SKILLEMALL.ai
GitHub ↗ RU Sign in

BD douyin-video-distiller

抖音视频蒸馏 Skill。用户提供抖音链接、视频文件或要求把短视频提炼成知识卡片、摘要、时间线、证据、行动项、Wiki 内容或第二大脑资料时使用；也适用于"帮我把这个视频真正变成可检索知识"等未明确说蒸馏的请求。

ClawHub Claude Code author: 3378925604-a11y v1.0.0 MIT-0 5 files body ≈ 480 tokens Open the sourceclawhub.ai↗ analyzed 26 h ago

抖音视频蒸馏 Skill。用户提供抖音链接、视频文件或要求把短视频提炼成知识卡片、摘要、时间线、证据、行动项、Wiki 内容或第二大脑资料时使用；也适用于"帮我把这个视频真正变成可检索知识"等未明确说蒸馏的请求。

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

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%
100
Quality 40%
70
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
  • 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"

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 (python) that frontmatter does not declare
  • 100Steps. 16 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 480 tokens
  • 100Running it twice. No mutating operations
  • medium 2 test cases, all positive: not one "should refuse" or "should ask first"

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)
  • +3Description length 106: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 16 items
  • +4Has examples (2 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

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

External checks

ClawHub: suspicious
The skill has a coherent video-distillation purpose, but it can upload whole media files and the NVIDIA API bearer token to a user-configurable endpoint, so it should be reviewed before installation.
LLM: suspicious (high) · 14 Sept 2026