SKILLEMALL.ai

AD byted-sol-live-highlight-slicer

从一段直播录屏(.webm/.mp4/.mov)中自动识别高光时刻并切割为多个短视频片段,支持音频能量、场景变化、音画混合、音画并集和 ASR 关键词五种分析方式,可输出独立片段与合并版视频。适用于“直播录屏自动剪高光”“从回放里切出主播情绪高点”“按声音峰值提取精彩片段”这类任务。

ClawHub Agent Skills author: juzanxie-dev v1.0.0 MIT-0 8 files body ≈ 1 125 tokens Open the sourceclawhub.ai analyzed 2 d ago

从一段直播录屏(.webm/.mp4/.mov)中自动识别高光时刻并切割为多个短视频片段,支持音频能量、场景变化、音画混合、音画并集和 ASR 关键词五种分析方式,可输出独立片段与合并版视频。适用于“直播录屏自动剪高光”“从回放里切出主播情绪高点”“按声音峰值提取精彩片段”这类任务。

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

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
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: 8. 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 "requirements"
  • note frontmatter-key unknown frontmatter key "permissions"

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 (byted-sol-live-highlight-slicer) differs from the folder (skill-live-highlight-slicer)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 48 steps
  • 100Execution cost. Instruction body is 1125 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
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 142: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 48 items
  • +4Has examples (8 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: clean
This skill is a local video highlight tool that reads user-selected media and writes clip outputs locally, with ordinary hardening issues but no evidence of hidden access or malicious behavior.
LLM: benign (high) · VirusTotal: · 9 Jul 2026