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AD video-pipeline-bundle

视频一站式工作流技能包。整合视频剪辑、转写、烧录、拼接全流程，支持分步执行和用户确认。包含：(1) auto-editor - 视频剪辑去除静音片段；(2) Faster Whisper + MiniMax LLM - 语音转字幕；(3) ffmpeg - 烧录字幕到视频；(4) FFmpeg 工具箱 - 拼接、格式转换；(5) pipeline.py - 完整工作流编排。使用场景：批量处理视频、从原始素材到成品的完整流程。

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 1 351 tokens Open the sourcegithub.com↗ analyzed 2 d ago

视频一站式工作流技能包。整合视频剪辑、转写、烧录、拼接全流程，支持分步执行和用户确认。包含：(1) auto-editor - 视频剪辑去除静音片段；(2) Faster Whisper + MiniMax LLM - 语音转字幕；(3) ffmpeg - 烧录字幕到视频；(4) FFmpeg 工具箱 -…

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

ProcedureInfrastructureMedia 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
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: 6. 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 (python) that frontmatter does not declare
  • 100Steps. 19 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1351 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 214: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 19 items
  • +4Has examples (14 code blocks)
  • +3All 5 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.