Token导航 LogoToken导航TokenDH.com
研究检索执行命令clawhub未标认证来源可访问clear审计提醒

openstoryline-install打开故事线安装

Agent Skill

openstoryline-install 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

21,096

周安装

879

GitHub Stars

公开资料未说明

下载量

7,032
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:openstoryline-install(打开故事线安装)
来源仓库:https://github.com/anlittledy/openstoryline-install
安装命令:
openclaw skills install openstoryline-install
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install openstoryline-install

简介

从源码在本地安装、配置并启动 FireRed-OpenStoryline 项目。

  • 解决常见安装故障与环境依赖冲突问题。
  • 适用于希望在私有环境中运行定制化故事生成系统的用户。
  • 需具备基础 Linux/macOS 操作能力以完成编译部署步骤。
  • 安装命令:openclaw skills install openstoryline-install

SKILL.md

name
openstoryline-install
description
Install, configure, and start FireRed-OpenStoryline from source on a local machine. Use when a user asks to set up OpenStoryline, troubleshoot installation, download required resources, fill config.toml API keys, or launch the MCP and web services, as well as Chinese requests like “安装 OpenStoryline”, “配置 OpenStoryline”, “启动 OpenStoryline”, “把 OpenStoryline 跑起来”, “修复 OpenStoryline 安装问题”, or “排查 OpenStoryline 启动失败”.

OpenStoryline Install

Use this skill when the task is to install or repair a local source checkout of FireRed-OpenStoryline.

Keep the workflow deterministic:

  1. Confirm the repo path and read the current README.md and config.toml.
  2. Detect local prerequisites before changing anything.
  3. Prefer a local venv install unless the user explicitly asks for Docker or conda.
  4. Download resources only after Python dependencies succeed.
  5. Validate imports and config loading before claiming success.
  6. This skill assumes macOS, Linux, or WSL with a POSIX shell.

What This Skill Covers

  • Clone the GitHub repo if needed
  • Create a Python environment
  • Install Python dependencies
  • Download .storyline models and resource/ assets
  • Fill config.toml model settings
  • Start MCP and web servers
  • Explain common installation/documentation gaps

Preconditions

Check these first:

  • git
  • Python >= 3.11
  • ffmpeg
  • wget
  • unzip

Optional:

  • docker
  • conda

If ffmpeg, wget, or unzip are missing, install them through the OS package manager before continuing.

Examples:

  • macOS with Homebrew:
  brew install ffmpeg wget unzip
  • Debian/Ubuntu:
  sudo apt-get update
  sudo apt-get install -y ffmpeg wget unzip

If no supported package manager or permission is available, stop and report the missing system dependency clearly.

Interpreter selection

First prefer any interpreter that already exists and passes version checks:

  1. A system Python >= 3.11
  2. An already available conda Python >= 3.11
  3. An already available pyenv Python >= 3.11, but only if basic stdlib modules work

Validate candidate interpreters before using them:

/path/to/python -c "import ssl, sqlite3, venv; print('stdlib_ok')"

If no supported interpreter already exists, peferr conda fallback:

conda create -y -n openstoryline-py311 python=3.11
conda run -n openstoryline-py311 python --version
conda run -n openstoryline-py311 python -m venv .venv

After a supported interpreter is found, always create a repo-local .venv and continue using .venv/bin/python for install, config validation, and service startup.

Do not duplicate the rest of the workflow for pyenv or conda unless the user explicitly asks to stay inside a conda environment.

Clone repository

If you don't have a local repository yet, clone the repository first.

git clone https://github.com/FireRedTeam/FireRed-OpenStoryline.git
cd FireRed-OpenStoryline

Preferred Install Path

From the repo root:

/path/to/python -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -r requirements.txt
bash download.sh

Notes:

  • download.sh pulls both model weights and a large resource archive. It can take a long time and may resume after network drops.
  • The resource download is required for a full local run, not just the Python package install.

Configuration

Before starting the app, update config.toml.

You can use scripts/update_config.py.

At minimum, fill:

.venv/bin/python scripts/update_config.py --config ./config.toml --set llm.model=REPLACE_WITH_REAL_MODEL
.venv/bin/python scripts/update_config.py --config ./config.toml --set llm.base_url=REPLACE_WITH_REAL_URL
.venv/bin/python scripts/update_config.py --config ./config.toml --set llm.api_key=sk-REPLACE_WITH_REAL_KEY

.venv/bin/python scripts/update_config.py --config ./config.toml --set vlm.model=REPLACE_WITH_REAL_MODEL
.venv/bin/python scripts/update_config.py --config ./config.toml --set vlm.base_url=REPLACE_WITH_REAL_URL
.venv/bin/python scripts/update_config.py --config ./config.toml --set vlm.api_key=sk-REPLACE_WITH_REAL_KEY

Optional but common:

  • search_media.pexels_api_key for searching media
  • TTS provider keys under generate_voiceover.providers.* (choose one provider)

Verification

Run these checks before saying installation is complete:

.venv/bin/pip check
PYTHONPATH=src .venv/bin/python -c "from open_storyline.config import load_settings; load_settings('config.toml'); print('config_ok')"

Also confirm key resources exist:

test -f .storyline/models/transnetv2-pytorch-weights.pth
test -d resource/bgms

Start Services

There are two common paths. These are long-running processes. Do not wait for them to exit normally. Treat successful startup log lines or confirmed listening ports as success, and keep the services running in separate shells/sessions as needed.

Manual start:

PYTHONPATH=src .venv/bin/python -m open_storyline.mcp.server

In a second shell:

PYTHONPATH=src .venv/bin/python -m uvicorn agent_fastapi:app --host 127.0.0.1 --port 8005

Expected Outputs

After a successful install:

  • .venv/ exists
  • MCP listens on the configured local port (commonly 127.0.0.1:8001)
  • Web listens on the configured web port (commonly 127.0.0.1:8005, though run.sh defaults may differ)

Common Problems

download.sh is slow or interrupted

Symptom:

  • Large downloads stall or reconnect

Fix:

  • Let wget continue; it supports resume behavior here
  • Verify extracted outputs instead of trusting the progress meter

Web/MCP server fails to bind

Symptom:

  • operation not permitted while binding 127.0.0.1 or 0.0.0.0

Fix:

  • In agent sandboxes, request permission to open local listening ports
  • Prefer 127.0.0.1 over 0.0.0.0 unless external access is required

Response Pattern

When reporting status to the user, separate:

  • what is installed
  • what is still downloading
  • what config is still missing
  • what address the service is listening on

Do not say "installation complete" if only the Python packages are installed but the resource bundle is still missing.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

88.55%
按下载量换算6,227

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install openstoryline-install 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills