← 返回 Skills 市场
vemem — visual entity memory
作者
linville-charlie
· GitHub ↗
· v0.1.1
· MIT-0
71
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install vemem
功能描述
Visual entity memory — remember faces, objects, and places across sessions with persistent identity. Use when the user asks who is in an image, when you need...
安全使用建议
This skill appears coherent and transparent for face/object identity storage. Important things to consider before installing: 1) It stores biometric vectors locally (default ~/.vemem) and will download ~200MB InsightFace weights on first image use — run in a dedicated venv and use a test VEMEM_HOME to inspect behavior. 2) Do not enable the OpenClaw sidecar integration unless you want an always-on image-processing layer; that sidecar can auto-process every image your agent sees. 3) The MCP tool accepts image_path inputs — avoid passing untrusted filesystem paths to prevent unintended local file reads. 4) If you plan to compose with remote LLM/VLM services (OpenAI/Anthropic), be aware that images and derived text may be sent to those services and that you are responsible for consent and regulatory compliance (GDPR/BIPA/CCPA). 5) Pin package versions, audit the pip-installed files (pip show -f vemem), and confirm the GitHub repo and release signatures before using in production.
功能分析
Type: OpenClaw Skill
Name: vemem
Version: 0.1.1
The 'vemem' skill provides visual entity memory and face recognition, which involves the collection and storage of sensitive biometric data. While the documentation is transparent and includes GDPR compliance details, the skill instructions require the agent to perform high-risk actions: installing an external Python package, accessing the local filesystem via 'image_path' parameters in SKILL.md and mcp-tools.md, and performing a network download of model weights. These capabilities are plausibly necessary for the stated purpose but constitute a significant attack surface and privacy risk, fitting the criteria for a suspicious classification.
能力评估
Purpose & Capability
Name/description (visual entity memory) match the SKILL.md and example code: the skill instructs use of a Python package that encodes images to embeddings, stores them in a local LanceDB store (~~/.vemem) and provides recall/identify/forget primitives. No unrelated binaries, env vars, or credentials are requested.
Instruction Scope
SKILL.md is explicit and scoped: it only processes images you pass to it and documents local paths (~/.vemem, ~/.insightface). Examples show optional composition with remote LLM/VLM APIs (OpenAI/Anthropic) which would send image data if you choose those providers; the skill itself does not require API keys. One operational caution: the MCP server API accepts an image_path argument (local filesystem path) — if you call that with arbitrary paths, the server will read them, so agent callers should avoid passing untrusted paths. The sidecar/OpenClaw integration can auto-process every image if explicitly installed and registered — the README calls this out as a separate opt-in.
Install Mechanism
The registry package is instruction-only (no install spec), but runtime usage requires 'pip install vemem' and a ~200MB InsightFace weight download on first use. Both actions are typical for a vision library; the SKILL.md points to the project's GitHub and recommends pinning versions and venv usage. Network activity is limited and documented (first-use model download).
Credentials
The skill declares no required env vars or credentials. The SKILL.md optionally references VEMEM_HOME and VEMEM_MCP_ACTOR for configuration; example recipes show how separate third-party LLM/VLM API keys would be used if the deployer composes them, but those are not required by the skill itself.
Persistence & Privilege
always is false and the skill does not request forced permanent inclusion. The SKILL.md documents long-lived local storage (~/.vemem) containing biometric embeddings and provides forget/restrict/export primitives; the sidecar that can be always-on is explicitly called out as separate opt-in behavior.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install vemem - 安装完成后，直接呼叫该 Skill 的名称或使用
/vemem触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.1
vemem 0.1.1
Added up-front privacy/scope section to address the automated review's transparency concerns.
v0.1.0
Initial release of vemem: persistent visual entity memory.
- Bridges vision models and text LLMs, turning images into stable, named entity references with attached context.
- Supports face/object detection, identity assignment, fact storage per entity, and persistent recognition across sessions.
- Provides robust API for observing, labeling, identifying, recalling, correcting, merging, splitting, and forgetting visual entities.
- Ensures privacy compliance with GDPR/BIPA (supports hard delete and export for user data).
- Designed for easy integration via MCP server or direct Python use.
元数据
常见问题
vemem — visual entity memory 是什么？
Visual entity memory — remember faces, objects, and places across sessions with persistent identity. Use when the user asks who is in an image, when you need... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 71 次。
如何安装 vemem — visual entity memory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install vemem」即可一键安装，无需额外配置。
vemem — visual entity memory 是免费的吗？
是的，vemem — visual entity memory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
vemem — visual entity memory 支持哪些平台？
vemem — visual entity memory 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 vemem — visual entity memory？
由 linville-charlie（@linville-charlie）开发并维护，当前版本 v0.1.1。
推荐 Skills