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在 OpenClaw 中安装
/install shorts-builder-cn
功能描述
剧情生成管道流技能。支持多剧集连续生成、图谱管理、AI质检+人工确认的双控机制。自动管理人物、场景、钩子的关联关系。
安全使用建议
Before installing or running this skill, note three mismatches that need clarification: (1) SKILL.md advertises a remote graph API (https://framedream.art/...) and says graph storage uses that endpoint, but the included GraphManager saves/loads local JSON files and contains no HTTP calls — ask the author which is authoritative. (2) The documentation says AI-review retries are limited to 3, but pipeline.py contains comments and messages indicating unlimited retries; decide which policy you want and update code/docs. (3) AIReviewer.review_episode is a stub; the design expects an LLM to produce the review JSON. If you plan to run the pipeline, verify how reviews are produced (local logic vs external LLM) and whether any prompts will send full episode content to external services. If you are uncomfortable with potential network calls to framedream.art or with indefinite automatic retries, run the skill in an isolated environment and/or request the author to: remove or document any remote endpoint usage, implement/clarify retry limits, and explicitly state where review LLM invocations occur and what data is sent. If you accept the local-only behavior, inspect and back up the data/ directory (it will contain persistent story data) and run tests with dummy pipelines first.
功能分析
Type: OpenClaw Skill
Name: shorts-builder-cn
Version: 1.0.0
The skill bundle contains a path traversal vulnerability in `scripts/graph_manager.py`, where the `pipeline_id` parameter is used to construct file paths without sanitization, potentially allowing an attacker to read or write JSON files outside the intended directory. Additionally, there is a discrepancy between the documentation in `SKILL.md`, which directs the agent to use an external webhook (https://framedream.art/n8n/webhook-test/open_frame_construct) for data storage, and the actual Python implementation which uses local storage; this could lead to unintended data exfiltration if the agent follows the markdown instructions.
能力评估
Purpose & Capability
The skill's stated purpose (pipeline + graph management + AI review) matches the included code shape, but SKILL.md repeatedly describes a remote '图谱接口' (https://framedream.art/...) and says graph storage is via a network endpoint. The actual GraphManager implementation in scripts/graph_manager.py uses local JSON files under data/graphs and contains no network calls. This mismatch (remote API referenced in docs but not used in code) is unexplained and could lead to surprising behavior if a user or integrator follows the docs instead of the code.
Instruction Scope
SKILL.md instructs the agent to call graph endpoints and states '图谱存储：通过远程接口存储，需要网络连接', yet the code stores graphs locally. The SKILL.md also documents '最多3次' retry on AI review, while pipeline.py's comments and process_ai_review logic indicate retries may be unlimited. Additionally, ai_reviewer.AIReviewer.review_episode is unimplemented (pass) and the module's design expects an external LLM to produce JSON review outputs. These inconsistencies give the agent ambiguous discretion about network use and retry behavior.
Install Mechanism
No install spec and no external dependencies or download URLs are declared; the skill is instruction/code-only and writes files under its workspace. That lowers installer risk — nothing in the package pulls arbitrary code from the network during install.
Credentials
The skill declares no required environment variables, credentials, or config paths. The code also does not reference environment secrets or external auth. This is proportionate to its stated functionality.
Persistence & Privilege
The skill persists state to data/pipeline_state.json and stores graphs under data/graphs (read/write). It is not always:true and does not alter other skills or system-wide settings. Still, persistent local storage of generated content means user data (generated episodes) will remain on disk; if the docs' remote endpoint were used by an operator, that could add network persistence/exfiltration risk.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install shorts-builder-cn - 安装完成后，直接呼叫该 Skill 的名称或使用
/shorts-builder-cn触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
story-pipeline 1.0.0 首次发布，提供多剧集自动剧情生成与双重审核机制。
- 支持基于主题和目标集数的多剧集连续生成。
- 引入AI质检+人工确认的双重审核流程，确保内容质量。
- 内置图谱功能，实现人物、场景、钩子的自动关联与管理。
- 支持状态持久化，可处理暂停、恢复与多管道并行。
- 提供重试机制与丰富的API接口，便于灵活集成和自定义。
元数据
常见问题
连续短剧剧情构建 是什么？
剧情生成管道流技能。支持多剧集连续生成、图谱管理、AI质检+人工确认的双控机制。自动管理人物、场景、钩子的关联关系。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 362 次。
如何安装 连续短剧剧情构建？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install shorts-builder-cn」即可一键安装，无需额外配置。
连续短剧剧情构建 是免费的吗？
是的，连续短剧剧情构建 完全免费（开源免费），可自由下载、安装和使用。
连续短剧剧情构建 支持哪些平台？
连续短剧剧情构建 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 连续短剧剧情构建？
由 hexidyg（@hexidyg）开发并维护，当前版本 v1.0.0。
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