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剪口播 v3 · aiskillstore | DecimalAI

Design & Mediaby aiskillstore

剪口播 v3

口播基础素材包生成。转录口播、识别口误、生成审核页；用户确认后剪出新视频，Agent 再基于剪后视频重新转写、AI 校对字幕，输出后续口播成片可用的 source_cut.mp4 和 subtitles.srt。触发词：剪口播、处理口播素材、准备口播素材、识别口误、基础素材包

Maintained

SkillScore

SkillScore63/100

Test results 63

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

21%63%of test cases passed +42 pts

Without skill5 of 24

With skill15 of 24

10 fixed5 passed either way9 still failing0 broke

Measured on gemini-3.6-flash9/4/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen `volcengine_transcribe.sh` calls the Volcengine Speech 2.0 bigmodel submission endpoint, what resource ID header value is passed by default?

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 aiskillstore-videocut
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("aiskillstore-videocut")
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 aiskillstore-videocut

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/aiskillstore-videocut/SKILL.md https://app.decimal.ai/s/aiskillstore-videocut/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-videocut/SKILL.md

This gets SKILL.md only. The skill's 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/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/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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("aiskillstore-videocut")
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/aiskillstore-videocut/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-videocut/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 8 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 aiskillstore-videocut

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/aiskillstore-videocut/SKILL.md https://app.decimal.ai/s/aiskillstore-videocut/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aiskillstore-videocut/SKILL.md

This gets SKILL.md only. The skill's 8 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/aiskillstore-videocut/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-videocut.json

This gets SKILL.md only. The skill's 8 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/4/2026
Source: aiskillstore/marketplace ↗
License: Not recorded

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

剪口播 v3

火山引擎转录 + AI 口误识别 + 网页审核 + 剪后重转写 + AI 字幕校对

快速使用

用户: 帮我剪这个口播视频
用户: 处理一下这个视频
用户: 把这条录屏做成基础素材包

不要再把字幕生成拆成单独 Skill。剪口播 是口播基础素材包的唯一入口。

用户给一条口播录屏后，本 Skill 默认产出后续 口播成片 可直接使用的基础素材包：

source_cut.mp4       # 剪后口播视频
subtitles.srt        # 基于剪后视频重新转写并 AI 校对后的字幕
assets/              # 可选截图、评论图、结果页

审核页只负责让用户确认“删哪里”并剪出新视频。字幕不能沿用原始视频字幕，也不能把火山转写初稿直接当最终字幕；Agent 必须基于剪后视频重新转写，并做 AI 校对后，才能写入 subtitles.srt。

输出目录结构

output/
└── YYYY-MM-DD_视频名/
    ├── 剪口播/
    │   ├── 1_转录/
    │   │   ├── audio.mp3
    │   │   ├── volcengine_result.json
    │   │   └── subtitles_words.json
    │   ├── 2_分析/
    │   │   ├── readable.txt
    │   │   ├── auto_selected.json
    │   │   └── 口误分析.md
    │   └── 3_审核/
    │       ├── review.html
    │       └── video.mp4 → 源视频(符号链接)
    └── 字幕/
        ├── 1_转录/
        │   ├── audio.mp3
        │   └── volcengine_result.json
        ├── subtitles_with_time.json
        └── 3_输出/
            ├── video.raw.srt
            └── video.srt

规则：已有文件夹则复用，否则新建。

流程

0. 创建输出目录
    ↓
1. 提取音频 (ffmpeg)
    ↓
2. 调用火山引擎录音文件识别 2.0（Seed ASR v3）
    ↓
3. 生成 volcengine_result.json
    ↓
4. 生成字级别字幕 (subtitles_words.json)
    ↓
5. AI 分析口误/静音，生成预选列表 (auto_selected.json)
    ↓
6. 生成审核网页 (review.html)
    ↓
7. 启动审核服务器，用户网页确认
    ↓
【等待用户确认】→ 网页点击「执行剪辑」
    ↓
8. 服务器只剪出新视频
    ↓
9. Agent 监听剪后视频生成完成
    ↓
10. 基于剪后视频重新提取音频，发火山转写，生成 video.raw.srt
    ↓
11. Agent 对照原稿/正文做 AI 校对，写 video.srt
    ↓
12. 整理基础素材包：source_cut.mp4 + subtitles.srt + 可选 assets/

剪后字幕硬规则：

必须基于剪后视频重新转写，不能用原始视频字幕反推。
火山转写只是初稿，输出到 video.raw.srt。
video.srt / subtitles.srt 只能是 Agent AI 校对后的最终字幕。
校对时对照用户给的口播稿、正文、专名和上下文，修正术语、同音误识别、断句；不能添加视频里没说的话。

执行步骤

步骤 0: 创建输出目录

# 变量设置（根据实际视频调整）
VIDEO_PATH="/path/to/视频.mp4"
VIDEO_NAME=$(basename "$VIDEO_PATH" .mp4)
DATE=$(date +%Y-%m-%d)
BASE_DIR="output/${DATE}_${VIDEO_NAME}/剪口播"

# 创建子目录
mkdir -p "$BASE_DIR/1_转录" "$BASE_DIR/2_分析" "$BASE_DIR/3_审核"
cd "$BASE_DIR"

步骤 1-3: 转录

cd 1_转录

# 1. 提取音频（文件名有冒号需加 file: 前缀）
ffmpeg -i "file:$VIDEO_PATH" -vn -acodec libmp3lame -y audio.mp3

# 2. 调用火山引擎录音文件识别 2.0（默认 resource_id=volc.seedasr.auc）
SKILL_DIR="/Volumes/成峰/代码/剪辑Agent/.claude/skills/剪口播"
"$SKILL_DIR/scripts/volcengine_transcribe.sh" audio.mp3
# 输出: volcengine_result.json

转录脚本默认使用火山 v3 Seed ASR 2.0：

POST /api/v3/auc/bigmodel/submit
POST /api/v3/auc/bigmodel/query
X-Api-Resource-Id: volc.seedasr.auc

如果要临时回退或对比其他资源，可用环境变量覆盖：

VOLCENGINE_ASR_RESOURCE_ID=volc.bigasr.auc "$SKILL_DIR/scripts/volcengine_transcribe.sh" audio.mp3

步骤 4: 生成字级时间轴

node "$SKILL_DIR/scripts/generate_subtitles.js" volcengine_result.json
# 输出: subtitles_words.json

cd ..

时间戳规则：

generate_subtitles.js 必须兼容 result.utterances（v3）和旧版 utterances。
v3 结果里的无效时间戳 token（常见为空格，start_time/end_time = -1）必须过滤，否则会制造假静音。
2026-06-25 的 180 秒样本评估：Seed ASR 2.0 相对校对字幕时间窗的词级边界偏移中位数约 80ms，P90 约 240ms，最大约 620ms；可作为默认剪口播时间轴，但长句仍需按词级时间戳重新切成字幕行。

步骤 5: 分析口误（脚本+AI）

5.1 生成易读格式

cd 2_分析

node -e "
const data = require('../1_转录/subtitles_words.json');
let output = [];
data.forEach((w, i) => {
  if (w.isGap) {
    const dur = (w.end - w.start).toFixed(2);
    if (dur >= 0.2) output.push(i + '|[静' + dur + 's]|' + w.start.toFixed(2) + '-' + w.end.toFixed(2));
  } else {
    output.push(i + '|' + w.text + '|' + w.start.toFixed(2) + '-' + w.end.toFixed(2));
  }
});
require('fs').writeFileSync('readable.txt', output.join('\\n'));
"

5.2 读取用户习惯

先读 用户习惯/ 目录下所有规则文件。

5.3 生成句子列表（关键步骤）

必须先分句，再分析。按静音切分成句子列表：

node -e "
const data = require('../1_转录/subtitles_words.json');
let sentences = [];
let curr = { text: '', startIdx: -1, endIdx: -1 };

data.forEach((w, i) => {
  const isLongGap = w.isGap && (w.end - w.start) >= 0.5;
  if (isLongGap) {
    if (curr.text.length > 0) sentences.push({...curr});
    curr = { text: '', startIdx: -1, endIdx: -1 };
  } else if (!w.isGap) {
    if (curr.startIdx === -1) curr.startIdx = i;
    curr.text += w.text;
    curr.endIdx = i;
  }
});
if (curr.text.length > 0) sentences.push(curr);

sentences.forEach((s, i) => {
  console.log(i + '|' + s.startIdx + '-' + s.endIdx + '|' + s.text);
});
" > sentences.txt

5.4 脚本自动标记静音（必须先执行）

node -e "
const words = require('../1_转录/subtitles_words.json');
const selected = [];
words.forEach((w, i) => {
  if (w.isGap && (w.end - w.start) >= 0.2) selected.push(i);
});
require('fs').writeFileSync('auto_selected.json', JSON.stringify(selected, null, 2));
console.log('≥0.2s静音数量:', selected.length);
"

→ 输出 auto_selected.json（只含静音 idx）

5.4b 头尾裁剪（转录盲区，必做）

🚨 火山只转语音，结尾的未转录杂音/收尾动作不在字幕里，所有检测器都看不见。必须比对视频时长补出来。见 用户习惯/3-静音段处理.md。

VDUR=$(ffprobe -v error -show_entries format=duration -of csv=p=0 "file:$VIDEO_PATH")
node -e "
const fs=require('fs');
const w=require('../1_转录/subtitles_words.json');
const auto=require('./auto_selected.json');
const VDUR=$VDUR, last=w[w.length-1];
if(VDUR - last.end > 0.3){            // 结尾未覆盖 → 补尾元素并预选
  w.push({text:'',start:last.end,end:VDUR,isGap:true,reason:'结尾未转录(杂音/收尾)'});
  auto.push(w.length-1);
  fs.writeFileSync('../1_转录/subtitles_words.json',JSON.stringify(w));
  fs.writeFileSync('./auto_selected.json',JSON.stringify([...new Set(auto)].sort((a,b)=>a-b),null,1));
  console.log('补尾元素 idx',w.length-1,'['+last.end+'→'+VDUR+']');
} else console.log('尾部已覆盖');
if(w[0].start>0.3) console.log('⚠️ 开头',w[0].start,'s 未覆盖，考虑补头元素');
"

5.5 AI 分析口误（追加到 auto_selected.json）

🚨 核心原则：删前保后。所有重复/口误，删前面的，保后面的。

按检测类型分工，多 Agent 并行执行：

每个 Agent 只负责一种检测，prompt 更短更精确，避免规则互相干扰。

Agent	输入	检测内容	删除范围
A-句间重复	sentences.txt	相邻/隔一句开头≥5字相同	删前句整句
B-句内重复	sentences.txt	同一句内 A+中间+A 模式	只删前面片段，不删整句
C-残句	sentences.txt	话说一半+静音+后面重说	删残句整句

脚本可直接处理（不需要 AI）：

卡顿词（那个那个、就是就是）→ 正则匹配
语气词（嗯、啊、呃）→ 标记待人工确认

复核（verify）—— 按风险投放，不要铺满（见 用户习惯/10-删除风险分层.md）：

低风险免验：静音、逐字子集重复（删的是保留内容的逐字开头，如删「我把同一句」保「我把同一句话」）、≤3字纯卡壳/语气词 → 直接进 auto_selected，交网页人工兜底。
高风险必验：整句删除、"开头撞结尾岔"（如「超出了预算」vs「超出了路线规划能力」）、长片段重复 → 派对抗 reviewer 复核，确认没把独有内容删掉。
教训：曾对全部候选铺开 2× 复核，命中仅 ~4/91 且全落在高风险类（见 log/）。verify 很贵，砸在容易删错的地方就好。

Agent prompt 模板：

给每个 Agent 的 prompt 包含：
1. 只放该 Agent 对应的一条检测规则（从用户习惯/读取）
2. 完整的 sentences.txt 内容
3. 明确要求：返回要删除的 idx 范围列表
4. 🚨 强调"删前保后"：删前面的版本，保留后面更完整的版本

Agent 返回格式：

| 句号 | idx范围 | 类型 | 内容摘要 | 处理 |
|------|---------|------|----------|------|

删除idx列表: [所有要删除的idx]

合并结果：

收集所有 Agent 返回的 idx 列表 → 合并到 auto_selected.json → 去重排序

范围整段删除规则：标记口误时，从 startIdx 到 endIdx 之间的所有元素（含中间的 gap）全部加入 auto_selected。不要逐个挑选文字 idx 而跳过 gap。

🚨 关键警告：行号 ≠ idx

readable.txt 格式: idx|内容|时间
                   ↑ 用这个值

行号1500 → "1568|[静1.02s]|..."  ← idx是1568，不是1500！

口误分析.md 格式：

## 句间重复 (Agent A)

| 句号 | idx范围 | 内容摘要 | 处理 |
|------|---------|----------|------|
| 5 | 212-233 | 与句6重复，句6更完整 | 删前句 |

## 句内重复 (Agent B)

| 句号 | idx范围 | 内容摘要 | 处理 |
|------|---------|----------|------|
| 16 | 492-510 | "很多人一提到CLI命令"前半重复 | 删片段 |

## 残句 (Agent C)

| 句号 | idx范围 | 内容摘要 | 处理 |
|------|---------|----------|------|
| 7 | 266-275 | "为了解释为了回答这个"未完成 | 删整句 |

5.6 口播稿对齐补漏（有口播稿时）

若用户提供口播稿/原文稿（口播是照稿读的），用它做句子级对齐补漏 —— 见 用户习惯/11-口播稿对齐.md。

口播稿 = 语义 ground truth（措辞会变，如"两个模型"↔"两个大模型"，别逐字 diff）。把每个转录句对齐到口播稿句，抓纯文本检测漏掉的整句级口误：整句重说、残句、无对应口误 —— 尤其技术名词卡壳（Mindverse / δ-mem / LoRA 念错重来、"Delta Mam"、孤立单字"从"）。这类整句删除属高风险 → 走复核，确认保留版覆盖了口播稿原意。

步骤 6-7: 审核

cd ../3_审核

# 6. 生成审核网页（传入视频文件，自动创建符号链接）
node "$SKILL_DIR/scripts/generate_review.js" ../1_转录/subtitles_words.json ../2_分析/auto_selected.json "$VIDEO_PATH"
# 输出: review.html, video.mp4(符号链接)

# 7. 启动审核服务器
node "$SKILL_DIR/scripts/review_server.js" 8899 "$VIDEO_PATH"
# 打开 http://localhost:8899

⚠️ 必须用 review_server.js，不能用 python3 -m http.server 替代。原因：视频播放依赖 HTTP Range 请求（206），python 简易服务器不支持，会导致视频无法播放/无声音。普通用户保持这个终端窗口开着即可；Claude Code / Codex 由 Agent 使用当前环境可用的后台任务能力托管。不要要求用户安装 tmux。

用户在网页中：

播放视频画面确认
勾选/取消删除项
左键按住滑过文字可批量标删/恢复，支持跨行滑动；拖过头时不松手往回拉，选区会实时收窄
点击「执行剪辑」

步骤 8: 监听剪后视频

用户点击审核页按钮后，review_server.js 只会剪出新视频，并在审核目录写入 cut_done.json。Agent 必须监听这个文件，确认剪后视频真实生成且大小稳定。

# 在另一个 Agent 任务/终端里等待用户确认后的剪辑结果
node "$SKILL_DIR/scripts/watch_cut_done.js" "output/YYYY-MM-DD_视频名/剪口播/3_审核"

监听结果会返回剪后视频路径：

{
  "output": "output/YYYY-MM-DD_视频名/剪口播/3_审核/视频名_cut.mp4",
  "newDuration": "123.45",
  "outputSize": 12345678
}

把返回里的 output 记为 CUT_VIDEO，后续字幕必须基于这个视频重转写。

不要用 curl /api/cut 或脚本模拟点击，除非用户明确授权。

步骤 9: 基于剪后视频重新转写

"$SKILL_DIR/scripts/generate_srt_for_video.sh" "剪后视频.mp4" "output/YYYY-MM-DD_视频名/字幕"

字幕必须基于剪后视频重新转写，不能用原始视频字幕反推。

这一步只产出火山转写初稿：

output/YYYY-MM-DD_视频名/字幕/1_转录/volcengine_result.json
output/YYYY-MM-DD_视频名/字幕/subtitles_with_time.json
output/YYYY-MM-DD_视频名/字幕/3_输出/video.raw.srt

步骤 10: AI 校对字幕

Agent 必须读取 video.raw.srt 或 subtitles_with_time.json，对照用户给的口播稿/正文/专名上下文做 AI 校对。

校对规则：

保留原时间轴，优先只改字幕文字。
修正技术名词、工具名、人名、英文词、同音误识别。
调整明显不合理的断句。
不新增视频里没有说出的内容。
校对完成后写入 字幕/3_输出/video.srt。

步骤 11: 整理基础素材包

AI 校对完成后，再把剪后视频和最终字幕整理到项目根目录：

OUTPUT_ROOT="output/YYYY-MM-DD_视频名"
ln -sf "$CUT_VIDEO" "$OUTPUT_ROOT/source_cut.mp4"
ln -sf "$OUTPUT_ROOT/字幕/3_输出/video.srt" "$OUTPUT_ROOT/subtitles.srt"

最终产物至少包括：

output/YYYY-MM-DD_视频名/字幕/3_输出/video.srt
output/YYYY-MM-DD_视频名/source_cut.mp4
output/YYYY-MM-DD_视频名/subtitles.srt

如果用户下一步要跑 口播成片，把产物整理成这个心智：

source_cut.mp4  = 剪后口播视频
subtitles.srt   = AI 校对后的剪后字幕 SRT
assets/         = 可选素材

source_cut.mp4 和 subtitles.srt 可以是符号链接；给用户汇报时，要明确指出它们已经指向剪后视频和剪后字幕。

审核硬规则

到审核页这一步必须停下来，把链接交给用户。

用户确认前，Agent 只能做三件事：

生成 review.html
启动 review_server.js
告诉用户审核页 URL

除非用户明确说“你帮我点执行”或“直接剪”，否则 Agent 不允许：

直接调用 /api/cut
用 curl / 脚本模拟点击
跳过审核页直接生成剪后视频

用户在审核页点击「执行剪辑」后，服务器只生成剪后视频。Agent 监听到剪后视频完成后，才能进入剪后重转写和 AI 校对字幕。

数据格式

subtitles_words.json

[
  {"text": "大", "start": 0.12, "end": 0.2, "isGap": false},
  {"text": "", "start": 6.78, "end": 7.48, "isGap": true}
]

auto_selected.json

[72, 85, 120]  // Claude 分析生成的预选索引

剪辑编码（硬性规则）

⚠️ 匹配原片参数重编码，帧级精确切割。

cut_video.sh 的工作方式：

自动检测原片编码参数（codec/profile/pix_fmt/bitrate）
用 filter_complex trim+concat 帧级精确切割
以相同参数重编码：-profile:v high -b:v {原片码率} -pix_fmt yuv420p

关键：重编码画质取决于是否匹配原片参数，不是 CRF 值。

✅ -b:v {原片码率} -profile:v high -pix_fmt yuv420p → 肉眼无区别
❌ 只指定 -crf N 不指定 profile/pix_fmt → 可能有偏差

配置

火山引擎 API Key

如果用户没有火山语音识别 API Key，先让用户打开火山控制台开通“录音文件识别 2.0”并创建 API Key：

https://console.volcengine.com/speech/new/setting/activate?projectName=default

当前默认模型资源：

录音文件识别 2.0 / Seed ASR
resource_id = volc.seedasr.auc

拿到 Key 后，再写入 Skills 根目录的 .env：

cd /Volumes/成峰/代码/剪辑Agent/.claude/skills
cp .env.example .env
# 编辑 .env 填入 VOLCENGINE_API_KEY=xxx

如果是通过 npm 安装，Skills 根目录通常是：

~/.claude/skills/chengfeng-videocut-skills
~/.codex/skills/chengfeng-videocut-skills

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

aiskillstore / marketplace

skills/ceeon/videocut/SKILL.md

View on GitHub ↗

Imported 8/10/2026Version v1Bundled 8 files · scripts

Illustrative Want to see what aiskillstore/videocut 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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剪口播 v3

快速使用

输出目录结构

流程

执行步骤

步骤 0: 创建输出目录

步骤 1-3: 转录

步骤 4: 生成字级时间轴

步骤 5: 分析口误（脚本+AI）

5.1 生成易读格式

5.2 读取用户习惯

5.3 生成句子列表（关键步骤）

5.4 脚本自动标记静音（必须先执行）

5.4b 头尾裁剪（转录盲区，必做）

5.5 AI 分析口误（追加到 auto_selected.json）

5.6 口播稿对齐补漏（有口播稿时）

步骤 6-7: 审核

步骤 8: 监听剪后视频

步骤 9: 基于剪后视频重新转写

步骤 10: AI 校对字幕

步骤 11: 整理基础素材包

审核硬规则

数据格式

subtitles_words.json

auto_selected.json

剪辑编码（硬性规则）

配置

火山引擎 API Key

SkillSafety

Source

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