SKILLEMALL.ai

BD video-to-text

提取视频语音并转成中文文字稿。当用户给出抖音、B站(bilibili/b23.tv)、YouTube、快手、视频号等常见视频网站的链接,想要"提取视频里说话的内容""视频转文字""语音转文字""视频字幕提取"时,必须使用本技能。即使用户只发了一个视频链接没说要做什么,也应主动用本技能识别并转写其中的说话内容。

ClawHub Agent Skills author: Luo4507 v1.0.0 MIT-0 6 files · 1 script body ≈ 439 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

提取视频语音并转成中文文字稿。当用户给出抖音、B站(bilibili/b23.tv)、YouTube、快手、视频号等常见视频网站的链接,想要"提取视频里说话的内容""视频转文字""语音转文字""视频字幕提取"时,必须使用本技能。即使用户只发了一个视频链接没说要做什么,也应主动用本技能识别并转写其中的说话内容。

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureYouTubePlaywrightMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
D
41/100
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: 6. 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 41/100

  • 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
  • 40Consistency. Frontmatter name (video-to-text) differs from the folder (video-to-text-zh)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 17 steps
  • 100Execution cost. Instruction body is 439 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
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 155: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (2 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

External checks

ClawHub: suspicious
The skill’s video transcription purpose is coherent, but installation deserves review because it can automatically fetch video links, use a headless browser, and disables TLS certificate verification during downloads.
LLM: suspicious (high) · 28 Sept 2026