Token导航 LogoToken导航

Agent Skill · 研究检索

liveavatar-feedback(实时头像反馈)

liveavatar-feedback 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

liveavatar-feedback 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 34,853。

总安装 2,175周安装 88GitHub Stars 1下载 683核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:liveavatar-feedback(实时头像反馈)
来源仓库:https://github.com/heygen-com/liveavatar-agent-skills
仓库路径:skills/liveavatar-feedback
安装命令:
npx skills add https://github.com/heygen-com/liveavatar-agent-skills --skill liveavatar-feedback
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/heygen-com/liveavatar-agent-skills --skill liveavatar-feedback

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

liveavatar-feedback 用于查找、检索和筛选相关信息。适合根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从 GitHub 仓库安装使用。安装前应确认权限范围、维护状态及是否涉及联网或文件操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/liveavatar-feedback

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/heygen-com/liveavatar-agent-skills --skill liv…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

liveavatar-feedback 怎么安装?

在终端执行 npx skills add https://github.com/heygen-com/liveavatar-agent-skills --skill liveavatar-fe…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

liveavatar-feedback 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

liveavatar-feedback 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

liveavatar-feedback 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

liveavatar-feedback 用的人多吗?

公开数据是累计安装 2,175 次,近一周安装 88 次,下载 683 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

liveavatar-feedback 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
liveavatar-feedback

实时头像反馈 · 本页

2,1751接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

LiveAvatar Feedback Collection

This skill collects integration feedback and sends it to the LiveAvatar team. It fires in three scenarios:

  1. Post-implementation — the user just finished an integration (Embed, FULL, or LITE)
  2. Frustration detected — repeated errors, explicit complaints, or visible friction
  3. Explicit request — the user asks to give feedback or report an issue

When to Trigger

Activate this skill when ANY of these are true:

  • The user says they're done or the integration is working
  • The user expresses frustration ("this is broken", "why doesn't this work", "I'm stuck")
  • The user explicitly asks to give feedback, report a bug, or share a suggestion
  • You've seen 3+ failed attempts at the same step during integration
  • The user says they're giving up or abandoning the integration

Do NOT trigger unprompted in the middle of active implementation. Wait for a natural pause or completion point.

Step 1: Gather Context from the Conversation

Before prompting the user, review the conversation history and silently compile:

  • Use case — what the user is building (e.g., "customer support avatar for a SaaS dashboard"). This comes from the discovery phase, the user's stated goal, or their codebase.
  • Blockers — anything that caused friction during the integration. Look for: errors hit, steps that required retries, confusing API behavior, silent failures, missing docs, or anything the user explicitly complained about.

Compact both into short, factual summaries. These are telemetry the agent drafts — the user will review them before anything is sent.

Step 2: Ask the User for Permission and Additional Feedback

You must get explicit permission before sending anything. Present what you've gathered and ask the user to approve, edit, or decline.

Adapt tone to context — celebratory after success, empathetic if frustrated.

After successful implementation:

Your LiveAvatar integration is working! The LiveAvatar team collects anonymous integration feedback to improve the developer experience. Would you be okay with me sending a short summary?

Here's what I'd send:
- **Use case:** <your compacted summary>
- **Blockers:** <your compacted summary, or "None">

Want to add anything else in your own words? (Feature requests, suggestions, general thoughts — totally optional.)

**Nothing is sent until you say yes.** You can also edit or remove anything above.

When frustration is detected:

It looks like this has been a rough integration. The LiveAvatar team actively collects developer feedback to fix exactly these kinds of pain points. Would you be okay with me sending them a summary of what happened?

Here's what I'd send:
- **Use case:** <your compacted summary>
- **Blockers:** <your compacted summary of what went wrong>

Want to add anything in your own words? (No pressure — but this goes directly to the team that can fix it.)

**Nothing is sent until you say yes.** You can edit or remove any of the above.

On explicit feedback request:

I can send feedback directly to the LiveAvatar team. Based on our conversation, here's what I've gathered:

- **Use case:** <your compacted summary>
- **Blockers:** <your compacted summary, or "None">

Want to add anything else in your own words?

**Nothing is sent until you give the go-ahead.** Feel free to edit any of the above.

Wait for the user to explicitly approve (e.g., "yes", "send it", "looks good", "go ahead"). If the user says no, declines, or ignores the prompt — do not send the request. Drop it.

Step 3: Map to Fields

FieldSourceWhat goes here
use_caseAgent-compacted from conversationShort summary of what the user is building (e.g., "Customer support avatar for SaaS dashboard")
blockersAgent-compacted from conversationWhat caused friction — errors, retries, confusion, silent failures (e.g., "Silent avatar due to missing context_id — no error thrown, took 30 min to diagnose")
additional_feedbackUser's own words, verbatimFreeform input the user wrote when prompted — feature requests, suggestions, general thoughts. Use the user's exact words. Do not rephrase or summarize.

All three fields are optional. Omit or pass null for anything that doesn't apply:

  • No frustration or issues during integration? Omit blockers.
  • User didn't write any additional feedback? Omit additional_feedback.
  • Couldn't determine use case from context? Omit use_case.

Step 4: Send the Feedback (only after explicit consent)

Only execute this step if the user explicitly approved in Step 2. If the user declined, edited, or didn't respond — stop here.

This call runs on the BACKEND using X-API-KEY.

curl -X POST https://api.liveavatar.com/v1/feedback/agents \
  -H "X-API-KEY: <YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "feedback_data": {
      "use_case": "Customer support avatar",
      "blockers": "Latency issues with long responses",
      "additional_feedback": "Would love multi-language support"
    }
  }'

Constructing the request

  • use_case and blockers: use the compacted summaries you showed the user in Step 2 (with any edits they requested).
  • additional_feedback: paste the user's freeform text verbatim. Do not rephrase.
  • Omit or pass null for any field that doesn't apply.
  • If the user edited your summaries in Step 2, use the edited versions.
  • If the user's API key is available in the environment (e.g., HEYGEN_API_KEY or LIVEAVATAR_API_KEY), use it. Otherwise, tell the user you need their API key to submit the feedback.

Example payloads

Agent-compacted telemetry only (user had no additional feedback):

{
  "feedback_data": {
    "use_case": "Customer support avatar for e-commerce site",
    "blockers": "Silent avatar due to missing context_id — no error thrown, took 30 min to diagnose"
  }
}

Full payload with user's own words:

{
  "feedback_data": {
    "use_case": "Real-time sales coach avatar for CRM",
    "blockers": "Custom LLM setup required 3 separate API calls (secret, config, session token) before first test",
    "additional_feedback": "The sandbox is great. Would be nice if sessions lasted longer than 1 min. Also would love multi-language support."
  }
}

Step 5: Confirm Submission

After sending, confirm to the user:

Feedback sent to the LiveAvatar team — thanks for sharing.

Keep it brief. If the request fails (e.g., invalid API key), tell the user and offer to show them the curl command so they can send it manually.

Gotchas

  1. Consent is mandatory. Never send the request without the user's explicit "yes." If in doubt, ask again. If the user ignores the prompt, do not send.
  2. Don't fabricate additional_feedback. That field is the user's own words only. use_case and blockers are agent-compacted, but additional_feedback must be verbatim user input or omitted entirely. Never generate, summarize, or pre-fill this field yourself — if the user didn't write anything, omit it from the payload.
  3. Show before you send. Always show the user the exact content you plan to send and let them edit or remove anything before you fire the request.
  4. Don't interrupt flow. If the user is mid-implementation, don't jump to feedback collection. Wait for a natural break.
  5. API key required. This endpoint uses X-API-KEY. If you don't have access to it, show the user the curl command instead of failing silently.
  6. All fields are optional. A valid request can have just one field populated and the rest omitted or null.

平台分布

Codex

34.57%
按下载量换算236

Claude

28.06%
按下载量换算192

Cursor

19.41%
按下载量换算133

Gemini CLI

9.52%
按下载量换算65

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills