AD video-digest
视频深读——把没时间看的 YouTube 视频提炼成中文结构化笔记：概述主要内容、按主题整理成文、区分🧱事实与💭观点、附原链接+时间戳可跳回，支持追问深挖与内容平台选题素材。仅在用户提供 YouTube 链接/视频 ID 或明确说「视频深读 <链接>」时使用。抓 YouTube 字幕→中文笔记（默认中文，用户可指定其他输出语言），落盘可复用。笔记生成由执行本 skill 的 AI 按模板完成，脚本只负责抓取。英文 AI/科技/访谈/TED/讲座效果最佳。需要 python3 + yt-dlp + 网络可达 YouTube（本机代理或可直连）。
视频深读——把没时间看的 YouTube 视频提炼成中文结构化笔记：概述主要内容、按主题整理成文、区分🧱事实与💭观点、附原链接+时间戳可跳回，支持追问深挖与内容平台选题素材。仅在用户提供 YouTube 链接/视频 ID 或明确说「视频深读 <链接>」时使用。抓 YouTube…
As a process D 46/100 · 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: 9. 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") - note
frontmatter-keyunknown frontmatter key "agent_created"
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. 53 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1457 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (6 tags): a typed call is more reliable
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
- +2Single-language instructions
- +3Description length 276: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 53 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.