BD video-analyzer
视频分析处理 — 本地视频反编译分析工具。将视频拆解为时间轴剧本、语音转文字、场景分析、跨模态关联和精华摘要，支持多ASR引擎切换（Whisper/Paraformer/SenseVoice）、中文NLP增强、PaddleOCR中文识别。v4.0 新增短视频平台适配（抖音/快手/B站/视频号）和自动剪辑建议（高光检测/冗余标记/EDL导出/字幕样式）。v4.1 新增tiny模型优先体验（75MB低门槛）、说话人分离质量评分、剪映draft.json导出。v4.2 新增场景管理（detect→slice一条链）、短视频爆款预测、实时直播分析（流式ASR+敏感词检测）。v4.3 新增纯音频输入（mp3/m4a/wav播客与录音）、批量队列（SQLite+硬件档位并发）、GPU自动加速（CT2 int8量化）、ASR配置统一（--asr-engine单参数）。
视频分析处理 — 本地视频反编译分析工具。将视频拆解为时间轴剧本、语音转文字、场景分析、跨模态关联和精华摘要，支持多ASR引擎切换（Whisper/Paraformer/SenseVoice）、中文NLP增强、PaddleOCR中文识别。v4.0…
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 51. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 43/100
- 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (video-analyzer) differs from the folder (video-analyzer-local)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 82 steps
- 100Execution cost. Instruction body is 2942 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- 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
- +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
- -222 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 382: enough signal without eating the budget
- +4Structure: 32 headings
- +3Step-by-step instructions: 82 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.