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Agent Skill · 开发

giggle-generation-speech(咯咯笑一代演讲)

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

giggle-generation-speech 是开发类 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,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 12,429。

总安装 1,294周安装 55GitHub Stars 公开资料未说明下载 453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:giggle-generation-speech(咯咯笑一代演讲)
来源仓库:https://github.com/giggle-official/skills
仓库路径:skills/giggle-generation-speech
安装命令:
npx skills add https://github.com/giggle-official/skills --skill giggle-generation-speech
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/giggle-official/skills --skill giggle-generation-speech

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

giggle-generation-speech 用于辅助音频处理和音乐生成,适合生成配乐说明或处理播客素材。适用于语音合成、音频转写或视频配音等声音素材处理的场景。通过 npx skills add 命令从指定 GitHub 仓库安装使用。使用时需确认音频来源、输出格式和时长限制,涉及人声克隆时应核对授权边界。

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/giggle-generation-speech

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/giggle-official/skills --skill giggle-generati…;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

giggle-generation-speech 怎么安装?

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

giggle-generation-speech 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。

giggle-generation-speech 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。

giggle-generation-speech 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。

giggle-generation-speech 用的人多吗?

公开数据是累计安装 1,294 次,近一周安装 55 次,下载 453 次。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。

giggle-generation-speech 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
giggle-generation-speech

咯咯笑一代演讲 · 本页

1,294—接触敏感数据未明确有提醒

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

Text-to-Audio

Synthesizes text into AI voice/voiceover via giggle.pro. Supports multiple voice tones, emotions, and speaking rates.

⚠️ Review Before Installing

Please review the following before installing. This skill will:

  1. Write to ~/.openclaw/skills/giggle-generation-speech/logs/ – Task state files for Cron deduplication
  2. Register Cron (30s interval) – Async polling when user initiates speech generation; removed when complete
  3. Forward raw stdout – Script output (audio links, status) is passed to the user as-is

Requirements: python3, GIGGLE_API_KEY (system environment variable), pip packages: requests


API Key: Set system environment variable GIGGLE_API_KEY. Obtain it at giggle.pro while logged in: left sidebar → API Key (API 密钥). The script will prompt if not configured.

No inline Python: All commands must be executed via the exec tool. Never use heredoc inline code.
No Retry on Error: If script execution encounters an error, do not retry. Report the error to the user directly and stop.

Execution Flow (Phase 1 Submit + Phase 2 Cron + Phase 3 Sync Fallback)

Speech generation typically takes 10–30 seconds. Uses "fast submit + Cron poll + sync fallback" three-phase architecture.

Important: Never pass GIGGLE_API_KEY in exec's env parameter. API Key is read from system environment variable.

Continuous progress updates (default; user need not put this in their prompt)

TTS is usually 10–30 seconds; Phase 3 sync wait can run up to --max-wait (default 120s). The user does not need to nag for progress.

  1. After Phase 1 submit, say the task is submitted, give task_id, and set expectation: usually 10–30s, may need up to ~2 minutes on slow runs.
  2. Keep polling without being asked: register Cron ~30s for --query, and always run Phase 3 --query --poll as fallback—do not wait for the user to say “check now.”
  3. On each poll (Cron or --query): if still JSON/processing, paraphrase and say you are still waiting; from the 2nd JSON poll onward, on every other Cron tick you may send one minimal line (e.g. “Still generating…”) so the thread is not dead silent.
  4. When done: forward full signed audio links (plain-text success from script); on failure, forward the error. If nothing completes after ~15 minutes of wall-clock retries/Cron, explain, give task_id, remove Cron, suggest retry.
  5. If the user explicitly opts out of Cron pings, skip Cron but still run --query when they ask; Phase 3 --poll can remain your primary path if appropriate.

Phase 0: Guide User to Select Voice and Emotion (required)

Before submitting, you must guide the user to select voice and emotion. Do not use defaults.

  1. Run --list-voices to get available voices:
python3 scripts/text_to_audio_api.py --list-voices
  1. Display the voice list to the user in a readable format (voice_id, name, style, gender, etc.) and guide them to pick one
  2. Ask the user's preferred emotion (e.g. joy, sad, neutral, angry, surprise). Use neutral if no preference
  3. Only after the user confirms voice and emotion, proceed to Phase 1 submit

Phase 1: Submit Task (exec completes in ~10 seconds)

First send a message to the user: Speech task submitted; usually finishes in 10–30 seconds. You will query automatically and send the link as soon as it is ready—no need to nag for progress.

# Must specify user-selected voice and emotion
python3 scripts/text_to_audio_api.py \
  --text "The weather is nice today" \
  --voice-id "Calm_Woman" \
  --emotion "joy" \
  --speed 1.2 \
  --no-wait --json

# View available voices
python3 scripts/text_to_audio_api.py --list-voices

Response example:

{"status": "started", "task_id": "xxx"}

Immediately store task_id in memory (addMemory):

giggle-generation-speech task_id: xxx (submitted: YYYY-MM-DD HH:mm)

Phase 2: Register Cron (30 second interval)

Use the cron tool to register the polling job. Strictly follow the parameter format:

{
  "action": "add",
  "job": {
    "name": "giggle-generation-speech-<first 8 chars of task_id>",
    "schedule": {
      "kind": "every",
      "everyMs": 30000
    },
    "payload": {
      "kind": "systemEvent",
      "text": "Speech task poll: exec python3 scripts/text_to_audio_api.py --query --task-id <full task_id>, handle stdout per Cron logic. If stdout is non-JSON plain text, forward to user and remove Cron. If stdout is JSON (still processing), keep Cron; from the 2nd JSON poll onward, alternate: every other poll send user one brief line (e.g. Speech still generating…). If stdout is empty, remove Cron immediately, do not send message."
    },
    "sessionTarget": "main"
  }
}

Cron trigger handling (based on exec stdout):

stdout patternAction
Non-empty plain text (not starting with {)Forward to user as-is, remove Cron
stdout emptyAlready pushed, remove Cron immediately, do not send message
JSON (starts with {, has "status" field)Keep Cron; per this section, from 2nd JSON poll onward send a brief line every other poll

Phase 3: Sync Wait (optimistic path, fallback when Cron hasn't fired)

Execute this step whether or not Cron registration succeeded.

Immediately before this exec, send the user a short line: Waiting for synthesis (usually quick, up to ~2 minutes)—please wait.

python3 scripts/text_to_audio_api.py --query --task-id <task_id> --poll --max-wait 120

Handling logic:

  • Returns plain text (speech ready/failed message) → Forward to user as-is, remove Cron
  • stdout empty → Cron already pushed, remove Cron, do not send message
  • exec timeout → Cron continues polling

View Voice List

When the user wants to see available voices, run:

python3 scripts/text_to_audio_api.py --list-voices

The script calls GET /api/v1/project/preset_tones and displays voice_id, name, style, gender, age, language to the user.


Link Return Rule

Audio links returned to the user must be full signed URLs (with Policy, Key-Pair-Id, Signature query params). Do not strip response-content-disposition=attachment when the API returns it. The script only normalizes ~ → %7E; forward URLs as-is otherwise.


New Request vs Query Old Task

When the user initiates a new speech generation request, must run Phase 1 to submit a new task. Do not reuse old task_id from memory.

For the in-flight task, follow Phases 2–3 and this section—do not wait for the user to ask. For an older task, query that task_id when the user asks (or poll if they want updates).


Parameter Reference

ParameterRequiredDefaultDescription
--textyes-Text to synthesize
--voice-idyes-Voice ID; must get via --list-voices and guide user to choose
--emotionyes-Emotion: joy, sad, neutral, angry, surprise, etc. Guide user to choose
--speedno1Speaking rate multiplier
--list-voices--Get available voice list
--query--Query task status
--task-idrequired for query-Task ID
--pollno-Sync poll with --query
--max-waitno120Max wait seconds

Interaction Guide

Before each speech generation, complete this interaction:

  1. If the user did not provide text, ask: "Which text would you like to convert to speech?"
  2. Must guide user to select voice: Run --list-voices, display list, have user choose. Do not use default voice
  3. Must guide user to select emotion: Ask the user's preferred emotion (joy, sad, neutral, angry, surprise, etc.)
  4. After user confirms text, voice, and emotion, run Phase 1 submit → Phase 2 register Cron → Phase 3 sync wait

平台分布

Codex

35.57%
按下载量换算161

Claude

28.57%
按下载量换算129

Cursor

19.18%
按下载量换算87

Gemini CLI

8.3%
按下载量换算38

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。

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