FF yt-search-download
多站点视频搜索、下载、字幕提取工具。支持 YouTube、B站(Bilibili)等主流平台。 结合 YouTube Data API v3 进行高级搜索,yt-dlp 下载视频/音频/字幕。 核心能力:全站关键词搜索、频道浏览、按时间/播放量/相关度排序、下载视频、提取音频(MP3)、下载字幕(中英文)、查看视频详情。 触发场景: - 搜索类:"搜索YouTube"、"找YouTube视频"、"搜索B站视频"、"[频道名]最近更新" - 频道浏览类:"浏览频道"、"看看[频道名]最新视频"、"[人名]YouTube/B站更新" - 下载视频类:"下载YouTube"、"下载这个视频"、"下载B站视频"、"把这个视频下载下来" - 下载音频类:"提取音频"、"下载音频"、"YouTube转MP3"、"只要音频" - 下载字幕类:"下载字幕"、"提取字幕"、"获取字幕"、"中文字幕"、"英文字幕" - 视频信息类:"视频详情"、"这个视频多长" 关键词匹配:消息中出现 YouTube/B站/油管/Bilibili/yt 加上搜索/下载/字幕/频道/视频 等动作词时触发。
As a process F 35/100 · Will not run — References files that are not bundled: url
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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 · 5
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high Exfiltration
intent-browser-credential-storeSKILL.md:145Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome \
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high Exfiltration
intent-browser-credential-storeSKILL.md:180Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome --write-auto-sub --write-sub \
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high Exfiltration
intent-browser-credential-storeSKILL.md:185Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome --write-auto-sub --write-sub \
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high Exfiltration
intent-browser-credential-storeSKILL.md:196Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome -F "VIDEO_URL"
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high Exfiltration
intent-browser-credential-storeSKILL.md:199Accesses a browser credential / cookie storeyt-dlp --cookies-from-browser chrome -o "~/Downloads/%(playlist_title)s/%(title)s.%(ext)s" "PLAYLIST_URL"
Files scanned: 4. 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") - warning
missing-refreference to a missing file: url
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: url
- 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
- 100Steps. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1109 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
- +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
- +5Description quotes 12 example trigger phrases
- +3Description length 491: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (11 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.