AI Agent Skills 中文导航平台

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/common-patterns.md
```
for interaction patterns
Never ask more than one question at a time

<HARD-GATE> Use the AskUserQuestion tool for every multiple-choice step — do NOT print options as plain text. Ask one question at a time. Wait for the user's answer before proceeding. After all parameters are collected, summarize and ask the user to confirm before running any transcription. </HARD-GATE>

Interaction Flow

Step 0: Prerequisites Check

Before config setup, silently check the environment:

bash

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)

Issue	Action
`coli` not found	Block. Tell user to run `npm install -g @marswave/coli` first
`ffmpeg` not found	Warn (WAV files still work). Suggest `brew install ffmpeg` / `sudo apt install ffmpeg`
Models not downloaded	Inform user: first transcription will auto-download models (~60MB) to `~/.coli/models/`

coli

is missing, stop here and do not proceed.

Step 0: Config Setup

shared/config-pattern.md

Step 0.

Initial defaults:

bash

# 当前目录:
mkdir -p ".listenhub/asr"
echo '{"model":"sensevoice","polish":true}' > ".listenhub/asr/config.json"
CONFIG_PATH=".listenhub/asr/config.json"

# 全局:
mkdir -p "$HOME/.listenhub/asr"
echo '{"model":"sensevoice","polish":true}' > "$HOME/.listenhub/asr/config.json"
CONFIG_PATH="$HOME/.listenhub/asr/config.json"

Config summary display:

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

Setup Flow (first run or reconfigure)

Ask in order:

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
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):

bash

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)

polish

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:

markdown

---
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"

Check prerequisites
Read config
Confirm: meeting.m4a, sensevoice, polish on

Run

coli asr -j --model sensevoice "meeting.m4a"

Polish the raw text
Display inline

"transcribe interview.wav, no polish"

Check prerequisites
Read config
Override polish to false for this session

Run

coli asr -j --model sensevoice "interview.wav"

Display raw transcript inline

asr

NPX Install

Tags

SKILL.md Content

When to Use

When NOT to Use

Purpose

Hard Constraints

Interaction Flow

Step 0: Prerequisites Check

Step 0: Config Setup

Setup Flow (first run or reconfigure)

Step 1: Get Audio File

Step 2: Confirm

Step 3: Transcribe

Step 4: Polish (if enabled)

Step 5: Present Result

Step 6: Export as Markdown (optional)

Composability

Examples