sjht-cam-anno

安防摄像头视频 VL 模型微调数据集标注工具。用于从安防摄像头视频中提取关键帧、分析视频内容、生成结构化标注（含环境/人物/行为/风险描述），并输出符合 dataset.jsonl 格式的微调训练数据。Use when 用户需要对安防摄像头视频进行数据标注、生成 VL 模型训练数据集、处理 /root/hair-...

Before installing or running this skill, consider the following: - Verify ffmpeg and ffprobe are installed and available on the PATH. The scripts call these binaries via subprocess but the skill metadata does not declare them as requirements. - Confirm the 'read' tool referenced in SKILL.md is available in your agent/runtime (it's an agent UI/tool to open images); otherwise adjust instructions to use an available image viewer. - The SKILL.md and files use the name 'hair-cam-anno' while the registry slug is 'sjht-cam-anno' — ask the publisher or inspect provenance to ensure this package is legitimate and unmodified. - The instructions mention processing '/root/hair-cam' (absolute path). Do not run the tool against system/root directories unless you intentionally want to process those files; point it at a controlled dataset directory instead. - Privacy and legal: the tool is designed to generate detailed descriptions of people (age, appearance, actions). Ensure you have consent and legal authority to process and store CCTV footage and derived datasets; consider redacting or avoiding personally identifying attributes. - Data handling: the build_jsonl script writes relative/declared paths into dataset.jsonl (it uses 'data/{video_name}' as image paths). Verify these paths meet your training pipeline expectations and that sensitive images are stored/handled securely. - Run the scripts in an isolated environment (sandbox or container) when first testing. Inspect logs/output for unexpected behavior; nothing in the code performs network calls but testing in isolation reduces risk. - If you require the skill to be deployable across different environments, update the metadata to declare required binaries and clarify the expected directory layout and the 'read' tool dependency. If you want, I can: (1) point out the exact lines to change to declare binaries, (2) produce a minimal wrapper to validate ffmpeg presence before running, or (3) produce a safer system-prompt variant that avoids sensitive PII extraction.

Type: OpenClaw Skill Name: sjht-cam-anno Version: 1.0.0 The skill bundle is a legitimate tool for annotating security camera footage to create datasets for vision-language models. The Python scripts (extract_frames.py and build_jsonl.py) use standard libraries and system utilities like ffmpeg for video processing, with no evidence of data exfiltration, malicious execution, or prompt injection.