SKILLEMALL.ai

BD Audio-Segmenter

当用户想要**把长音频切成小段**、**音频切片**、**音频分割**、**把音频分成固定时长片段**、**制作语音数据集**、**准备Karaoke素材**、**翻唱音频切片**时自动触发。 支持单个音频文件或整个文件夹(支持递归),自动用 ffmpeg 把音频按指定秒数切成小片段,完美保留原始文件夹结构,并智能选择输出路径。 常见触发口语: - “帮我把这个音频切成60秒一段” - “把这个长音频分割成小段” - “音频切片,这个文件夹” - “把语音文件切成每段30秒” - “制作数据集,把音频切片” - “Karaoke素材切片” - “翻唱音频分割” - “把MP3切成小段” - “递归切片整个音频文件夹” 支持格式:mp3、wav、m4a、ogg、flac 等常见音频。 只处理音频切片相关需求,其他音频处理(如转格式、降噪)不触发。

ClawHub Agent Skills author: 顶尖王牌程序员 v1.1.9 MIT-0 9 files body ≈ 278 tokens Open the sourceclawhub.ai analyzed 2 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
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 name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • 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. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 278 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 9 example trigger phrases
  • +3Description length 380: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 14 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: suspicious
The skill performs audio slicing, but it also automatically installs packages, changes Python tooling, creates a virtual environment, and downloads FFmpeg without a clear user approval step.
LLM: suspicious (high) · 3 Jul 2026