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Live Replay Analyzer

作者 an · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install live-replay-analyzer
功能描述
(已验证) 根据客户和场次,自动生成详细的《直播复盘与成长规划报告》。
使用说明 (SKILL.md)

SKILL.md for live-replay-analyzer

Description

这是一个专业的直播复盘工具,它根据指定的客户名称和直播场次,自动读取相关的直播数据、用户画像和直播话术,生成一份详尽的《直播复盘与成长规划报告》。

该技能采用"AI 代理作为总调度 (Agent as Orchestrator)"的模式,由 AI 代理负责数据验证、脚本执行和最终报告交付。

Configuration

1. API 配置 (必需)

本技能需要配置一个用于生成报告的 API 密钥。请在 ~/.openclaw/config.json 中添加以下配置:

{
  "review_api_key": "YOUR_API_KEY",
  "review_api_url": "https://api2.aigcbest.top/v1/chat/completions"
}

review_api_url 是可选的,默认值为 https://api2.aigcbest.top/v1/chat/completions

2. 数据目录结构

本技能期望数据文件按照以下结构存放在 input/ 目录中:

input/
└── {客户名称}/
    └── {场次名称}/
        ├── data.txt          # 直播数据 (必需)
        ├── profile.txt       # 用户画像 (必需,或由 AI 从 profile.png 生成)
        └── script.txt        # 直播话术 (必需)

How to Use

Parameters

  • --client (必填): 客户名称,对应 input/{client}/ 目录。
  • --session (必填): 直播场次名称,对应 input/{client}/{session}/ 目录。
  • --call-model (可选): 添加此参数后,脚本会直接调用模型生成报告并保存到 output/ 目录;否则只输出提示词。

Example Invocation

模式 A:仅生成提示词 (由 AI 代理进行分析和交付)

# AI 应动态查找 python 路径
python path/to/analyzer.py --client "客户 A" --session "2026-03-26"

模式 B:直接生成报告文件

# AI 应动态查找 python 路径
python path/to/analyzer.py --client "客户 A" --session "2026-03-26" --call-model

Output

报告文件将保存在 output/{客户名称}/{场次名称}/ 目录下,文件名格式为:{客户名称}-{场次名称}_report_{时间戳}.md

安全使用建议
Before installing/using this skill: 1) Be aware the script will read ~/.openclaw/config.json for a 'review_api_key' and will send your input files (data/profile/script) to the configured review_api_url — the default endpoint is api2.aigcbest.top (not an obvious mainstream provider). If you don't trust that endpoint, do not put secrets there. 2) Prefer creating a dedicated API key for this skill (not reuse high‑privilege keys) and limit its scope if possible. 3) Verify the skill source (the SKILL.md lists a placeholder GitHub URL). 4) If you must test, run it in an isolated environment or container and monitor outbound network calls. 5) Ask the author to: (a) declare the config requirement in registry metadata, (b) explain why the default endpoint was chosen and provide instructions for using a trusted LLM provider, and (c) fix the documentation mismatch (profile.png vs profile.txt).
能力评估
Purpose & Capability
Functionality matches the stated purpose: analyzer.py reads input files, assembles a prompt and (optionally) calls a model to generate a report. Requiring an API key for a model call is reasonable. However, the registry metadata declared no required env vars/config, while SKILL.md and analyzer.py require a 'review_api_key' stored in ~/.openclaw/config.json — this is an inconsistency that should be explained by the author.
Instruction Scope
SKILL.md instructs the user to add an API key and (optionally) override the API URL in ~/.openclaw/config.json; analyzer.py reads exactly that file. The script sends the assembled prompt and user-provided input files (data.txt/profile.txt/script.txt) to the configured remote endpoint. That network transmission of potentially sensitive client data to an external third party is within the skill's runtime scope but may be unexpected to users. Additional mismatch: SKILL.md mentions profile.png as an alternative source, but analyzer.py only reads profile.txt (no image handling).
Install Mechanism
There is no external install/download; the skill is instruction + local Python script. It requires aiohttp/requests at runtime (SKILL.md lists them). No archive downloads or remote installers are used, so install risk is low.
Credentials
The skill requests a single API credential (review_api_key) and an optional review_api_url — which is proportionate to making model calls. But the credential is stored in a shared path (~/.openclaw/config.json) that may hold other secrets used by other skills; the registry metadata did not declare this config requirement or any required env vars, creating a transparency gap. Also the default review_api_url (https://api2.aigcbest.top) is a third‑party endpoint that may not be the provider the user expects.
Persistence & Privilege
The skill does not request 'always: true', does not modify other skills, and does not claim to persist or elevate privileges. Autonomous invocation is allowed by platform default but not in itself an additional red flag here.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install live-replay-analyzer
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /live-replay-analyzer 触发
  4. 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
- Initial release of live-replay-analyzer (v1.0.0). - Automatically generates a detailed live streaming review and growth plan report based on client and session data. - Supports integration with AI orchestration for data validation, script execution, and report delivery. - Requires configuration of an API key and structured input data directory. - Offers two usage modes: outputting analysis prompts or directly generating report files. - Output reports are saved in a timestamped markdown format within the output directory.
元数据
Slug live-replay-analyzer
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Live Replay Analyzer 是什么?

(已验证) 根据客户和场次,自动生成详细的《直播复盘与成长规划报告》。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 95 次。

如何安装 Live Replay Analyzer?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install live-replay-analyzer」即可一键安装,无需额外配置。

Live Replay Analyzer 是免费的吗?

是的,Live Replay Analyzer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Live Replay Analyzer 支持哪些平台?

Live Replay Analyzer 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 Live Replay Analyzer?

由 an(@ahsbnb)开发并维护,当前版本 v1.0.0。

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