HTTP failures (including 429 and 410) for the caller to handle:

def submit_annotation(sequence, sequence_name):
    r = requests.post(f"{BASE}/v1/tasks/annotation/predict",
                      headers={**HEADERS, "Prefer": "respond-async"},
                      json={"sequence": sequence, "sequence_name": sequence_name},
                      timeout=(10, 30))
    r.raise_for_status()
    if r.status_code != 202:
        raise RuntimeError(f"Unexpected submission status {r.status_code}")
    return r.json()["data"]["job_id"]

def wait_for_job(job_id, max_polls=120):
    if max_polls < 1:
        raise ValueError("max_polls must be positive")
    for attempt in range(max_polls):
        r = requests.get(f"{BASE}/v1/tasks/jobs/{job_id}", headers=HEADERS,
                         timeout=(10, 30))
        r.raise_for_status()  # failed job -> its underlying 4xx/5xx, not 200
        if r.status_code == 200:
            return r.json()
        if r.status_code != 202:
            raise RuntimeError(f"Unexpected polling status {r.status_code}")
        if attempt + 1 < max_polls:
            time.sleep(5)
    raise TimeoutError(f"Polling stopped; resume this job rather than resubmit: {job_id}")

# job_id = submit_annotation(seq, "TP53_region")  # persist this ID
# result = wait_for_job(job_id)
# assert result["data"]["task"] == "annotation"
# transcripts = result["data"]["transcripts"]

200 is completion; 202 contains data.status and data.progress. Unknown/not-owned jobs are 404; expired jobs are 410 job_expired. Results are documented as retained 24 hours from last activity; save results locally. Job listing is a recent, bounded list, not a paginated archive.

MCP workflow (handle-based)

On an MCP host, acquire a handle, then predict against it — sequences stay out of the context:

# 1. Acquire a sequence handle (each returns data.ref, passed as sequence_ref):
load_demo_sequence(name="promoter_tp53")  # keyless smoke test; name is required
fetch_ensembl_sequence(gene="TP53", flank_bp=5000)  # include regulatory context
fetch_region(region="chr11:5,225,000-5,235,000")   # coordinates -> handle
fetch_gene_for_expression(gene="HBB")     # TSS-centred 9,198 bp handle for expression

# 2. Predict against the handle:
predict_promoter(sequence_ref=<ref>)
predict_expression(sequence_ref=<ref>, description="K562 cells")
predict_splice(sequence_ref=<ref>)        # + predict_enhancer / predict_chromatin

# 3. Annotation on MCP is `find_genes` (there is no predict_annotation).
#    It takes a handle, not a region, and runs async internally:
find_genes(sequence_ref=<ref>)            # wait=True (default) returns the result
find_genes(sequence_ref=<ref>, wait=False)  # own key only -> job_id; poll get_job(job_id)

# Acquisition returns data.ref; use that value as sequence_ref.
# Discover models with list_models(task); reference context lives in the
# gi://models, gi://docs/tasks, and gi://account MCP resources.

The shared demo disables get_job, list_jobs, and detached wait=False. Keep wait=True there; a wait timeout is an error, not a recoverable job handle. See MCP details for resources, result envelopes and lifetimes.

Composite: find genes, then predict expression

To answer "what genes are in this region and how are they expressed?", use the composite:

MCP: find_genes_and_predict_expression(sequence_ref=..., description=...) — takes a handle, not a region (acquire one with fetch_region first); description is required. Finds genes in the sequence and returns an expression prediction for each.
REST: one call — POST /v1/workflows/find-genes-and-predict-expression, body {sequence, options} with sequence 1,000–500,000 bp and options.description (cell type / assay) required; a missing or empty description is a 422 validation_failed. It annotates, centres a 9,198 bp window on each discovered gene's TSS (padding with N up to half the window rather than dropping an edge gene), and returns a prediction per gene. meta.task_specific_counts = {genes_found, genes_predicted, genes_skipped} with genes_predicted + genes_skipped == genes_found; per-gene causes in data.expression_predictions[].skip_reason. Above 50,000 bp (its x-sync-limit-bp) it forces async: a synchronous request over that size is 413 sync_too_large with error.details = {sequence_length, threshold} — retry the same body with Prefer: respond-async.

The API also publishes a separate, under-development VCF workflow. Its outputs are not established model results when meta.model is absent; see the bounded contract note.

Errors

Code	`error.code`	Meaning	Action
400	`bad_request`	Malformed request	Check the body shape
401 / 403	`unauthorized` / `forbidden`	Missing/invalid key (REST)	Set `GI_API_KEY`; or use the keyless MCP demo
404	`not_found`	Unknown task (`/v1/tasks/bogus/predict`) or unknown job	Check the task name — an unrecognised task is a 404, not a 422
413	`payload_too_large`	Raw request body over 16 MiB	Split the input — this is the body cap, not the sequence cap
410	`job_expired`	Result retention elapsed	Recover saved results or deliberately submit new work
413	`sync_too_large`	Synchronous JSON request above the operation's `x-sync-limit-bp` (200,000 bp on `annotation`, 50,000 bp on the composite)	Retry with `Prefer: respond-async`
415	`unsupported_format`	Unsupported `format` query value	Use a format the task supports; there is no silent fallback to JSON
422	`validation_failed`	The most common failure: sequence under the task floor or over 500,000 bp, expression below 9,198 bp, a missing/out-of-range `tss_index`, a missing `options.description`, or any unknown body or `options` key; also the splice response cap	Read the message; fix the body
429	`rate_limited` / `too_many_requests`	Rate / concurrency cap	Back off (honour `Retry-After`); ask GI to raise your tier
5xx	`internal_error` / `service_unavailable` / `model_loading` / `timeout`	Server error	Preserve request/job IDs; retry polling with backoff, avoid blind POST resubmission

error.code is a closed 21-value enum (bad_request, unauthorized, forbidden, not_found, conflict, job_expired, payload_too_large, sync_too_large, unsupported_format, validation_failed, too_many_requests, rate_limited, internal_error, timeout, insufficient_memory, model_not_found, task_not_supported_by_model, model_loading, service_unavailable, http_error, unknown); treat an unlisted value as a generic failure, not a parse error.

Branch first on code, never on message text or loc. Pydantic request failures usually carry details.errors; the splice response cap instead carries record_count, maximum_records, sequence_length, and threshold. Handle these as distinct optional detail shapes. More than 20,000 splice records causes 422 validation_failed, not a truncated result; raise the threshold and record that changed analysis setting. See task caveats.

For correlation, error.request_id and the X-Request-Id header are both documented on API responses, and success envelopes carry meta.request_id. Reading the header first remains a safe default. API responses document RateLimit-Limit, RateLimit-Remaining, RateLimit-Reset, RateLimit-Policy; a 429 adds Retry-After. The limit is a burst bucket, not rpm: the published x-rate-limit-burst-divisor is 6, so the sustained minute allowance is six times that header. Proxy failures may omit these headers and the usual JSON error envelope.

Reviewed 2026-10-01 against live OpenAPI info.version 2026.09.22.2 (af902d84), x-contract-revision: 16, and gi-mcp 0.1.0a21. Record the contract revision, resolved model ID, assembly, strand/TSS provenance, options, and experimental description with results. Hash the exact submitted bases when available. A handle-only MCP acquisition returns a preview, not the full bases or a checksum: preserve its acquisition parameters and source release metadata, and do not invent a hash or claim byte-level verification. Review evidence and limits distinguish public discovery, source review, and mocked examples from inference validation.

Reference files

references/tasks.md — per-task output shapes, model registries, the async annotation contract.
references/api-and-auth.md — REST endpoints, the {data, meta} envelope, auth, base-URL override, tiers.
references/mcp.md — the hosted MCP tool list, the handle-based flow, and the gi:// resources.
references/sequence-acquisition.md — Ensembl fetch calls and the expression-window (9,198 bp, TSS-centred) math, including tss_index.

When it activates

Automatically, when a task matches. Use when the user has a gene sy

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k-dense-ai / scientific-agent-skills

skills/genomic-intelligence/SKILL.md

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License MITImported 10/8/2026Version v3Bundled 5 files

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Genomic Intelligence — DNA Sequence Models

When to use this skill

Two ways to call GI

Hosted MCP server (keyless; preferred on MCP hosts)

REST API (universal)

Access and authentication

The six tasks

Sequence acquisition

Core REST workflow

Async (any task; recommended for annotation)

MCP workflow (handle-based)

Composite: find genes, then predict expression

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Reference files

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