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sn-ppt-entry · opensensenova | DecimalAI

Design & Mediaby opensensenova

sn-ppt-entry

Entry point for PPT generation. Asks the user to choose a mode (fast, standard, or creative), then collects role / audience / scene / page_count as needed. For standard mode, also asks how images should be sourced (AI generation, web search, or none), whether charts should use AI-generated infographics or ECharts, and whether the final deliverable should be PPTX or PDF. Parses uploaded pdf/docx/md/txt files, produces task_pack.json + info_pack.json in a new deck_dir, then dispatches to sn-ppt-cr

Maintained

SkillScore

SkillScore70/100

Test results 70

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

13%70%of test cases passed +57 pts

Without skill3 of 23

With skill16 of 23

13 fixed3 passed either way7 still failing0 broke

Measured on gemini-3.6-flash9/24/2026v218 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen creating a new presentation folder for a project named Cloud Migration Strategy, the workspace metadata lists the workspace as /app/session1, but running pwd -P in the terminal returns /var/agent/workspace/output. Which absolute path must be used to form workspace_root?

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 opensensenova-sn-ppt-entry
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("opensensenova-sn-ppt-entry")
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 opensensenova-sn-ppt-entry

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/opensensenova-sn-ppt-entry/SKILL.md https://app.decimal.ai/s/opensensenova-sn-ppt-entry/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/opensensenova-sn-ppt-entry/SKILL.md

This gets SKILL.md only. The skill's 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/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/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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("opensensenova-sn-ppt-entry")
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/opensensenova-sn-ppt-entry/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/opensensenova-sn-ppt-entry/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 2 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 opensensenova-sn-ppt-entry

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/opensensenova-sn-ppt-entry/SKILL.md https://app.decimal.ai/s/opensensenova-sn-ppt-entry/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/opensensenova-sn-ppt-entry/SKILL.md

This gets SKILL.md only. The skill's 2 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/opensensenova-sn-ppt-entry/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/opensensenova-sn-ppt-entry.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/24/2026
Source: opensensenova/sensenova-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

sn-ppt-entry

统一接收 PPT 生成请求，建立唯一任务目录，准备材料。Standard 先完成定向外部证据补充， Deep 先完成完整 Research，下一步必须调用 sn-ppt-story 生成公共 outline.md，再按 choices.output 分发到 sn-ppt-standard、sn-ppt-dazzle 或 sn-ppt-creative。

这是一套 Skill 的入口，不是独立运行时。文本理解、视觉理解和推理使用宿主 Agent 已有能力。普通搜索、图片搜索和图片生成优先使用宿主原生工具；原生能力不存在或实际调用不可用时，才使用 PPT 整包自带的 sn-ppt-tools。不得要求用户另外配置模型客户端，不得调用 model_client.py、stage.py 或为本流程再造 CLI 调度层。

Box-Agent 兼容入口

在 Box-Agent 中，从本次加载的 Skill 提示取得 Entry 的绝对 Skill Root，记为 <ENTRY_SKILL_ROOT>。Entry 的所有确定性脚本都必须使用 python "<ENTRY_SKILL_ROOT>/scripts/<name>.py" 调用；禁止使用 $SKILL_DIR、当前工作目录或相对的 skills/ 路径。所有任务产物仍必须写入同一个绝对 $DECK_DIR，并在调用 Story 和出口 Skill 时逐字复用该路径。

三个独立选择

执行深度

draft：尽快形成可看的方向稿。默认不做外部事实搜索，大纲完成后直接生产。
standard：质量和等待时间平衡。Story 前由 Entry 做定向外部搜索和证据补充，不运行完整 sn-deep-research；生成前让用户看 outline.md。
deep：先完成 sn-deep-research，再生成正式 outline.md；生成前让用户确认。

Entry 根据任务复杂度、事实时效性、材料完整度和用户措辞推荐一档，同时展示 Draft / Standard / Deep。用户可以覆盖；用户未覆盖时采用推荐，不为这个选择增加一轮阻塞问答。

输出格式

static_html -> sn-ppt-standard
dynamic_html -> sn-ppt-dazzle
creative -> sn-ppt-creative

用户提供已有 PPTX、模板或半成品并要求修改时，直接进入 sn-ppt-edit，不是先走生成出口。Static HTML 仍由 sn-ppt-standard 自己后处理（默认导出 PPTX）。Entry 只负责路由、验证真实文件并登记 artifact。

设计丰富度

restrained（克制）：政企、学术、法务、财务或明确要求简洁。
rich（丰富）：默认，适合多数业务汇报、产品介绍和正式演示。
high_creative（高创意）：发布会、品牌传播、创意提案或明确要求强视觉。

用户明确指定时直接采用；否则结合场景、受众、输出格式和措辞推荐。显示当前选择与三档名称，但不单独阻塞。用户在生成前、确认大纲时或后续修改中覆盖时，更新同一份 task_pack.json。丰富度只决定视觉投入，不改变事实标准和 Story 遵循要求。

唯一目录规则

新任务的输出位置沿用已经验证稳定的原 Entry 规则，不得重新推断：

先执行 pwd -P，把该命令实际返回的完整绝对路径逐字记为 workspace_root。宿主显示的 session workspace、repo 根或其他路径标签都不能替代这次 pwd -P 的结果；尤其不得自行删除返回路径末尾的 output、workspace 等目录名。
唯一父目录是 <workspace_root>/ppt_decks/。
目录名是 <topic_concise>_<YYYYMMDD_HHMMSS>。
创建后在同一条命令中用 pwd -P/绝对化结果回显 deck_dir，立即写入 task_pack.deck_dir，此后全链路只逐字复用该值，禁止根据框架 workspace 信息重新拼接。
不使用用户 home 根、Skill 目录、repo 根、/tmp、另一个 workspace 或 PPT_DECK_ROOT。
不能创建 <workspace_root>/ppt_decks/ 时停止并报告权限问题，不换地方继续。

Box-Agent 的文件工具相对根通常是 <session_workspace>/output，而会话元数据中的 workspace 可能是它的父目录。两者不是同一路径。创建 task_pack.json、info_pack.json、outline.md 和后续产物时必须全部使用上述同一个 deck_dir；每次文件工具返回的绝对落盘路径都要位于该目录。若返回路径不一致，立即停止并修正路径，不得在两个 ppt_decks/ 之间继续。JSON 和 Markdown 直接用文件工具写入，不要用 execute_code 内嵌一个重新推导的绝对路径。

已有任务必须复用已有 task_pack.json 中的绝对 deck_dir；继续任务不得新建目录。所有 Skill 安装目录只读。

`task_pack.json`

创建目录后立即写入，并在每个阶段边界原地更新。它在任务完成后仍然保留，用于进度展示、任务中断恢复和后续编辑。保持字段少而稳定：

{
  "schema_version": "ppt_task_v2",
  "deck_id": "topic_20260729_143015",
  "deck_dir": "/absolute/workspace/ppt_decks/topic_20260729_143015",
  "workspace_root": "/absolute/workspace",
  "ppt_mode": "standard",
  "params": {
    "role": "...",
    "audience": "...",
    "scene": "...",
    "page_count": 8,
    "language": "zh-Hans",
    "image_source": "auto",
    "infographic_source": "echarts"
  },
  "request": {
    "query": "...",
    "source_files": []
  },
  "choices": {
    "execution_depth": "standard",
    "output": "static_html",
    "design_richness": "rich",
    "static_postprocess": ["pptx"]
  },
  "state": {
    "status": "preparing",
    "current_stage": "entry",
    "completed_stages": [],
    "research": {
      "required": false,
      "executor": null,
      "mode": null
    },
    "capabilities": {},
    "artifacts": {},
    "last_error": null,
    "updated_at": "ISO-8601"
  },
  "created_at": "ISO-8601"
}

当 choices.output 是 static_html 时，ppt_mode 必须是 standard，static_postprocess 默认必须是 ["pptx"]。只有用户明确说“只要 HTML”或“不要 PPTX”时才写 []。

其他输出出口的后处理字段写 []，不触发 Static 后处理。

ppt_mode 是旧出口的兼容字段：

`choices.output`	`ppt_mode`
`static_html`	`standard`
`dynamic_html`	`dazzle`
`creative`	`creative`

阶段更新只修改相关字段，不重写用户选择和历史完成项。开始阶段时写 current_stage/status/updated_at；完成时把阶段加入 completed_stages 并把真实产物的绝对路径写入 artifacts；失败时写 last_error。文件系统中的真实产物优先于过时的状态字段。

`info_pack.json`

只承载材料和事实信息，不复制运行状态：

{
  "user_query": "...",
  "user_assets": {
    "reference_images": [],
    "reference_image_captions": {},
    "reference_docs": [],
    "reference_docs_failed": []
  },
  "document_digest": {
    "topic_summary": "...",
    "key_sections": [],
    "key_points": [],
    "data_highlights": [],
    "conflicts": [],
    "open_questions": [],
    "inherited_tables": [],
    "inherited_images": []
  },
  "raw_documents": "/absolute/deck/raw_documents.json",
  "research_report": null
}

document_digest 由宿主 Agent 阅读完整材料后直接写入，不通过额外模型 API。数字、专有名词、时间、单位和材料间冲突必须保真。上传图片和文档内图片继续遵守“一张图片只理解一次”的原有能力：宿主 Agent 用原生视觉能力读取后，把上传图片说明缓存到 reference_image_captions[absolute_path]，把文档内图片说明缓存到 raw_documents.documents[].inherited_images[].visual_summary。已有缓存且图片未变时跳过，下游统一读取缓存；不创建独立 caption 服务。

可选搜索与图像能力

把当前 Skill 的同级目录解析为 skills 根，并固定：

PPT_TOOLS_DIR = <skills root>/sn-ppt-tools

开始需要某项能力时读取 <PPT_TOOLS_DIR>/references/capability-policy.md，按 native -> bundled -> none 选择。sn-ppt-tools 是 PPT 整包的一部分，不做“是否安装” 判断，也不扫描其他仓库。只把每类能力的来源、状态、非敏感错误摘要和更新时间写入 task_pack.state.capabilities；不得记录 key 或 Authorization header。

媒体能力都不是 Entry 的强制前置。缺失时按 policy 继续；只有 Creative 已被明确选择且原生、内置生图都不可用时，暂停 Creative 出口并保留全部前置产物，不自动切换出口。需要用户处理配置时只提示运行 sn-ppt-doctor；不要在 Entry 重复变量清单。Doctor 会显示 Hermes/OpenClaw 实际读取的用户级 .env、缺失项和配置模板。

新任务流程

进入和识别：回显已经进入 Entry。提取角色、受众、场景、页数、语言、附件、明确的设计要求，以及用户是否明确要求 Static 只交付 HTML。已经提供的信息不再询问。
路由已有 PPTX：若任务是编辑、优化、续写或模板填充，交给 sn-ppt-edit。若是从零生成，继续本流程。
推荐三个选择：确定执行深度、输出格式和设计丰富度。只有输出意图确实无法判断且会产生完全不同交付物时才询问；其他情况先给推荐并继续。
建立固定目录：严格按“唯一目录规则”创建目录和初始 task_pack.json。
解析附件：对 PDF、DOCX、MD、TXT 执行：
```
python "<ENTRY_SKILL_ROOT>/scripts/parse_user_docs.py" \
  --files <absolute paths...> \
  --output "<deck_dir>/raw_documents.json" \
  --asset-dir "<deck_dir>/source_assets"
```
Markdown 表格与本地图片引用、DOCX 表格与内嵌图片、PDF 文本、inherited_images 以及按需生成的 page_visuals 都要保留；page_visuals[].path 必须是 $DECK_DIR/source_assets/ 下的绝对路径，并随raw_documents.json 一起交接。未生成页图时，Figure 页裁切任务必须返回 blocked。一份文件失败时记录到 reference_docs_failed，继续处理其他文件。
理解全部材料：读取 raw_documents.json 中全部正文、表格和图片索引；必要时分段阅读，但不得只读开头。用宿主 Agent 原生视觉能力逐张理解相关上传图片和文档内图片，按上述兼容字段缓存，失败项记录后继续。再生成 document_digest，写 info_pack.json。Story 和出口不得重复理解已有缓存的图片，除非文件变化或当前任务确实需要重新核对视觉细节。
启动生成进度工作台：task_pack.json 和 info_pack.json 都已存在后，立即调用 sn-ppt-workbench/scripts/open_workbench.py，始终传入当前任务的绝对 deck_dir 和可用的同会话智能体参数。启动成功后立即向用户提供返回的 generation_url（固定为 /progress）；启动失败或返回 skipped 时简要说明原因并继续生成，不得因此阻塞 Research、Story 或页面生产。不得在此步骤运行 npm、构建 Workbench 或切换任务目录。
决定外部证据路径：
- Draft：默认跳过，写 required=false、executor=null、mode=null。用户明确要求核查某个事实时，可直接用普通搜索完成该核查，但不为 Draft 启动完整 Deep Research。
- Standard：默认写 required=true、executor=entry、mode=null。不得因为“用户材料看起来足够”而跳过；只要用户未禁止联网且普通搜索可用，就必须在 Story 前执行至少一次真实普通搜索。
- Deep：写 required=true、executor=sn-deep-research；默认 mode=normal，复杂、多维、争议、高时效或高风险任务使用 heavy。quick / normal / heavy 只属于 sn-deep-research，不用于描述 Standard。
- 用户明确禁止外部搜索或要求只使用给定材料时，不联网，写 required=false、 executor=null、mode=null 和 skipped_reason=user_forbidden，只做材料内证据审计。
完成外部证据补充：先按可选能力规则确定普通搜索来源：宿主原生网页搜索可用时直接使用；否则调用 <PPT_TOOLS_DIR>/scripts/web_search.py。
- Standard 由 Entry 当前 Agent 做定向搜索，不调用 sn-deep-research，不做 scout、多维拆解、补研循环或完整研究报告编排。搜索目标只来自用户 query、材料中的时效性风险、关键事实和会影响叙事的明显缺口；优先官方和一手来源，记录标题、URL、日期、支持的结论及未解决限制。停止条件是 Story 所需关键证据已经覆盖或缺口已经明确，不为扩写而漫游。把简洁结果写到 <deck_dir>/research/report.md。
- Deep 把普通搜索来源、绝对 report_dir=<deck_dir>/research 和当前任务材料交给 sn-deep-research，按 mode=normal|heavy 完整运行。sn-ppt-tools 只作为搜索能力 fallback，不是另一套 Research 流程。
- 两条路径完成后都把同一个 <deck_dir>/research/report.md 写入 info_pack.research_report 和 task_pack.state.artifacts.research_report，再进入 Story。
- 原生与内置搜索都不可用时，不伪造 Research，也不阻塞 PPT：把 state.research.required=false、state.research.executor=null、 state.research.skipped_reason=search_unavailable 和 info_pack.document_digest.open_questions 中的事实覆盖限制写清楚，然后直接进入 Story。
- 搜索能力存在但 Research 本身尚未完成时，仍不得先写正式大纲。
调用 Story：把同一 deck_dir 交给 sn-ppt-story。Story 读取 query、 info_pack.json、raw_documents.json 和已有 Research，生成唯一 <deck_dir>/outline.md。
大纲交互：Draft 默认继续生产，同时告知大纲路径；Standard / Deep 展示一行整体叙事和路径，等待用户修改或确认。继续前重新读取磁盘上的 outline.md，不能使用聊天中的旧副本。
出口分发：Story 已完成且当前磁盘 outline.md 已按本档位确认后，按 choices.output 调用唯一对应 Skill：static_html 调用 sn-ppt-standard， dynamic_html 调用 sn-ppt-dazzle，creative 调用 sn-ppt-creative。始终传绝对 deck_dir。不得绕过 Story 把原始 query 直接交给任一出口；三个出口都只做表达与产物生产，不再研究、重排页面或重写 Story。
后处理和收尾：Static HTML 默认在页面完成后由 sn-ppt-standard 内置 exporter（scripts/export_pptx/html_to_pptx.mjs）导出 PPTX；该出口必须同时交付 present.html 和 PPTX 两个产物，缺一即技术故障（用户明确要求只要 HTML 时，PPTX 可缺、present.html 仍必须存在）。PPTX 只能由该内置 exporter 生成：禁止用 python-pptx、自写脚本或宿主原生工具替代。Entry 最终把各出口生成的真实产物写回 task_pack.state.artifacts；转换失败保留已有 HTML 产物、状态置 partial 并如实说明，不得另起炉灶补产出或伪造 PPTX 路径。

恢复规则

当用户提供 deck 目录，或当前目录下存在明确的任务包时：

读取 task_pack.json、info_pack.json 和磁盘实际文件。
当前 outline.md 存在时，以磁盘版本为 Story 真相。
Research 报告、outline、页面、渲染图和最终产物存在时，不因状态字段滞后而重做。
从最早一个“必要产物确实缺失”的阶段继续。
用户修改 outline 后，只失效 Story 之后受影响的页面及其派生 PPTX；Research 和材料解析不自动重跑；若 HTML、PNG、讲稿、播放器均完整而仅 PPTX 缺失，只重跑对应出口的 exporter。
不创建 task_pack_v2.json、outline_v2.md 或第二个 deck 目录。

进度反馈

超过约 30 秒的工作必须让用户看到进度。至少在以下边界各回显一句：

已识别任务与三个选择；
已建立 deck_dir；
附件解析开始/完成；
生成进度工作台已启动并提供 /progress，或说明非阻塞的跳过原因；
Research 开始/完成或明确跳过；
Story 开始/outline.md 已生成；
等待确认或已进入出口；
页面生产进度；页面完成后回显 present.html、PNG、讲稿和播放器的真实路径。
后处理与最终产物；PPTX 失败时保留 HTML、PNG、讲稿和播放器，状态为 partial，不得伪造 PPTX 路径，并把失败原因写入 task_pack.state.last_error。

进度以用户能理解的阶段描述为主，不暴露内部 prompt、模型调用或细碎状态字段。

硬规则

原有能力没有被明确删除时必须保留。
不在 Entry 生成页面、图片或视觉方案。
不允许出口自行搜索或重新决定页序、标题和核心结论。
不静默切换输出格式或设计档位。
可选搜索或图像能力失败时最多尝试原生一次、内置一次；随后执行 policy 的无工具路径。
不用 mock 数字冒充事实；缺口应回到 Research/Story 或明确标示。
不因 Static 默认的 PPTX 后处理失败删除可用的 HTML、图片或 PPTX。
不新增外部模型客户端、额外 API 配置、通用状态机或大型调度脚本。
Workbench 启动是生成流程的最佳努力辅助能力；失败不得改变输出选择或中止生成。

About this skill

Automatic

Doc Ops pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/23/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

opensensenova / sensenova-skills

skills/sn-ppt-entry/SKILL.md

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License MITImported 9/23/2026Version v2Bundled 2 files · scripts

Illustrative Want to see what opensensenova/sn-ppt-entry 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

sn-ppt-entry

Box-Agent 兼容入口

三个独立选择

执行深度

输出格式

设计丰富度

唯一目录规则

task_pack.json

info_pack.json

可选搜索与图像能力

新任务流程

恢复规则

进度反馈

硬规则

SkillSafety

Source

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`task_pack.json`

`info_pack.json`