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asr阿斯鲁

Agent Skill

asr 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

19,266

周安装

787

GitHub Stars

49

下载量

6,170
CodexClaudeCursorGemini CLI

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

unknown

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:asr(阿斯鲁)
来源仓库:https://github.com/marswaveai/skills
仓库路径:skills/asr
安装命令:
npx skills add https://github.com/marswaveai/skills --skill asr
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/marswaveai/skills --skill asr

简介

本地离线音频转录,具有多语言支持和可选的人工智能润色。

  • 使用 coli asr 将音频文件转录为文本
  • 无需 API 密钥;通过 sensevoice 模型支持中文、英语、日语、韩语和粤语,或通过 Whisper-tiny 仅支持英语
  • 模型在首次使用时自动下载 (~60MB) 到 ~/.coli/models/
  • ;需要大肠杆菌
  • CLI 和 ffmpeg
  • (WAV 文件无需它即可工作)
  • 可选的人工智能润色步骤可以纠正标点符号,删除填充词,并在保留原始含义的同时提高可读性
  • 将成绩单导出为带有元数据(来源、日期、模型、持续时间、检测到的语言)的 Markdown 文件

SKILL.md

When to Use

  • User wants to transcribe an audio file to text
  • User provides an audio file path and asks for transcription
  • User says "转录", "识别", "transcribe", "语音转文字"

When NOT to Use

  • User wants to synthesize speech from text (use /tts)
  • User wants to create a podcast or explainer (use /podcast or /explainer)

Purpose

Transcribe audio files to text using coli asr, which runs fully offline via local speech recognition models. No API key required. Supports Chinese, English, Japanese, Korean, and Cantonese (sensevoice model) or English-only (whisper model).

Run coli asr --help for current CLI options and supported flags.

Hard Constraints

  • No shell scripts. Use direct commands only.
  • Always read config following shared/config-pattern.md before any interaction
  • Follow shared/cli-patterns.md for interaction patterns
  • Never ask more than one question at a time

Interaction Flow

Step 0: Prerequisites Check

Before config setup, silently check the environment:

COLI_OK=$(which coli 2>/dev/null && echo yes || echo no)
FFMPEG_OK=$(which ffmpeg 2>/dev/null && echo yes || echo no)
MODELS_DIR="$HOME/.coli/models"
MODELS_OK=$([ -d "$MODELS_DIR" ] && ls "$MODELS_DIR" | grep -q sherpa && echo yes || echo no)
IssueAction
coli not foundBlock. Tell user to run npm install -g @marswave/coli first
ffmpeg not foundWarn (WAV files still work). Suggest brew install ffmpeg / sudo apt install ffmpeg
Models not downloadedInform user: first transcription will auto-download models (~60MB) to ~/.coli/models/

If coli is missing, stop here and do not proceed.

Step 0: Config Setup

Follow shared/config-pattern.md Step 0 (Zero-Question Boot).

If file doesn't exist — silently create with defaults and proceed:

mkdir -p ".listenhub/asr"
echo '{"model":"sensevoice","polish":true}' > ".listenhub/asr/config.json"
CONFIG_PATH=".listenhub/asr/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Do NOT ask any setup questions. Proceed directly to the Interaction Flow with sensible defaults (sensevoice model, polish enabled).

If file exists — read config silently and proceed:

CONFIG_PATH=".listenhub/asr/config.json"
[ ! -f "$CONFIG_PATH" ] && CONFIG_PATH="$HOME/.listenhub/asr/config.json"
CONFIG=$(cat "$CONFIG_PATH")

Setup Flow (user-initiated reconfigure only)

Only run when the user explicitly asks to reconfigure. Display current settings:

当前配置 (asr):
  模型:sensevoice / whisper-tiny.en
  润色:开启 / 关闭

Ask in order:

  1. model: "默认使用哪个语音识别模型?"

- "sensevoice(推荐)" — 支持中英日韩粤,可检测语言、情绪、音频事件 - "whisper-tiny.en" — 仅英文

  1. polish: "转录后由 AI 润色文本?(修正标点、去语气词、提升可读性)"

- "是(推荐)" → polish: true - "否,保留原始转录" → polish: false

Save all answers at once after collecting them.

Step 1: Get Audio File

If the user hasn't provided a file path, ask:

"请提供要转录的音频文件路径。"

Verify the file exists before proceeding.

Step 2: Confirm

准备转录:

  文件:{filename}
  模型:{model}
  润色:{是 / 否}

继续?

Step 3: Transcribe

Run coli asr with JSON output (to get metadata):

coli asr -j --model {model} "{file}"

On first run, coli will automatically download the required model. This may take a moment — inform the user if models haven't been downloaded yet.

Parse the JSON result to extract text, lang, emotion, event, duration.

Step 4: Polish (if enabled)

If polish is true, take the raw text from the transcription result and rewrite it to fix punctuation, remove filler words, and improve readability. Preserve the original meaning and speaker intent. Do not summarize or paraphrase.

Step 5: Present Result

Display the transcript directly in the conversation:

转录完成

{transcript text}

─────────────────
语言:{lang} · 情绪:{emotion} · 时长:{duration}s

If polished, show the polished version with a note that it was AI-refined. Offer to show the raw original on request.

Step 6: Export as Markdown (optional)

After presenting the result, ask:

Question: "保存为 Markdown 文件到当前目录?"
Options:
  - "是" — save to current directory
  - "否" — done

If yes, write {audio-filename}-transcript.md to the current working directory (where the user is running Claude Code). The file should contain the transcript text (polished version if polish was enabled), with a front-matter header:

---
source: {original audio filename}
date: {YYYY-MM-DD}
model: {model used}
duration: {duration}s
lang: {detected language}
---

{transcript text}

Composability

  • Invoked by: future skills that need to transcribe recorded audio
  • Invokes: nothing

Examples

"帮我转录这个文件 meeting.m4a"
  1. Check prerequisites
  2. Read config
  3. Confirm: meeting.m4a, sensevoice, polish on
  4. Run coli asr -j --model sensevoice "meeting.m4a"
  5. Polish the raw text
  6. Display inline
"transcribe interview.wav, no polish"
  1. Check prerequisites
  2. Read config
  3. Override polish to false for this session
  4. Run coli asr -j --model sensevoice "interview.wav"
  5. Display raw transcript inline

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

35.82%
按下载量换算2,210

Claude

29.69%
按下载量换算1,832

Cursor

19.59%
按下载量换算1,209

Gemini CLI

9.57%
按下载量换算590

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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。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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