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Biotech Pitch Deck Narrative · leoyeai | DecimalAI

Financeby leoyeai

Biotech Pitch Deck Narrative

Use when creating biotech pitch decks, translating scientific data for investors, preparing fundraising presentations, or developing investor Q&A. Transforms complex scientific and clinical data into compelling investor narratives for biotech fundraising.

Never made a case worse

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

86%95%of test cases passed +9 pts

Without skill19 of 22

With skill21 of 22

2 fixed19 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/6/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe have an existing presentation file named `pitch.pptx` for a Series A raise and want to evaluate it using `scripts/main.py`. A team member suggested `python scripts/main.py inspect --file pitch.pptx`. What is the correct command structure?

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-biotech-pitch-deck-narrative
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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative/SKILL.md https://app.decimal.ai/s/leoyeai-biotech-pitch-deck-narrative/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative")
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-biotech-pitch-deck-narrative/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative

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-biotech-pitch-deck-narrative/SKILL.md https://app.decimal.ai/s/leoyeai-biotech-pitch-deck-narrative/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-biotech-pitch-deck-narrative/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-biotech-pitch-deck-narrative/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-biotech-pitch-deck-narrative.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

Biotech Pitch Deck Narrative

Overview

Strategic communication tool that translates complex biotechnology innovations into compelling business narratives optimized for venture capital, pharmaceutical partnerships, and public market investors.

Key Capabilities:

Science Translation: Convert technical data into business value language
Narrative Architecture: Structure Problem→Solution→Market→Traction→Vision flow
Stage Optimization: Tailor messaging for seed through IPO fundraising
Investor Calibration: Adapt for generalist vs. specialist audiences
Risk Mitigation: Frame scientific and regulatory risks as manageable challenges
Q&A Preparation: Anticipate investor questions and prepare responses

When to Use

✅ Use this skill when:

Preparing Series A/B pitch decks for VC presentations
Creating management presentations for IPO roadshows
Developing BD materials for pharma partnership discussions
Crafting executive summaries for grant applications
Rehearsing investor Q&A for earnings calls
Translating clinical data into commercial narratives
Adapting academic presentations for business audiences

❌ Do NOT use when:

Scientific conference presentations → Use technical language
Regulatory submission documents → Use formal FDA/EMA formats
Internal R&D team communications → Use full scientific detail
Patent applications → Use precise legal/scientific terminology
Patient-facing materials → Use lay-summary-gen

Integration:

Upstream: market-access-value (commercial assessment), competitor-trial-monitor (competitive landscape)
Downstream: business-model-canvas (strategy development), investor-relations-prep (ongoing communications)

Core Capabilities

1. Science-to-Business Translation

Convert technical concepts into investor-friendly language:

from scripts.narrative_engine import BiotechNarrativeEngine

engine = BiotechNarrativeEngine()

# Translate technical description
translation = engine.translate_science(
    technical_description="""
    Our proprietary AAV9-based gene therapy utilizes a codon-optimized 
    transgene under control of a liver-specific promoter to restore 
    functional enzyme in patients with MPS I deficiency.
    """,
    audience="generalist_vc",
    preserve_accuracy=True
)

print(translation.business_narrative)
# "One-time gene therapy delivering a functional copy of the missing enzyme, 
#  potentially curing MPS I rather than managing symptoms"

Translation Strategies:

Technical Concept	Business Translation	Why It Works
"CRISPR-Cas9 gene editing"	"Precision genetic medicine platform"	Platform implies scalability
"Phase II clinical data"	"De-risked asset with human proof-of-concept"	Reduces perceived risk
"Off-target effects"	"Industry-leading specificity profile"	Competitive framing
"MOA via JAK-STAT pathway"	"Novel mechanism addressing root cause"	Value proposition

2. Narrative Architecture

Structure pitch deck flow for maximum impact:

# Generate complete narrative arc
narrative = engine.build_narrative(
    company_stage="series_b",
    science_type="gene_therapy",
    clinical_stage="phase_2",
    target_market="rare_disease",
    key_differentiation="one_time_cure"
)

# Access each component
print(narrative.hook)           # Opening grab
print(narrative.problem)        # Market pain point
print(narrative.solution)       # Your approach
print(narrative.traction)       # Validation to date
print(narrative.ask)            # Funding request

Narrative Structure:

Hook (30 seconds): Why this, why now, why you
Problem ($B+ market): Unmet medical need, current standard limitations
Solution: Your technology/platform, mechanism of action
Traction: Clinical data, partnerships, validation
Market: Size, competition, your advantage
Team: Track record, why you'll succeed
Ask: Funding amount, use of proceeds, milestones

3. Stage-Specific Optimization

Calibrate message depth for funding round:

# Optimize for different stages
seed_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="seed",
    focus="team_and_vision"  # Seed cares about team and big idea
)

series_a_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="series_a",
    focus="proof_of_concept"  # Series A needs validation
)

ipo_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="ipo",
    focus="commercial_readiness"  # IPO requires near-term revenue
)

Stage Requirements:

Stage	Key Questions	Focus Areas
Seed ($500K-$2M)	Can you execute?	Team, vision, early validation
Series A ($10-30M)	Does it work?	POC data, IP position, market entry
Series B ($30-75M)	Will it scale?	Phase 2/3 data, BD traction, team expansion
Series C/IPO ($100M+)	Commercial execution	Registration trials, launch prep, revenue path

4. Investor Audience Calibration

Adapt tone and depth for different investor types:

# Calibrate for specific investor
calibrated = engine.calibrate_for_audience(
    narrative=narrative,
    investor_type="healthcare_vc",  # vs "generalist_vc" or "pharma_corp"
    technical_depth="moderate",      # Depth of scientific detail
    risk_tolerance="high"            # Early vs late stage framing
)

Investor Types:

Generalist VC: Focus on market size, business model, team pedigree
Healthcare VC: Balance science rigor with commercial potential
Pharma BD: Emphasize strategic fit, validation data, partnership potential
Public Market: Highlight near-term catalysts, revenue projections, risk mitigation

Common Patterns

Pattern 1: Clinical-Stage Therapeutics

Scenario: Phase 2 biotech raising Series B.

# Generate complete pitch narrative
python scripts/main.py \
  --science "Small molecule inhibitor targeting mutant KRAS G12C" \
  --stage "phase_2" \
  --indication "lung_cancer" \
  --data "ORR 45%, median PFS 6.5 months" \
  --competition "Mirati, J&J" \
  --output series_b_narrative.json

Narrative Elements:

Problem: KRAS mutations in 30% of cancers; previously "undruggable"
Solution: First-in-class covalent inhibitor with superior selectivity
Traction: Phase 2 data showing 45% response rate, durable responses
Market: $15B+ opportunity across multiple tumor types
Differentiation: Best-in-class potency, favorable safety profile
Ask: $75M to complete Phase 3 and prepare NDA

Pattern 2: Platform Company

Scenario: Novel delivery platform company raising seed.

platform_narrative = engine.generate_platform_narrative(
    platform_technology="Lipid nanoparticle for CNS delivery",
    differentiator="Crosses BBB with 50x improvement over existing LNPs",
    applications=["Alzheimer's", "Parkinson's", "brain_cancer"],
    stage="seed",
    target="platform_value_creation"
)

Platform Story Arc:

Platform Thesis: Solving delivery problem unlocks multiple indications
Validation: Proof-of-mechanism in 2+ disease models
Breadth: Pipeline across CNS, oncology, rare disease
**Partnership Appeal": Pharma interest in accessing CNS targets
Scalability: Manufacturing platform supports multiple assets

Pattern 3: MedTech Device

Scenario: Surgical robotics company Series A.

device_narrative = engine.generate_device_narrative(
    device_type="surgical_robot",
    clinical_benefit="50% reduction in complications, 30% faster recovery",
    regulatory_path="510k_de_novo",
    reimbursement="CPT_code_established",
    stage="series_a"
)

Device-Specific Elements:

Clinical Evidence: Superior outcomes vs. standard of care
Economic Value: Cost savings to healthcare system
Regulatory Clarity: Clear FDA pathway, reimbursement strategy
Adoption Strategy: Training, support, key opinion leader engagement

Pattern 4: Pharma Partnership Pitch

Scenario: Out-licensing asset to big pharma.

# Generate BD materials
python scripts/main.py \
  --mode partnership \
  --asset "Phase 2 ready asset" \
  --indication "NASH" \
  --data_package "Phase 1b complete, biomarker validated" \
  --partner_profile "novo_nordisk" \
  --output bd_presentation.json

Partnership Framing:

Strategic Fit: Complements partner's metabolism franchise
Validation: De-risked with human proof-of-mechanism
Value Creation: $500M+ peak sales potential
Deal Structure: Flexible partnership terms proposed

Complete Workflow Example

Building comprehensive fundraising materials:

from scripts.narrative_engine import BiotechNarrativeEngine
from scripts.slide_generator import SlideGenerator
from scripts.qa_prep import QAPreparation

# Initialize
engine = BiotechNarrativeEngine()
slides = SlideGenerator()
qa = QAPreparation()

# Step 1: Generate core narrative
narrative = engine.build_narrative(
    company_stage="series_a",
    therapeutic_area="oncology",
    modality="cell_therapy",
    clinical_stage="phase_1",
    key_differentiation="allogeneic_off_the_shelf"
)

# Step 2: Create slide-by-slide guidance
slide_guide = slides.generate_guide(
    narrative=narrative,
    n_slides=12,
    include_visual_suggestions=True
)

# Step 3: Prepare Q&A
qa_prep = qa.generate_qa(
    narrative=narrative,
    investor_type="healthcare_vc",
    depth="comprehensive"
)

# Step 4: Export complete package
engine.export_package(
    narrative=narrative,
    slides=slide_guide,
    qa=qa_prep,
    output_dir="series_a_pitch_package/"
)

Quality Checklist

Narrative Quality:

Opening hook grabs attention in 30 seconds
Problem is a $B+ market with clear unmet need
Solution is differentiated vs. competition
Traction validates technical and commercial hypotheses
Team has relevant track record
Ask is specific with clear milestones

Translation Accuracy:

Scientific claims remain accurate after simplification
No misleading statements or exaggerated claims
Risk factors disclosed appropriately
Regulatory pathway is realistic
Market size assumptions are defensible

Investor Alignment:

Appropriate for stage and investor type
Addresses likely investor concerns proactively
Financial projections are reasonable
Exit strategy is credible

Before Presentation:

CRITICAL: Legal review of all claims
CRITICAL: Scientific accuracy check by domain expert
Rehearsed with feedback from experienced biotech investors
Backup slides prepared for detailed questions

Common Pitfalls

Translation Errors:

❌ Oversimplification → "Our drug cures cancer" (misleading)
- ✅ "Our drug showed tumor shrinkage in 40% of patients"
❌ Jargon overload → Technical terms without explanation
- ✅ Use analogies: "Like a molecular GPS guiding drugs to tumors"
❌ Hiding risks → No mention of side effects or competition
- ✅ Acknowledge risks with mitigation strategies

Narrative Mistakes:

❌ Technology in search of problem → Cool science, no market
- ✅ Start with problem, solution follows naturally
❌ Ignoring competition → "We have no competitors"
- ✅ Acknowledge competition, explain differentiation
❌ Unrealistic projections → $10B revenue in Year 3
- ✅ Conservative estimates with clear assumptions

Stage Mismatch:

❌ Seed deck with Phase 3 projections → Too far ahead
- ✅ Match milestones to stage-appropriate timelines
❌ IPO presentation to seed investors → Wrong focus
- ✅ Tailor depth and emphasis to investor sophistication

References

Available in references/ directory:

vc_presentation_best_practices.md - Venture capital pitch guidelines
biotech_valuation_models.md - Valuation methodologies by stage
regulatory_pathway_guides.md - FDA/EMA approval timelines
market_sizing_methodologies.md - TAM/SAM/SOM calculations
investor_question_bank.md - Common Q&A by investor type
competitive_landscape_templates.md - Positioning frameworks

Scripts

Located in scripts/ directory:

main.py - CLI interface for narrative generation
narrative_engine.py - Core story architecture
science_translator.py - Technical to business translation
slide_generator.py - Deck structure and visual guidance
qa_preparation.py - Investor Q&A preparation
competitive_analyzer.py - Market positioning analysis
risk_framer.py - Risk mitigation messaging
stage_optimizer.py - Funding round calibration

Limitations

Not Financial Advice: Cannot provide investment recommendations
Regulatory Compliance: Does not ensure SEC or other regulatory compliance
Market Specificity: May not capture niche investor preferences
Real-Time Adaptation: Cannot adjust to live investor reactions
Confidentiality: Does not handle material non-public information protection
Legal Review: All materials require legal counsel review before use

Parameters

Parameter	Type	Default	Required	Description
`--science`	string	-	Yes*	Scientific description of technology
`--stage`	string	-	Yes*	Funding stage (pre-seed, seed, series-a, etc.)
`--audience`	string	-	Yes*	Target audience type (generalist-vc, healthcare-vc, etc.)
`--section`	string	-	No	Section to rewrite (hook, problem, solution, etc.)
`--content`	string	-	No	Content to rewrite
`--input`	string	-	No	Input file path
`--output`, `-o`	string	-	No	Output file path

*Required depending on subcommand

Usage

Basic Usage

# Generate narrative from science description
python scripts/main.py generate --science "CRISPR gene therapy for sickle cell" --stage series-a --audience healthcare-vc

# Rewrite specific section
python scripts/main.py rewrite --section technology --content "We use AAV vectors..." --audience generalist-vc

# Analyze existing pitch deck
python scripts/main.py analyze --input pitch.pptx --stage series-a

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python script executed locally	Low
Network Access	No external API calls	Low
File System Access	Read/write files	Low
Data Exposure	May process confidential business info	Medium
Regulatory	Does not ensure SEC compliance	Medium

Security Checklist

No hardcoded credentials or API keys
No unauthorized file system access
Output does not expose sensitive information
Prompt injection protections in place
Error messages sanitized
Script execution in sandboxed environment

Prerequisites

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

Evaluation Criteria

Success Metrics

Successfully generates pitch narratives
Adapts content to different investor types
Rewrites technical content for business audiences
Provides stage-appropriate messaging

Test Cases

Generate Narrative: Science description → Complete pitch narrative
Rewrite Section: Technical content → Business-friendly version
Audience Adaptation: Same content for different VC types

Lifecycle Status

Current Stage: Draft
Next Review Date: 2026-03-06
Known Issues: Help text in Chinese
Planned Improvements:
- Translate all interface text to English
- Add more investor personas
- Enhance narrative templates

💼 Business Note: Successful biotech fundraising requires balancing scientific credibility with business appeal. This tool helps structure narratives, but the underlying science and team execution ultimately determine success. Always maintain integrity—overpromising destroys credibility with sophisticated investors.

About this skill

Automatic

Sales & GTM pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/biotech-pitch-deck-narrative/SKILL.md

View on GitHub ↗

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

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Biotech Pitch Deck Narrative

Overview

When to Use

Core Capabilities

1. Science-to-Business Translation

2. Narrative Architecture

3. Stage-Specific Optimization

4. Investor Audience Calibration

Common Patterns

Pattern 1: Clinical-Stage Therapeutics

Pattern 2: Platform Company

Pattern 3: MedTech Device

Pattern 4: Pharma Partnership Pitch

Complete Workflow Example

Quality Checklist

Common Pitfalls

References

Scripts

Limitations

Parameters

Usage

Basic Usage

Risk Assessment

Security Checklist

Prerequisites

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

SkillSafety

Source

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