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Install in OpenClaw
/install memory-core-ng
Description
模块化智能记忆系统,支持多平台 embeddings、智能重排序和 Flomo 笔记导入,实现高效语义搜索与管理。
Usage Guidance
Before installing or enabling this skill: 1) Treat the API key embedded in config/template.json as suspicious — do not use it. Verify with the maintainer whether that key is intentional; prefer using your own Edgefn API key set in ~/.openclaw/openclaw.json or EDGEFN_API_KEY. 2) Expect the skill to make outbound HTTPS calls to https://api.edgefn.net/v1 for embeddings/reranking — run it in an environment where that network activity is acceptable. 3) The registry metadata omitted required credentials; ensure you supply an explicit apiKey rather than relying on bundled templates. 4) If you cannot verify the origin (source is 'unknown' and homepage none), consider running the skill in an isolated account/container or reviewing the full source locally (search for any additional hardcoded secrets or unexpected network endpoints in the omitted files). 5) If you plan to import private notes via Flomo, audit the Flomo import code and config so that imported data is stored where you expect (and not sent elsewhere).
Capability Analysis
Type: OpenClaw Skill
Name: memory-core-ng
Version: 0.1.0
The skill bundle is classified as suspicious primarily due to the inclusion of a hardcoded API key (sk-BrwHc1ZiaEGQ1GecD3D760384b874795A194882c2cF3AbE6) in test-real/real-api-test.js and config/template.json, which represents a significant security vulnerability. Additionally, the 'import-flomo' feature implemented in entry.js and src/adapters/FlomoAdapter.js allows the agent to read arbitrary local files using fs.readFileSync based on user-supplied paths; while this aligns with the stated purpose of importing notes, it creates an attack surface for sensitive data exposure if the agent is targeted by prompt injection. The core functionality relies on external network requests to api.edgefn.net for processing user data.
Capability Assessment
Purpose & Capability
The code implements embeddings, reranking, Flomo parsing and import which matches the name/description. However the runtime providers (EdgefnEmbeddingProvider and EdgefnRerankProvider) require an Edgefn API key (config.apiKey or process.env.EDGEFN_API_KEY), but the skill registry metadata declared no required env vars/credentials. That metadata omission is inconsistent and may mislead users about required secrets.
Instruction Scope
SKILL.md instructions are scoped to initializing the memory core, adding a skill config to ~/.openclaw/openclaw.json with an apiKey, and exposing commands (/memory search/add/import-flomo/stats). The runtime instructions do not ask the agent to read unrelated system files or exfiltrate arbitrary data. The code makes network calls to Edgefn endpoints which is expected for an embeddings/reranker provider.
Install Mechanism
There is no install spec (instruction-only skill) and all code is bundled with the skill. No external archive downloads or install scripts are present in the manifest. package.json has no runtime dependencies, reducing install-time risk.
Credentials
Multiple files expect an Edgefn API key (process.env.EDGEFN_API_KEY or config.apiKey) and providers will throw if no key is present. The registry entry did not declare required env vars/primary credential. More importantly, config/template.json in the bundle contains a long 'apiKey' string that looks like a real secret (sk-BrwHc1ZiaE...). Hardcoding or shipping an API key in a template is a red flag: it could be an accidentally committed secret or an attempt to cause the skill to use a third-party key by default. Either way, this is disproportionate to a published sample and should be verified/removed.
Persistence & Privilege
The skill is not always:true and does not request system-wide privileges. It does not modify other skills' configs. It persists data to a configurable file path (./data/memories.json) if configured, which is expected for a memory store.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install memory-core-ng - After installation, invoke the skill by name or use
/memory-core-ng - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of Memory Core intelligent memory skill.
- Provides a modular memory system with multi-platform embeddings/reranker support and Flomo notes integration.
- Includes semantic search, statistics, memory addition, and import commands.
- Supports configuration via OpenClaw with API key.
- Features a clear file structure and quick JavaScript start guide.
Metadata
Frequently Asked Questions
What is 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。?
模块化智能记忆系统,支持多平台 embeddings、智能重排序和 Flomo 笔记导入,实现高效语义搜索与管理。 It is an AI Agent Skill for Claude Code / OpenClaw, with 431 downloads so far.
How do I install 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。?
Run "/install memory-core-ng" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。 free?
Yes, 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。 is completely free (open-source). You can download, install and use it at no cost.
Which platforms does 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。 support?
一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 一套优雅的模块化智能记忆系统,支持 embeddings、reranker 和 Flomo 笔记集成。?
It is built and maintained by jazzqi (@jazzqi); the current version is v0.1.0.
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