SKILLEMALL.ai

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产品介绍视频自动生成技能。当用户需要生成产品介绍视频、宣传视频、演示视频时、复刻声音时触发。支持:(1) 声音合成——若用户明确指定"我的"声音,则复刻声音并用于下次合成;否则自动使用edge-tts合成语音(支持男声/女声选择);(2) 基于用户提供的图片素材生成静态幻灯片视频(无需大模型生成视频);(3) 自动生成对应语言的SRT字幕(软字幕/硬字幕/无字幕可选)并与音频同步。(4)复刻声音。(5)口播引导视频:当脚本首段以"口播:"开头时,使用真人照片+TTS音频 并生成真人口播片段。适用场景:用户说"帮我生成产品视频"、"制作一个XX的介绍视频"、"用我的声音或男声或女声生成视频"、"把这些图片做成视频"、"生成一段口播视频"时,务必使用此技能。

ClawHub Agent Skills author: Delilegal v1.0.1 MIT-0 9 files body ≈ 2 714 tokens Open the sourceclawhub.ai analyzed 16 h ago

产品介绍视频自动生成技能。当用户需要生成产品介绍视频、宣传视频、演示视频时、复刻声音时触发。支持:(1) 声音合成——若用户明确指定"我的"声音,则复刻声音并用于下次合成;否则自动使用edge-tts合成语音(支持男声/女声选择);(2) 基于用户提供的图片素材生成静态幻灯片视频(无需大模型生成视频);(3)…

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

IntegrationSoftware developmentMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
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: 9. 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 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. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2714 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -32 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 331: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This video skill appears purpose-built, but it needs review because it can upload voice and portrait media, persist voice/API data, install packages at runtime, and delete a user-chosen output directory.
LLM: suspicious (high) · 21 Jul 2026