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在 OpenClaw 中安装
/install screen-recommendation-loop
功能描述
Build and run a low-friction movie/anime recommendation + follow-up loop. Use when a user wants long-term taste profiling from watched/unfinished/dropped fee...
安全使用建议
This instruction-only skill is coherent and lightweight, but before installing consider: where will you store the JSON/SQLite state (ensure appropriate access controls/backups)? If you or an implementer add fetching from Douban/Bangumi or other external sources, review what network access and API credentials are needed and whether scraping is allowed. Also confirm the agent/platform's scheduling and message-sending behavior so follow-ups behave as you expect and no private identifiers (chat IDs, account tokens) are recorded by the skill.
功能分析
Type: OpenClaw Skill
Name: screen-recommendation-loop
Version: 0.0.1
The skill bundle defines a logic-based recommendation engine for movies and anime using public lists like Douban and Bangumi. The instructions in SKILL.md are focused entirely on the recommendation workflow, scheduling follow-ups, and updating user preferences, with no evidence of malicious intent, data exfiltration, or unauthorized command execution. Furthermore, the documentation includes explicit safety guidelines advising the agent not to store private identifiers or sensitive data.
能力评估
Purpose & Capability
Name and description match the SKILL.md: the document describes a low-friction recommendation loop, candidate pools, follow-up timing, a small record schema, and update heuristics. It does not request unrelated credentials, binaries, or install actions.
Instruction Scope
Instructions remain focused on recommendation/polling and local state (JSON/SQLite). They assume the agent/platform can send messages and schedule follow-ups; the SKILL.md does not prescribe network access or APIs, but it references external candidate pools (Douban/Bangumi) without specifying how to fetch them. If an implementation adds scraping/API calls, that expands scope and may require credentials or further privacy review.
Install Mechanism
There is no install spec or code; this instruction-only skill writes nothing to disk by itself. That minimizes install-time risk.
Credentials
The skill declares no environment variables, credentials, or config paths. Its data storage suggestion (JSON or SQLite) is proportionate to the stated purpose. Be aware that a concrete implementation that pulls from external services may later require API keys.
Persistence & Privilege
always is false and autonomous invocation is not disabled (the platform default). The skill does not request permanent platform-level presence or access to other skills' configs.
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 screen-recommendation-loop - 安装完成后，直接呼叫该 Skill 的名称或使用
/screen-recommendation-loop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
v0.0.1
- Initial release of screen-recommendation-loop skill.
- Supports one-at-a-time movie/anime recommendations with feedback loops.
- Integrates mixed-source candidate pools (e.g., Douban/Bangumi Top lists).
- Adapts future picks using preference signals and constrained randomness.
- Automatic, type-based follow-up timing and minimal feedback schema.
- Emphasizes user privacy and does not store personal identifiers.
元数据
常见问题
Screen recommendation loop 是什么？
Build and run a low-friction movie/anime recommendation + follow-up loop. Use when a user wants long-term taste profiling from watched/unfinished/dropped fee... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 242 次。
如何安装 Screen recommendation loop？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install screen-recommendation-loop」即可一键安装，无需额外配置。
Screen recommendation loop 是免费的吗？
是的，Screen recommendation loop 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Screen recommendation loop 支持哪些平台？
Screen recommendation loop 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 Screen recommendation loop？
由 GloryXia（@gloryxia）开发并维护，当前版本 v0.0.1。
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