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llc-phone公司电话

Agent Skill

llc-phone 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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

4,774

周安装

195

GitHub Stars

1

下载量

1,544
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:llc-phone(公司电话)
来源仓库:https://github.com/cygnostik/llc-phone
安装命令:
openclaw skills install llc-phone
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 llc-phone

简介

基于 OpenAI Realtime API 与 Twilio 的低延迟 AI 电话系统。

  • 支持呼入/呼出通话、IVR 导航与会客接待自动化流程。
  • 适用于客服、销售或远程协作场景的智能语音交互扩展。
  • 需对接电话号码资源与语音网关,涉及通信安全与合规要求。
  • 部署前应完成声学模型适配与话术逻辑测试,确保服务质量。

SKILL.md

name
llc-phone
description
>-
user-invocable
true
homepage
https://promethean-dynamic.com
metadata
{"openclaw":{"emoji":"📞","homepage":"https://promethean-dynamic.com","requires":{"env":["OPENAI_API_KEY","TWILIO_ACCOUNT_SID","TWILIO_AUTH_TOKEN","TWILIO_PHONE_NUMBER"]},"primaryEnv":"OPENAI_API_KEY"}}

Lowest Latency Calls

Architecture, configuration, and reference for the OpenAI Realtime API + Twilio phone system.

To PLACE calls, manage prospects, and run campaigns: pair this skill with your own outbound dialer / campaign layer. This skill is about the real-time call infrastructure itself.

DO NOT CHANGE (confirmed working, breaks if altered)

The call flow, session config format, and audio path below were debugged through many iterations. Do not restructure without reading this entire skill.

Session config — FLAT format only

// CORRECT:
{ type: "session.update", session: {
    modalities: ["text", "audio"], voice: "cedar",
    turn_detection: { type: "semantic_vad", eagerness: "high", create_response: true, interrupt_response: true },
    input_audio_format: "g711_ulaw", output_audio_format: "g711_ulaw",
}}
// WRONG (API rejects): session: { type: "realtime", audio: { input: { format: ... } } }

Outbound call flow — caller-first

Callee picks up, says hello, THEN the agent responds. No forced greeting. Semantic VAD with create_response: true handles it automatically.

Audio path — direct passthrough

Audio deltas from OpenAI are already base64 g711_ulaw. Forward directly to Twilio. No PCM conversion, no gain control, no resampling.

Greeting trigger

conversation.item.create (user message) + response.create. NOT response.create with instructions. Trigger on session.updated, NOT session.created.

Twilio webhook

Must point to /twiml. Verify: check Twilio API, not assumptions.

SAFE TO TUNE

  • Prompt size: smaller = faster inference. Reference outbound prompt is ~478 tokens.
  • VAD eagerness: "high" first turn, "medium" after. Configurable.
  • Tool loading: lean tools first turn, full set after first response.done.
  • Voice: cedar is a solid default for all scenarios. Can change per scenario.
  • Inference priming: text-only response.create during pre-warm warms pipeline without audio.
  • Twilio edge: configure to colocate with your deployment region and OpenAI region for lowest RTT.

Debugging Checklist

Before adding patches when calls fail:

  1. Is the websocket server process running? (systemctl status <your-service>, pm2 status, or your equivalent)
  2. Single owner on the websocket port? lsof -i :<PORT>
  3. Twilio webhook URL correct? Check the Twilio API, not local config files.
  4. Check your server log (whatever path you configured — stdout, file, or journald)
  5. OpenAI outage? Check status.openai.com
  6. Session config accepted? Look for session.updated in logs. error after session.created = wrong config format.

Do not pile patches. If it worked before and doesn't now, check infrastructure first.

Restart Procedure (pattern)

Whatever process supervisor you use, the correct sequence is:

stop the websocket server
→ kill any orphaned listeners on the websocket port (lsof -i :<PORT> -t | xargs kill)
→ start the websocket server

Always stop → kill orphans → start. A bare restart can leave a stale listener holding the port.

Restore from Snapshot (pattern)

Keep a known-good copy of sessionManager.ts (the file most affected by tuning) in a snapshots directory alongside the source. To restore:

copy snapshots/sessionManager-TUNED-<date>.ts → src/sessionManager.ts
restart using the procedure above

Key Files (relative to the websocket-server project)

WhatPath
sessionManager.tswebsocket-server/src/sessionManager.ts
server.tswebsocket-server/src/server.ts
Snapshotswebsocket-server/snapshots/
Service unityour process supervisor unit file (systemd user unit, pm2 ecosystem file, etc.)
Logswherever you configured (stdout + journald, /var/log/..., pm2 logs, etc.)
.envwebsocket-server/.env (contains PORT)

Reference Documents

All reference docs in {baseDir}/docs/:

FileContent
{baseDir}/docs/01-overview.mdModel landscape, changelog
{baseDir}/docs/02-session-config.mdsession.update reference + defaults
{baseDir}/docs/03-prewarm-outbound.mdPre-warm: buffer, fallback, edge cases
{baseDir}/docs/04-inbound-modes.mdAI IVR, Receptionist, CSR with DB
{baseDir}/docs/05-async-tools.mdAsync tool calling
{baseDir}/docs/06-latency-tuning.mdAll latency levers
{baseDir}/docs/07-twilio-integration.mdPCMU format, edge, AMD, stream events
{baseDir}/docs/08-known-issues.mdBugs, workarounds, watch-later
{baseDir}/docs/09-openclaw-config.mdConfig + install/publish

Load the relevant doc before answering architecture or config questions.

Key Facts (always available without file load)

  • Model: gpt-realtime-1.5 (flagship), gpt-realtime-mini (cost-sensitive)
  • WebSocket: wss://api.openai.com/v1/realtime?model=gpt-realtime-1.5
  • Audio: mu-law / PCMU at 8 kHz mono, base64 encoded
  • Turn detection: semantic_vad with eagerness: "high" is the tested default
  • Pre-warm timeout: 10 seconds (fallback to cold connect)

Lessons

  1. Session config: flat format only. Nested is rejected.
  2. Trigger greeting on session.updated, not session.created.
  3. Semantic VAD works without prior audio response.
  4. Verify infrastructure before debugging behavior.
  5. Audio is already PCMU. No conversion needed.
  6. Prompt size directly affects per-turn latency.
  7. When patches pile up: stop, read docs, rewrite from baseline.

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

96.08%
按下载量换算1,483

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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

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