掌眼小助理

专注文物、书画、瓷器、玉器与古董收藏领域的智能鉴定助手，帮助用户进行初步分析、风险识别、收藏建议与鉴定思路讲解。

What to consider before installing: - Function: The skill is internally consistent for giving preliminary, image‑based antique/collectible advice and includes thorough templates, prompts, and simple Python utilities; it explicitly disclaims official or judicial authentication. - No secrets needed: it asks for no credentials or environment variables. - Image handling: although the manifest lists an 'image_based_precheck' capability, the shipped scripts do not process image files — they only inspect text/keywords and prompt the user to supply photos. If you expect automated image analysis, confirm how images will be handled (by the platform, by a separate tool, or not at all). - Privacy & risk: the skill’s workflow asks users to upload photos of objects; do not share sensitive provenance documents or high‑resolution images you don't want stored publicly. The skill is for preliminary, not authoritative, conclusions — for high‑value transactions always get an in‑person or institutional appraisal. - Source origin: the Source/Homepage are unknown. While the code here is readable and contains no networking/exfiltration, consider running it in a sandbox or reviewing the scripts yourself before deploying in a sensitive environment. - Minor implementation notes: the scripts use simple keyword matching and may misclassify or miss context; they are utility helpers rather than robust NLP/image‑analysis modules. If you need, I can: (a) summarize exact places the skill will prompt users for images/data, (b) point out any lines in the scripts you'd like explained, or (c) suggest security checks to run before enabling the skill (e.g., run the Python files in a restricted environment to confirm no outbound network activity).

Type: OpenClaw Skill Name: zhangyan-assistant Version: 1.0.0 The skill bundle is a legitimate antique appraisal assistant called 'Zhangyan Assistant'. The included Python scripts (intake_checklist.py, risk_flagger.py, etc.) are simple text-processing utilities that use keyword matching to help the agent identify missing information or potential risks in user descriptions; they contain no network calls, file system modifications, or dangerous execution functions. The extensive documentation and system prompts are well-structured and focus on maintaining professional boundaries and preventing the AI from making overconfident or fraudulent claims, with no evidence of malicious intent or prompt injection.