SKILLEMALL.ai

BD linkpix-video-translate

自动识别视频语音并翻译为多国语言,支持 AI 配音、字幕与对口型,帮助视频快速面向全球市场。当用户要求翻译视频、视频配外语音、加外语字幕、视频出海本地化时必须触发。关键词:LinkPix、qhkit、视频翻译、翻译配音、外语字幕、对口型、视频本地化、出海视频、多语言视频、dubbing、字幕翻译。

ClawHub Agent Skills author: AutoAGC v0.1.5 MIT-0 2 files body ≈ 905 tokens Open the sourceclawhub.ai analyzed 2 d ago

自动识别视频语音并翻译为多国语言,支持 AI 配音、字幕与对口型,帮助视频快速面向全球市场。当用户要求翻译视频、视频配外语音、加外语字幕、视频出海本地化时必须触发。关键词:LinkPix、qhkit、视频翻译、翻译配音、外语字幕、对口型、视频本地化、出海视频、多语言视频、dubbing、字幕翻译。

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

ProcedureWriting and documentsMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
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: 2. 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")
  • note frontmatter-key unknown frontmatter key "homepage"

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, node) that frontmatter does not declare
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 905 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 149: enough signal without eating the budget
  • +4Structure: 7 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill performs a real video-translation workflow, but it asks agents to install and auto-upgrade an unpinned global CLI, persist credentials, upload local videos, and potentially start billable jobs without clear enough user confirmation.
LLM: suspicious (high) · 8 Sept 2026