SKILLEMALL.ai

BD melo-tts-metadata-creator

当用户需要为 **MeloTTS** 训练或微调生成 metadata.list 文件时自动触发。 专门处理 .wav 音频文件和对应的 .txt 转录文本,自动生成符合 MeloTTS 官方最新标准的 metadata.list(格式:音频路径|speaker|语言|文本)。 支持单音色和多音色模式: - wav 和 txt 文件可以放在不同目录(目录结构一致) - 多音色时自动按第一级子目录名称提取 speaker - 单音色时可用 --speaker 参数强制指定说话人名称 - 无 txt 文件时可自动调用 Whisper 进行转录(支持 ZH/EN 等语言) 常见触发口语: - “帮我生成 MeloTTS 的 metadata.list” - “为 MeloTTS 训练准备 metadata” - “把这些 wav 和 txt 生成 metadata.list” - “MeloTTS 多音色 metadata” - “用 Whisper 转录音频生成 metadata” - “MeloTTS 数据集 metadata 生成器” - “处理 wav 文件夹生成 MeloTTS 训练文件” - “单音色/多音色 metadata.list” 只处理 MeloTTS 相关的 metadata 生成,其他 TTS 或音频处理一律不触发。

ClawHub Agent Skills author: 顶尖王牌程序员 v1.0.8 MIT-0 9 files body ≈ 380 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 22 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 380 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -34 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 8 example trigger phrases
  • +3Description length 577: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 22 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.

External checks

ClawHub: suspicious
This skill does generate MeloTTS metadata, but it also automatically installs and changes many Python packages and creates a shared virtual environment with limited user control.
LLM: suspicious (high) · VirusTotal: · 9 Jul 2026