SKILLEMALL.ai

CD video-subtitle

日语视频自动翻译烧录技能。将日语视频转换为中文硬字幕,完整流程:ffmpeg 提取音频 → Whisper 日语转录 → LLM 翻译日→中 → SRT 转 ASS → ffmpeg NVENC 烧录硬字幕 → 验证。触发条件:用户提到视频字幕、硬字幕、字幕烧录、日语视频翻译、whisper 字幕、ass 字幕、或要求处理 .mp4 视频加字幕。

ClawHub Agent Skills author: xunnv v1.0.0 MIT-0 4 files · 1 script body ≈ 726 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

日语视频自动翻译烧录技能。将日语视频转换为中文硬字幕,完整流程:ffmpeg 提取音频 → Whisper 日语转录 → LLM 翻译日→中 → SRT 转 ASS → ffmpeg NVENC 烧录硬字幕 → 验证。触发条件:用户提到视频字幕、硬字幕、字幕烧录、日语视频翻译、whisper 字幕、ass…

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

ProcedureAI and agentsMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
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: 4. 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-subtitle) differs from the folder (video-japanese-subtitle)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 8 steps
  • 100Execution cost. Instruction body is 726 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 174: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
The skill performs the advertised subtitle workflow, but it can automatically send video transcript text to translation services and ships a hardcoded bearer token.
LLM: suspicious (high) · VirusTotal: · 16 Sept 2026