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PPTX creation, editing, and analysis · k-dense-ai | DecimalAI

Design & Mediaby k-dense-ai

PPTX creation, editing, and analysis

Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the u

Never made a case worse

SkillScore

SkillScore88/100

Test results 88

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

48%88%of test cases passed +40 pts

Without skill12 of 25

With skill22 of 25

10 fixed12 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/9/2026v123 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI generated two presentation files: `scratch_deck.pptx` (built from scratch) and `templated_deck.pptx` (built by editing `master_template.pptx`). How should the validation tool be invoked for each file?

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-pptx
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-pptx")
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-pptx

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

Copy SKILL.md

Save it in your project, as

.claude/skills/k-dense-ai-pptx/SKILL.md

This gets SKILL.md only. The skill's 4 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-pptx

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-pptx")
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-pptx/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-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/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-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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-pptx")
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-pptx/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-pptx/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 4 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-pptx

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-pptx")
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-pptx/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/k-dense-ai-pptx/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 4 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-pptx

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

Copy SKILL.md

Save it in your project, as

.agents/skills/k-dense-ai-pptx/SKILL.md

This gets SKILL.md only. The skill's 4 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-pptx/SKILL.md

Read the summary as JSON

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

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: 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

PPTX creation, editing, and analysis

A .pptx is a ZIP archive of XML files. Choose your approach by task:

Task	Approach
Create a new deck	Write a `pptxgenjs` script — see gotchas below
Edit an existing deck, or build from a template	unzip → edit `ppt/slides/slideN.xml` → zip
Read content	`markitdown deck.pptx` (one block per slide under `<!-- Slide number: N -->` markers); visual grid: `python scripts/thumbnail.py deck.pptx`

Scripts

Paths are relative to this skill's directory. Everything else is plain Python, node, or shell.

Script	What it does
`scripts/thumbnail.py deck.pptx [prefix]`	Labeled grid of every slide, for picking template layouts. `.pptx` only. Pass `prefix` — it defaults to `thumbnails`, which overwrites the grids of any other deck done in the same directory
`scripts/add_slide.py unpacked/ slide2.xml [--after slideN.xml]`	Duplicate a slide (or a `slideLayoutN.xml`) with all the package bookkeeping. Also takes a `.pptx` directly with `-o out.pptx`
`scripts/clean.py unpacked/`	Delete slides, media, and rels no longer referenced. Run after `<p:sldIdLst>` is final
`scripts/office/validate.py deck.pptx [--original src.pptx]`	Schema, relationship, content-type, chart and slide checks; each failure names its fix. Pass `--original` for any template-derived deck — it baselines the schema checks against the template, so the template's own XSD errors don't read as yours
`scripts/office/soffice.py --headless --convert-to pdf deck.pptx`	LibreOffice wrapper — bare `soffice` hangs in this sandbox

Creating with pptxgenjs — gotchas

pptxgenjs is preinstalled — do not run npm install first; write the script and require('pptxgenjs') directly. Only if that require fails: npm install pptxgenjs. The model knows the API; these are the footguns:

Set pres.layout before adding slides. The default canvas is LAYOUT_16x9 = 10" × 5.625", not 13.3" wide. Coordinates past the edge are written, not clamped — the shape just isn't on the slide. (LAYOUT_WIDE is 13.3" × 7.5".)
Hex colors: never #, never 8 digits. color: "FF0000". Both "#FF0000" and alpha baked into the hex ("00000020") corrupt the file. For translucency: transparency: 0-100 on fills and images, opacity: 0.0-1.0 on shadows — each is silently ignored on the other.
pptxgenjs mutates option objects in place (converts values to EMU on first use). Never share one shadow/options object across two add* calls — build a fresh object each time.
Shadow offset must be ≥ 0 — a negative offset corrupts the file. To cast a shadow upward, use angle: 270 with a positive offset.
letterSpacing is silently ignored — the real option is charSpacing.
Lists: bullet: true on each item, never a literal • (renders double bullets). Set breakLine: true on every array item except the last. Space bulleted paragraphs with paraSpaceAfter, not lineSpacing (huge gaps).
One new pptxgen() per output file — never reuse an instance.
rectRadius only works on ROUNDED_RECTANGLE, not RECTANGLE.
Gradient fills aren't supported — use a gradient image as the background instead.
Text boxes have built-in internal padding — set margin: 0 whenever text must align with a shape, line, or icon at the same x.
Speaker notes go in slide.addNotes("...") (plain text, once per slide), never in a text box on the slide.
Keep charts native. Use addChart() for everything PowerPoint can chart (pass an array of {type, data, options} for combos). For PowerPoint-native features the library doesn't expose (trendlines, error bars), compute the extra series yourself or post-process the generated OOXML — do not fall back to a rendered image. Only chart types PowerPoint has no native form for (Sankey, network, chord) go in as images.
Default charts render bare — no title, no data labels, dated palette. Set showTitle + title, showValue: true + dataLabelPosition, chartColors: [...] from your palette, and quiet the frame (catAxisLabelColor/valAxisLabelColor, valGridLine: { color, size }, catGridLine: { style: "none" }, showLegend: false for a single series).
On a stacked bar or column chart, dataLabelPosition must be ctr, inEnd, or inBase. outEnd corrupts the file.
A combo series using secondaryValAxis/secondaryCatAxis needs both valAxes and catAxes on the chart options, two entries each. Without them pptxgenjs writes axis ids it never declares, and PowerPoint discards that chart and reports the file as corrupt. Supplying only valAxes is not enough.
After writeFile(), run python scripts/office/validate.py deck.pptx. It reports the two chart faults above and the slide-XML defects PowerPoint refuses, and names the fix for each. Fix them in your generator, not by hand-editing the packed XML.
Never reorder the children of <p:presentation>. pptxgenjs writes <p:notesMasterIdLst> right after <p:sldIdLst> and points both masters at one theme part. PowerPoint reads that happily — move the element and the same deck becomes unopenable.
Icons: render react-icons to SVG (ReactDOMServer.renderToStaticMarkup), rasterize with sharp at ≥256px, and insert via addImage({ data: "image/png;base64," + buf.toString("base64") }) — the image/png;base64, prefix is required (react-icons, react, react-dom, and sharp are preinstalled — npm install react-icons react react-dom sharp only if a require fails).

Editing existing decks and templates

Pick layouts first: python scripts/thumbnail.py template.pptx template-thumbs writes a labeled grid of every slide and prints the file(s) it created — template-thumbs.jpg, split into template-thumbs-N.jpg past 12 slides. Always pass that second argument, named after the deck. It defaults to thumbnails, so two decks thumbnailed in one directory silently overwrite each other's grids — the first deck's are simply gone (template analysis only — visual QA needs the full-resolution renders from Converting to Images; it only accepts .pptx, so copy a .potx to a .pptx name first). Use it with markitdown to map each content section onto a template slide, and vary the layouts — don't put every section on the same title-and-bullets slide.

python3 -c "import sys,zipfile; zipfile.ZipFile(sys.argv[1]).extractall('unpacked')" deck.pptx
python scripts/add_slide.py unpacked/ slide2.xml --after slide2.xml   # duplicate a slide (or slideLayoutN.xml); prints the new slide's path
# reorder / delete slides = edit <p:sldIdLst> in ppt/presentation.xml
python scripts/clean.py unpacked/                                     # after deletions: removes orphaned slides, media, rels
# edit slide content in ppt/slides/slideN.xml
(cd unpacked && rm -f ../out.pptx && zip -Xr ../out.pptx .)           # zip from INSIDE the dir; rm first or deleted parts survive
python scripts/office/validate.py out.pptx --original deck.pptx

Do all structural work — add, delete, reorder — before editing any slide's content. add_slide.py copies a slide file verbatim, so duplicating after you edit clones the edited content; and clean.py deletes any slide missing from <p:sldIdLst>, including one you just wrote.
Never copy a slide file by hand — add_slide.py does every registration a new slide needs and reports what it made (Created ppt/slides/slide17.xml from slide2.xml). It also works directly on a file: add_slide.py deck.pptx slide2.xml -o out.pptx — pass -o, or it rewrites the input deck in place. A duplicated slide still references its source's chart/SmartArt/embedded-object parts rather than cloning them, so editing one slide's chart changes the other's.
If you use python-pptx, three things it won't do: duplicate a slide (its only entry point is add_slide(layout)), preserve formatting through text_frame.text = "..." (that collapses the paragraph to a single unstyled run — assign run.text instead), or read the SVG/EMF most template art uses (add_picture raises UnidentifiedImageError).
Legacy .ppt must be converted first: python scripts/office/soffice.py --headless --convert-to pptx file.ppt. .potx templates unpack and pack identically — keep the .potx extension on the output.
To reuse a template icon or image, duplicate a slide or layout that already contains it.

When filling in a template:

If you script an XML transform, parse with defusedxml.minidom — round-tripping OOXML through xml.etree.ElementTree rewrites namespace prefixes and corrupts the deck.
Template slots ≠ source items. If the template shows 4 team members and you have 3, delete the 4th member's entire group (image + text boxes), not just its text — then check for orphaned visuals in QA.
One <a:p> per list item — never concatenate items into a single paragraph. Copy the sibling <a:pPr> to preserve spacing, and put b="1" on the <a:rPr> of titles, section headers, and inline labels (Status:, Owner:).
Let bullets inherit from the layout; only add <a:buChar>, <a:buAutoNum> (numbered), or <a:buNone> to override — never a literal • in the text.
Text with leading or trailing spaces needs xml:space="preserve" on its <a:t>.

Design Ideas

Don't create boring slides. Plain bullets on a white background won't impress anyone. Consider ideas from this list for each slide.

Before Starting

Pick a bold, content-informed color palette: The palette should feel designed for THIS topic. If swapping your colors into a completely different presentation would still "work," you haven't made specific enough choices.
Dominance over equality: One color should dominate (60-70% visual weight), with 1-2 supporting tones and one sharp accent. Never give all colors equal weight.
Dark/light contrast: Dark backgrounds for title + conclusion slides, light for content ("sandwich" structure). Or commit to dark throughout for a premium feel.
Commit to a visual motif: Pick ONE distinctive element and repeat it — rounded image frames, icons in colored circles. Carry it across every slide. Do not use a color bar or accent stripe as your motif (see Avoid list).

Color Palettes

Choose colors that match your topic — don't default to generic blue. Use these palettes as inspiration:

Theme	Primary	Secondary	Accent
Midnight Executive	`1E2761` (navy)	`CADCFC` (ice blue)	`FFFFFF` (white)
Forest & Moss	`2C5F2D` (forest)	`97BC62` (moss)	`F5F5F5` (cream)
Coral Energy	`F96167` (coral)	`F9E795` (gold)	`2F3C7E` (navy)
Warm Terracotta	`B85042` (terracotta)	`E7E8D1` (sand)	`A7BEAE` (sage)
Ocean Gradient	`065A82` (deep blue)	`1C7293` (teal)	`21295C` (midnight)
Charcoal Minimal	`36454F` (charcoal)	`F2F2F2` (off-white)	`212121` (black)
Teal Trust	`028090` (teal)	`00A896` (seafoam)	`02C39A` (mint)
Berry & Cream	`6D2E46` (berry)	`A26769` (dusty rose)	`ECE2D0` (cream)
Sage Calm	`84B59F` (sage)	`69A297` (eucalyptus)	`50808E` (slate)
Cherry Bold	`990011` (cherry)	`FCF6F5` (off-white)	`2F3C7E` (navy)

For Each Slide

Every slide needs a visual element — image, chart, icon, or shape. Text-only slides are forgettable.

Layout options:

Two-column (text left, illustration on right)
Icon + text rows (icon in colored circle, bold header, description below)
2x2 or 2x3 grid (image on one side, grid of content blocks on other)
Half-bleed image (full left or right side) with content overlay

Data display:

Large stat callouts (big numbers 60-72pt with small labels below)
Comparison columns (before/after, pros/cons, side-by-side options)
Timeline or process flow (numbered steps, arrows)

Visual polish:

Icons in small colored circles next to section headers
Italic accent text for key stats or taglines

Typography

Font names you write into the .pptx are rendered by the user's PowerPoint, not by this environment. Your visual QA renders via LibreOffice, which substitutes fonts it doesn't have — and for some fonts the substitute has different widths, so your QA preview can show text overflow (or fit) that the real deck won't have. To keep your QA trustworthy:

Safe fonts (render true-to-width in QA and ship with Office): Arial, Calibri, Cambria, Times New Roman, Courier New, Bookman Old Style, Century Schoolbook. Use these for body text and anything where fit matters.
Headers with personality at zero QA risk: pair a safe-list serif header (Cambria, Bookman Old Style, Century Schoolbook) with a safe-list sans body (Calibri or Arial). You get visual contrast without giving up reliable overflow checks.
If the user asks for a font outside the safe list (e.g. Georgia or Trebuchet MS): use it where the user asked, but size those containers with extra slack (~10%) and don't trust QA text-fit on those elements — the preview of that font is approximate. If the user hasn't specified, prefer safe-list fonts for body text.
QA-unreliable fonts (substitute has different widths — overflow checks can be wrong): Georgia, Trebuchet MS, Impact, Arial Black, Garamond, Consolas, Palatino Linotype. Calibri Light substitution varies by environment; treat as QA-unreliable. Fine for titles/accents with slack; don't trust QA text-fit on these.
Never default to Aptos — Office's post-2023 default has no metric-compatible substitute here and is missing from older Office installs, so it's unreliable on both ends.

Element	Size
Slide title	36-44pt bold
Section header	20-24pt bold
Body text	14-16pt
Captions	10-12pt muted

Spacing

0.5" minimum margins
0.3-0.5" between content blocks
Leave breathing room—don't fill every inch

Avoid (Common Mistakes)

Don't repeat the same layout — vary columns, cards, and callouts across slides
Don't center body text — left-align paragraphs and lists; center only titles
Don't skimp on size contrast — titles need 36pt+ to stand out from 14-16pt body
Don't default to blue — pick colors that reflect the specific topic
Don't mix spacing randomly — choose 0.3" or 0.5" gaps and use consistently
Don't style one slide and leave the rest plain — commit fully or keep it simple throughout
Don't create text-only slides — add images, icons, charts, or visual elements; avoid plain title + bullets
Don't forget text box padding — when aligning lines or shapes with text edges, set margin: 0 on the text box or offset the shape to account for padding
Don't use low-contrast elements — icons AND text need strong contrast against the background; avoid light text on light backgrounds or dark text on dark backgrounds
NEVER use accent lines under titles — these are a hallmark of AI-generated slides; use whitespace or background color instead
NEVER add decorative color bars or accent stripes — this includes: header/footer bars spanning the slide width, vertical sidebar stripes down one edge of the slide, thin accent stripes along one edge of a card or content block, and "single-side borders" on rectangles. These read as AI-generated filler. If you want to set a card apart, use a subtle background tint, a drop shadow, or an icon — not an edge stripe.
Don't default to cream/beige backgrounds — when no background is specified, use white (FFFFFF) or the user's brand palette; avoid warm-neutral defaults like F5F5DC, FAF0E6, FAEBD7, FFF8E1
Don't ship text that overflows its shape — if text doesn't fit, reduce font size, split across slides, or enlarge the container; never leave content cut off or spilling past bounds

QA (Required)

Your first render usually has a few real issues — overlaps, overflow, misalignment. Find and fix those, re-render only the slides you changed, and stop.

Content QA

markitdown output.pptx

Check for missing content, typos, wrong order.

When using templates, check for leftover placeholder text:

markitdown output.pptx | grep -iE "\bx{3,}\b|lorem|ipsum|\bTODO|\[insert|this.*(page|slide).*layout"

If grep returns results, fix them before declaring success.

File QA (required)

python scripts/office/validate.py output.pptx                      # built from scratch
python scripts/office/validate.py output.pptx --original src.pptx  # built from a template

If the deck came from a template, always pass --original. A template may itself contain parts the XSD rejects, so a bare run can report failures you never caused — and a genuine regression can hide among them. --original baselines the schema and slide checks against the template, suppressing errors it already had. The structural checks — relationships, content types, charts — ignore --original and report template-inherited problems either way, so read those on their own merits.

pptxgenjs emits chart XML PowerPoint refuses to open, and every other tool accepts: python-pptx opens those decks, LibreOffice renders them, the XSD passes them. Every failure names its fix. Fix it in the generator and rebuild.

Visual QA

Convert the slides to images (see Converting to Images) and inspect every one. After staring at the generating code you tend to see what you expect rather than what rendered, so look at the images fresh (a subagent works well for this if you have one). User-visible defects to look for:

Text overflow or text cut off at a box or slide boundary — check this first. It is the most common defect and always user-visible. (For a font the previewer renders unreliably per Typography, the preview is approximate: trust the ~10% slack you left, not its apparent fit.)
Overlapping elements (text through shapes, lines through words, stacked elements)
Source citations or footers colliding with content above
Elements too close (< 0.3" gaps) or cards/sections nearly touching
Uneven gaps (large empty area in one place, cramped in another)
Insufficient margin from slide edges (< 0.5")
Columns or similar elements not aligned consistently
Low-contrast text (e.g., light gray text on cream-colored background)
Template decoration mispositioned after text replacement — e.g., a title underline positioned for one line, but the replaced title wrapped to two
Low-contrast icons (e.g., dark icons on dark backgrounds without a contrasting circle)
Text boxes too narrow causing excessive wrapping
Leftover placeholder content

Converting to Images

Convert presentations to individual slide images for visual inspection:

python scripts/office/soffice.py --headless --convert-to pdf output.pptx
rm -f slide-*.jpg
pdftoppm -jpeg -r 150 output.pdf slide
ls -1 "$PWD"/slide-*.jpg

Pass the absolute paths printed above directly to the view tool. The rm clears stale images from prior runs. pdftoppm zero-pads based on page count: slide-1.jpg for decks under 10 pages, slide-01.jpg for 10-99, slide-001.jpg for 100+.

After fixes, rerun all four commands above — the PDF must be regenerated from the edited .pptx before pdftoppm can reflect your changes.

Dependencies

pptxgenjs (npm, preinstalled — install only if require('pptxgenjs') fails) · markitdown[pptx], Pillow, defusedxml, lxml (pip — text dump, thumbnail, clean, validate) · LibreOffice (soffice, auto-configured for sandboxed environments via scripts/office/soffice.py) · pdftoppm (Poppler)

This skill is created and maintained by Anthropic. Vendored here unmodified except for frontmatter metadata; see LICENSE.txt for terms.

About this skill

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Doc Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

k-dense-ai / scientific-agent-skills

skills/pptx/SKILL.md

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License Proprietary. LICENSE.txt has complete termsImported 8/3/2026Version v1Bundled 4 files · scripts

Illustrative Want to see what k-dense-ai/pptx would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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Use this skill

PPTX creation, editing, and analysis

Scripts

Creating with pptxgenjs — gotchas

Editing existing decks and templates

Design Ideas

Before Starting

Color Palettes

For Each Slide

Typography

Spacing

Avoid (Common Mistakes)

QA (Required)

Content QA

File QA (required)

Visual QA

Converting to Images

Dependencies

SkillSafety

Source

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