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BB video-subtitle-translation-dubbing

Multi-language video subtitle translation and automatic dubbing skill (supports English, Chinese, Japanese, Spanish, French, German, Korean, etc.).

ClawHub Agent Skills author: zbjincheng v0.1.4 MIT-0 59 files body ≈ 1 694 tokens Open the sourceclawhub.ai↗ analyzed 9 h ago

Multi-language video subtitle translation and automatic dubbing skill (supports English, Chinese, Japanese, Spanish, French, German, Korean, etc.).

As a process B 75/100 · Nearly there — weak spots: when it triggers, progress reporting

ProcedureMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
73
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
50
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv README.md:66
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv README.zh-CN.md:66
    Reads a .env file
    cp .env.example .env

Files scanned: 59. 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 75/100

  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1694 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

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)
  • -34 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 147: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 17 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill appears to perform the advertised subtitle translation and dubbing workflow, but it should be reviewed because it can send subtitles, TTS text, and provider API keys to arbitrary endpoints, including unencrypted HTTP URLs.
LLM: suspicious (high) · 12 Sept 2026