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ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg · zenstory-ai | DecimalAI

Design & Mediaby zenstory-ai

ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg

从输入视频生成中文解说成片或原声剧情短片。用户提供 .mp4 / .mov / .mkv / .webm，并要求剪辑、添加旁白、配音、总结、短剧/电视剧/电影/纪录片/科普解说时使用。负责编排 video-* 技能链：视频理解 → Agent 制定故事与视听方案 → 剪辑 → 配音 → 合成。触发词：视频解说、视频旁白、生成解说、视频 recap、video recap、voiceover、narration、auto-dub、recap。

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%100%of test cases passed +82 pts

Without skill4 of 22

With skill22 of 22

18 fixed4 passed either way0 still failing0 broke

Measured on gemini-3.8-flash11/3/2026v5 · latest is v6

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are assembling a cut recap across three source files: part1.mp4, part2.mp4, and part3.mp4. When authoring clip_plan.json during the cut planning stage, what mandatory field must accompany every clip segment entry?

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 zenstory-ai-video-recap
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("zenstory-ai-video-recap")
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 zenstory-ai-video-recap

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/zenstory-ai-video-recap/SKILL.md https://app.decimal.ai/s/zenstory-ai-video-recap/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/zenstory-ai-video-recap/SKILL.md

This gets SKILL.md only. The skill's 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/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/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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("zenstory-ai-video-recap")
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/zenstory-ai-video-recap/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/zenstory-ai-video-recap/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 19 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 zenstory-ai-video-recap

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/zenstory-ai-video-recap/SKILL.md https://app.decimal.ai/s/zenstory-ai-video-recap/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/zenstory-ai-video-recap/SKILL.md

This gets SKILL.md only. The skill's 19 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/zenstory-ai-video-recap/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/zenstory-ai-video-recap.json

This gets SKILL.md only. The skill's 19 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v6 (v5 tested 11/3/2026)
Source: zenstory-ai/video-recap-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

1. 定位与流程

本技能是五个独立技能的轻量编排器。各技能只通过 work_dir 中的 JSON / MP4 产物通信，不共享代码：

video-understanding ─▶ Agent 按 video-script 制定方案并写稿 ─▶ [video-cut] ─▶ video-voiceover ─▶ video-assemble

把成片拆成制作参考不是生产路径。用户要求时：对成片跑 video-understanding（建议 ASR_SEGMENT_SECONDS=5），再用 video-reference 做 measure、标注、check、export，把导出的 production_reference.json 复制进下一次运行的 work_dir，或登记成资源库的 production_reference 模板、采纳后经 --project 绑定。recap 不会自动运行它，也不拿新成片与参考做比对。

流程支持断点续跑：写好 narration.json 后重复同一条命令即可继续。第二阶段会比对 recap_run_manifest.json 记录的源视频路径、文件大小/修改时间与运行参数，拒绝复用来自其他源视频或其他参数的旧工作目录；视频理解产物也只在来源一致时复用。暂停时打印的续跑命令就是原命令：保留原来的写法，视频与路径参数转成绝对路径，补上 --work-dir 和来自环境变量的设置，从任何目录都能直接运行；同一份参数写在 manifest 的 argv。

画面流程 --edit-mode full|cut|dub 与声音策略 --audio-mode 是两个独立开关； narration 保留上述解说流程，source-mix 不做配音，adopted-packet-copy 冻结当前输入的已采用 AAC 音轨。组合只有下面几条路径，其余组合在启动时直接报错：

输入	`--edit-mode`	`--audio-mode`	Agent 暂停点	流程	详见
单视频	full	narration	1：`narration.json`	理解 → 写稿 → 校验 → 配音 → 合成	§4
单视频	cut	narration	2：`clip_plan.json`，再对着成片写 `narration.json`	理解 → 剪辑 → 重建输出时间 brief → 写稿 → 配音 → 合成	§4
多视频	cut	narration	2：同上，clip 必须带 `source_id`	逐源理解 → 剪辑 → 写稿 → 配音 → 合成	§4.3
单视频	full	source-mix / adopted-packet-copy	无	直接合成当前整段	§4.7
单 / 多视频	cut	source-mix / adopted-packet-copy	1：`clip_plan.json`	理解 → 剪辑 → 合成，不写稿	§4.7
单视频	dub	narration	1：`dub_script.json`	英文转写 → 译稿 → 克隆音色整轨替换	§5
已剪好的母版	full	narration + 三个采用 JSON	无	只做严格合成	下文

所有 full/cut 路径共用同一段收尾：（有旁白时）评审 → TTS → 合成 → 成片 QC。使用原声模式时读 references/audio-routing.md。

已有预制画面和本地采用的完整声音三件套时，可走严格 assembly-only 路径：

python3 scripts/recap.py picture.mp4 --edit-mode full --work-dir NEW_WORK \
  --output-dir DELIVERY \
  --tts-meta tts_meta.json \
  --narration-adoption narration_adoption.json \
  --audio-mix-adoption audio_mix_adoption.json

三个 JSON 参数必须同时出现。该入口只接受单视频、full、narration、音轨 0、新工作目录和未存在的交付文件；不运行理解、写稿、解说评审、TTS、cut 或剪映导出。语义与媒体形状仍由 video-assemble 严格验证，recap 只核对子技能绑定记录引用的是同一批采用文件与母版路径，不把调用方采用的声音或混音声明成自动创作或发布批准。详见 references/audio-routing.md。

这里的单视频是已经剪好的母版。重剪后可以复用未改动的 WAV 与 tts_meta.json，但必须按新母版重新写混音采用文件里的落点与准备好的音床；衔接步骤见 references/audio-routing.md 的 “Keep adopted voice after a cut”。

2. 创作职责

Agent 负责本次创作。先判断本轮的创作控制模式（CREATE / DIRECTED / REVISION，与 --edit-mode 分别选择），再在配音和渲染前完成五次判断：

导演判断：观众承诺、POV、戏剧问题、情绪终点与揭示节奏。
故事编辑：beat 定义为“发生了什么变化”，不是场景摘要。
画面剪辑：选择真正值得保留的具体时刻、反应、入点与出点。
声音/旁白：先分配画面、原声、沉默和旁白的任务，再写解说词。
观众复核：分别检查无旁白、只听声音和第一次观看的体验。

三种控制模式的定义、REVISION 的修改/冻结规则、创作方法以及 recap_story_plan.json / visual_audio_board.json / style_card.json 的写法，全部按 video-script 执行；它会要求先读创作手册。这些文件只记录可审计的当前决定，不增加服务或渲染依赖。

3. 环境与脚本路径

# ffmpeg: brew install ffmpeg | apt install ffmpeg | choco install ffmpeg
export MIMO_API_KEY=***

同一个 MiMo key 驱动：

ASR：mimo-v2.5-asr
VLM：mimo-v2.5
TTS：mimo-v2.5-tts

TTS 供应商由 --tts-provider mimo-tts|fish-audio|index-tts（或 TTS_PROVIDER）透传给配音技能；Fish Audio 与自托管 index-tts 各自的环境变量、默认音色和能力限制见该技能。ASR/VLM 始终使用 MiMo。--doctor 只做离线配置检查。

tp-* Token Plan 密钥默认使用中国区集群，可用 MIMO_TOKEN_PLAN_CLUSTER 覆盖。

可选能力：

--mimo-video-overview：按场景块补充 MiMo 视频理解。

可覆盖配置见 references/config-playbook.md，final_qc.json 的字段见 references/data-schema.md。

下面的 scripts/... 均相对于本技能目录。若执行器从仓库根目录启动，请给脚本路径加上本技能的绝对目录。编排脚本从同一技能根目录定位所需阶段；安装时需让五个阶段技能与本技能位于同一层。

4. 标准解说流程（audio-mode narration）

4.1 背景调研

若能识别影片、剧集或主题，先按 video-understanding 技能的调研指南 research-guide.md 调研并写入 work_dir/background_research.json。视频理解会把人物名和剧情背景折入 VLM 上下文，避免只得到“黑衣男子”一类模糊描述。无法识别来源时可跳过。多视频运行同样写在项目 work_dir 下：recap 在每个来源理解前把它复制到 sources/<source_id>/（来源目录里没有、或比项目文件旧时才复制，所以某一集需要单独的调研时，在项目文件之后写入该来源目录即可）。

4.2 分析并暂停创作

python3 scripts/recap.py <video> --work-dir <work_dir> --context "背景"

命令完成视频理解、写出 agent_narration_brief.md，然后暂停。此时按以下顺序执行 video-script：

查看创作 brief 与原片故事板。
写 recap_story_plan.json 和 visual_audio_board.json。
full 模式写 narration.json；cut 模式第一阶段只写 clip_plan.json。
cut 模式第二阶段查看剪后故事板，补充输出时间与声音分工，再写 narration.json。

不要从标题或旁白句子开始；先锁定故事体验和素材选择。

时间线有两条不可降级的硬约束：原声只能在可靠句末/静音边界被切入、切出或恢复；旁白必须使用完整逐段音频，任何 clip 映射裁段、TTS 裁尾或剪映引用更长的加速前素材都阻断。Agent 收到 interrupts_source_sentence / unsafe_clip_sentence_boundary / no_safe_fit / timeline_audio_mismatch 时，应移动边界、缩短整句或删除该块，而不是增加抢断 override； unsafe_clip_sentence_boundary 附带的 nearest_safe 给出前后最近的安全边界时间。

4.3 多视频与素材库

多视频只支持 cut 模式。项目 brief 会列出稳定的 source_id，clip_plan.json 中每个片段都必须填写来源：

python3 scripts/recap.py ep1.mp4 ep2.mp4 --edit-mode cut --target-duration 10m --work-dir work_dir_multi_ep

第二阶段与单视频一样生成剪后故事板 storyboard/edited_storyboard.*，取自各来源 sources/<source_id>/frames/，brief 顶部列出 S1/S2… 对应的 source_id；素材库恢复的来源没有抽帧，这些片段不出现在故事板里，用 inspect clip-map 核对。

可选文件系统素材库：

python3 scripts/recap.py ep1.mp4 --material-library-dir .video-materials --save-materials
python3 scripts/recap.py ep1.mp4 ep2.mp4 --edit-mode cut --material-library-dir .video-materials --use-materials

素材检索只是对 JSON / MD / JSONL 做 grep，例如 grep -R "keyword" .video-materials。当前版本不复制原始媒体，也不提供数据库、向量或语义搜索。

同一根目录还可以登记可复用的资源（BGM、音效、音色、字体、图片）、带版本与采用记录的模板（字幕样式、包装图层、制作参考）和样片。格式与 scripts/library.py check|list|show 只读工具见 references/resource-library.md。用 --project recap_project.json 把已采用的字幕样式、包装、制作参考、音色与 BGM 绑定到这次运行；每次 full / cut 合成后 work_dir/resource_lock.json 记下实际用到的资源与授权状态。

4.4 继续生成成片

写好所需产物后，重复同一条命令：

python3 scripts/recap.py <video> --work-dir <work_dir>  # 可追加 --edit-mode cut / --no-burn-subtitles

流程会校验当前阶段的硬输入（clip_plan.json / narration.json）；两份创作计划仍是 Agent 与建议型评审使用的工作记录，不是渲染门禁。cut 模式随后生成 edited_source.mp4，再合成旁白并输出 recap_<name>.mp4。

校验从不改写解说稿（只更新实测的 overlaps_speech）；full 模式下文本装不下时间窗会以 over_budget error 退回给 Agent。已有批准解说稿时加 --preserve-approved-text，TTS 也原样保留批准稿，装不下时间窗即失败，不缩稿、不降级为部分成功。

4.5 字幕与克隆旁白

若要把旁白字幕固定在原片字幕区域，先测量位置。完整源码安装可在仓库根目录运行以下工具；单独安装技能时，由 Agent 查看视频帧测出同一显示画布上的上下边界：

python3 tools/measure_subtitle.py <video>

再传入测得的 --subtitle-y-top/--subtitle-y-bot。坐标基于 ffmpeg 自动旋转后的显示画布，区间为半开 [top, bot)，并要求底对齐 ASS 样式；显式设置后，该区域默认使用不透明的旁白窗口遮罩；可用 SUBTITLE_MASK_OPACITY 调整。

解说模式如需克隆参考声音，使用 --voice-ref <audio>；它与 dub 模式不同。

4.6 最终观看与交付复核

脚本、接点检测、样帧和 QC 报告都不能替代观看。每轮准备交付前，必须检查本轮实际要交付的最终文件，而不是旧别名、无字幕母版或中间代理：

正常速度完整播放一次短片，不边看边改；先记录真实观看问题。
播放每个拼接点前后约 0.5–1 秒，检查闪帧、原片叠化被截断、动作跳变和半句原声。
完整只听声音一次，检查旁白是否碎成一句一停、场景间声音是否接得上、关键原声是否完整。
单独复看开头、核心情绪/表演点和结尾，确认进入时机、回报停留和收束都成立。
REVISION 分别验证本轮修改项已经改变、冻结项没有意外变化；然后再做解码、时长、音画规格等机械检查。

scene score、亮度统计、抽帧拼图与自动 QC 只负责定位候选问题；最终判断以真实播放为准。密集切点的来源判断与处理规则按剪辑技能执行。修复失败时回到剪点、声音或文案层，不用更多包装掩盖。

full/cut 交付如需让确定性的最终检查影响命令退出状态，显式传 --require-final-qc。只有 final_qc.json 的摘要为 ok: true 且整数 blocker_count: 0 才打印完成并返回成功；缺失、畸形或 blocker 会保留报告和已渲染诊断媒体，但命令非零退出且不打印完成。默认仍是仅报告、不阻断。该参数不支持 --edit-mode dub；dub 未传该参数时的准备和渲染行为不变。

交付时读 final_qc.json 的 metadata.warnings，有内容就原样告诉用户，不能只报“完成”。subtitle_burn_degraded 表示 ffmpeg 缺 libass、默认烧录被降级：新字幕保存在成片旁的同名 .srt，原片已有硬字幕仍保留。用户要烧录字幕，就请他装带 libass 的 ffmpeg 后重跑；显式传 --burn-subtitles 时缺 libass 会在开跑前报错。旁白里写了 visual_overlays 而 ffmpeg 缺 drawtext 时，流程在配音前停下，按报错删掉叠加或换 ffmpeg 后续跑。

4.7 不需要解说的片子

# 对当前整段输入直接合成；不隐式跑理解/ASR/TTS
python3 scripts/recap.py locked_picture.mp4 --work-dir source_work --audio-mode source-mix
# 剪辑计划仍按 cut 流程产生，剪完不再暂停等待 narration.json
python3 scripts/recap.py ep1.mp4 ep2.mp4 --edit-mode cut --work-dir cut_work --audio-mode source-mix
# 只换包装时冻结当前整片 AAC；不允许同时加 BGM/TTS
python3 scripts/recap.py adopted.mp4 --work-dir packaging_work --audio-mode adopted-packet-copy

source-mix 仍会混音和重编码；cut + adopted-packet-copy 冻结的是剪后中间片的声音，不是原片的 AAC 包。当前严格字幕轨只支持 adopted 模式；其他字幕来源没有因此变成精确对齐。切换声音模式须新工作目录，不得把旧 TTS、QC 或自动生成的解说花字混入本轮原声生产。细节见 references/audio-routing.md。

5. 英译中原声复刻模式

--edit-mode dub 是实验功能：把英文视频翻译为中文，并用原说话者的克隆音色替换人声；它不是在压低原声上叠加解说。只在用户明确要求英译中配音时使用。

dub 会把源视频音轨分窗发给 MiMo ASR 转写，并把截取的约 10 秒原说话人声音作为参考，连同每句译文发给 MiMo voiceclone。因此必须带 --confirm-voice-rights：先向用户确认他有权使用这段视频与音频、且说话人同意被克隆声音，确认后才加这个参数；缺少时启动即报错，不抽音频、不发请求。该参数只用于 dub，其他模式传入会报错。

python3 scripts/recap.py <video> --edit-mode dub --confirm-voice-rights --work-dir <work_dir>

准备阶段会转写英文、提取一段参考音频，并写出 dub_brief.md 与 dub_transcript.json。Agent 按 dub_brief.md 里的翻译要求（逐句忠实、时间窗、语速）写 dub_script.json：

[{"start": 0.0, "end": 2.0, "zh": "中文译文"}]

重复同一命令后先做确定性 lint（dub_lint.json，空行、重叠、越界即中止），再输出 dub_<name>.mp4。每句单独克隆并贴回原时间线；只有即将覆盖下一句时才局部加速。当前版本只支持单说话者、整轨替换，不分离背景音乐。

6. 自检与只读 dashboard

python3 scripts/recap.py --doctor

6.1 只读 dashboard

python3 scripts/dashboard_server.py --root <目录> [--port 0] [--open]

前台运行并打印本机地址（只绑定 127.0.0.1，Agent 启动时放到后台）。它在 --root 下按 library.json、recap_project.json、 recap_run_manifest.json 发现资源库、项目与运行，按阶段显示剪辑节奏、旁白、成片与时间线、QC 和 resource_lock.json。严格只读：只接受 GET / HEAD，不写任何文件；页面上的「复制给助手」只复制一句请求，改动回到对话里做。

7. 输出与参数

主要输出：

recap_<video>.mp4：最终成片。
subtitles.srt / subtitles.ass：字幕。
work_dir/：全部中间产物，契约见 references/data-schema.md。
work_dir/recap_story_plan.json / visual_audio_board.json：Agent 创作意图与剪辑决定。

完整参数列表以 python3 scripts/recap.py --help 为准。--style 是原样传给 Agent 的自由文本指导，不是 preset、枚举、开关或有限风格分类。

8. 能力边界

语义评审默认建议型、失败开放；只有调用方显式启用严格解说评审时，事实矛盾、残句或评审不可用才会在 TTS 前阻断。确定性校验阶段始终负责硬校验。
宣发标题、花字或外部文案回填交给 video-script，由它读取自己的宣发参考。

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skills/video-recap/SKILL.md

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License MITImported 11/8/2026Version v6Bundled 19 files · scripts

Illustrative Want to see what zenstory-ai/video-recap 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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1. 定位与流程

2. 创作职责

3. 环境与脚本路径

4. 标准解说流程（audio-mode narration）

4.1 背景调研

4.2 分析并暂停创作

4.3 多视频与素材库

4.4 继续生成成片

4.5 字幕与克隆旁白

4.6 最终观看与交付复核

4.7 不需要解说的片子

5. 英译中原声复刻模式

6. 自检与只读 dashboard

6.1 只读 dashboard

7. 输出与参数

8. 能力边界

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