Tracked Video Analysis
Use this skill for long, noisy, or operationally awkward videos where trust and visibility matter as much as the final summary.
The core idea is simple:
- 1. Extract content first
- Structure it second
- Track both stages explicitly
Never claim that a background process is still running unless a live OS process or a fresh status file proves it.
Core workflow
1) Acquire the video reliably
Prefer this order:
- 1. direct local file
- direct downloadable link
- document upload
- external file host fallback
If chat media is inaccessible, ask for a direct link instead of retrying vague media access indefinitely.
Use tmp/video_analysis/ as the working directory.
2) Prepare local tools without root
Prefer workspace-local packages over system installs.
Useful local tools:
- - INLINECODE1
- INLINECODE2
- INLINECODE3
- INLINECODE4
If root/elevated package install is blocked, do not stall the task—install locally in the workspace when possible.
3) Run tracked extraction
Extraction should produce:
- - INLINECODE5
- INLINECODE6
- INLINECODE7
- INLINECODE8
Rules:
- - Prefer chunking over one-shot whole-video ASR.
- Prefer lighter ASR first for stability.
- Update status after each chunk.
- If a run dies, resume from files when practical instead of starting from zero automatically.
4) Run tracked final structuring
Structuring should produce:
- - INLINECODE9
- INLINECODE10
- INLINECODE11
This stage should:
- - clean filler and repeated phrases
- group related chunks
- infer categories
- normalize wording
- convert raw transcript into the user’s requested format
5) Report status honestly
Use these rules:
- - Extraction running → report INLINECODE12
- Extraction complete, no final process running → say so plainly
- Final structuring running → report INLINECODE13
- Final result ready → read
final_analysis.md and answer normally
Standard output formats
Common targets:
- - Category → function → description → benefit
- Category → function → short description
- Function list + timestamps
- Clean summary with confidence caveats
For noisy ASR, prefer readable normalization over false precision.
Status discipline
Do not say “the process is running” unless at least one of these is true:
- - the OS process is alive
- the relevant status file is actively updating
If extraction finished, explicitly say:
- - extraction is complete
- no live extraction process remains
- only structuring remains (if true)
Read these files when needed
- - Read
references/pipeline.md for the canonical tracked workflow and failure handling. - Use
scripts/transcribe_tracked_light.mjs for extraction as a starting point. - Use
scripts/final_structurer.py for initial structuring as a starting point.
Delivery style
Prefer concise, readable sections.
When the user wants a polished deliverable:
- 1. create a clean
.md file - keep the structure visually pleasant
- send it as a document/file if requested
Practical note
This skill is optimized for operational reliability, not perfect transcription fidelity. If ASR is messy, produce a useful structured summary with explicit uncertainty rather than pretending the raw transcript is exact.
追踪式视频分析
使用此技能处理时长较长、噪声较多或操作不便的视频,在这些场景中,可信度与可见性同最终摘要一样重要。
核心理念很简单:
- 1. 先提取内容
- 再结构化整理
- 明确追踪两个阶段
除非有活跃的操作系统进程或最新的状态文件证明,否则切勿声称后台进程仍在运行。
核心工作流程
1) 可靠获取视频
优先采用以下顺序:
- 1. 本地直接文件
- 直接可下载链接
- 文档上传
- 外部文件托管备用方案
如果聊天媒体无法访问,请直接索要链接,而非无限重试模糊的媒体访问。
使用 tmp/video_analysis/ 作为工作目录。
2) 无需 root 权限准备本地工具
优先使用工作区本地包,而非系统安装。
有用的本地工具:
- - ffmpeg-static
- ffprobe-static
- @xenova/transformers
- wavefile
如果 root/提权包安装被阻止,不要停滞任务——尽可能在工作区内本地安装。
3) 运行追踪式提取
提取应生成:
- - tmp/videoanalysis/status.json
- tmp/videoanalysis/progress.log
- tmp/videoanalysis/transcript.jsonl
- tmp/videoanalysis/analysis.md
规则:
- - 优先采用分块处理,而非一次性全视频 ASR。
- 优先使用较轻量的 ASR 以确保稳定性。
- 每处理完一个块后更新状态。
- 如果运行中断,尽可能从文件恢复,而非自动从头开始。
4) 运行追踪式最终结构化整理
结构化整理应生成:
- - tmp/videoanalysis/finalstatus.json
- tmp/videoanalysis/finalprogress.log
- tmp/videoanalysis/finalanalysis.md
此阶段应:
- - 清理填充词和重复短语
- 将相关块分组
- 推断类别
- 规范化措辞
- 将原始转录转换为用户要求的格式
5) 如实报告状态
使用以下规则:
- - 提取运行中 → 报告 status.json
- 提取完成,无最终处理运行中 → 明确说明
- 最终结构化整理运行中 → 报告 finalstatus.json
- 最终结果就绪 → 读取 finalanalysis.md 并正常回答
标准输出格式
常见目标:
- - 类别 → 功能 → 描述 → 收益
- 类别 → 功能 → 简短描述
- 功能列表 + 时间戳
- 带置信度说明的简洁摘要
对于噪声较大的 ASR,优先采用可读的规范化处理,而非虚假的精确性。
状态纪律
不要说“进程正在运行”,除非以下至少一项为真:
- - 操作系统进程处于活动状态
- 相关状态文件正在积极更新
如果提取已完成,明确说明:
- - 提取已完成
- 无活跃的提取进程残留
- 仅剩结构化整理(如果属实)
需要时读取这些文件
- - 读取 references/pipeline.md 了解规范化的追踪工作流程和故障处理。
- 使用 scripts/transcribetrackedlight.mjs 作为提取的起点。
- 使用 scripts/final_structurer.py 作为初始结构化整理的起点。
交付风格
优先采用简洁、可读的章节。
当用户需要精美的交付物时:
- 1. 创建干净的 .md 文件
- 保持结构视觉舒适
- 按需以文档/文件形式发送
实用说明
此技能针对操作可靠性进行了优化,而非完美的转录保真度。如果 ASR 结果杂乱,请生成带有明确不确定性的有用结构化摘要,而非假装原始转录是精确的。