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Memory Dreaming
作者
Peter Rossi
· GitHub ↗
· v0.1.2
· MIT-0
112
总下载
0
收藏
0
当前安装
3
版本数
在 OpenClaw 中安装
/install openclaw-memory-dreaming
功能描述
A Markdown + JSON memory framework with conversation archiving for AI agents. Provides persistent long-term memory with biologically-inspired decay, recall b...
安全使用建议
This skill is coherent with its stated goal (local memory + archiving), but review these before installing:
- Expectation vs reality: The registry lists no required env vars, yet the summariser will need an OpenRouter/OpenAI API key (found via .env files or env vars). If you don't want transcripts leaving your host, do not set an external API key or configure the summariser to use a self-hosted model.
- Data exfiltration risk: conversation-summarise sends chat transcripts to third-party APIs. It attempts to redact secrets using regex, but redaction is inherently imperfect — sensitive tokens, credentials, or PII could leak. If you must summarise externally, audit redactSecrets patterns for your environment or avoid external APIs.
- File access: the archiver reads OpenClaw session stores (paths include relative and absolute locations). Run the scripts in an isolated workspace or with least privilege to avoid touching other agents' session data.
- Prompt content: SKILL.md contains agent-oriented payloads and was flagged for 'system-prompt-override' patterns. Inspect SKILL.md and the cron payload examples to ensure nothing unintentionally overrides agent/system prompts when scheduled.
- Practical steps: (1) Inspect the six scripts yourself; (2) Run archiving locally with sample transcripts first; (3) If you need summarisation, prefer a self-hosted LLM or supply a dedicated API key with limited scope; (4) Remove or sanitize any .env files you don't want scanned; (5) Keep backups of MEMORY.md and memory/archive before running decay/prune operations.
Given the credential-metadata mismatch and the privacy-sensitive behavior (external LLM calls + regex redaction), treat this skill cautiously and verify configuration and code before enabling nightly automation.
功能分析
Type: OpenClaw Skill
Name: openclaw-memory-dreaming
Version: 0.1.2
The 'memory-dreaming' skill bundle is a well-documented framework for managing agent memory and conversation archives. While the script 'conversation-summarise.js' sends data to external LLM APIs (OpenRouter or OpenAI), it includes a dedicated 'redactSecrets' function designed to strip API keys, tokens, and passwords before transmission. All other components, including the 'dream-cycle' logic and memory decay scripts, operate strictly on local files within the agent's workspace. The instructions provided to the AI agent in SKILL.md and the reference documents are transparent, functional, and lack any indicators of prompt injection or malicious intent.
能力评估
Purpose & Capability
The scripts and documentation align with the stated purpose (local Markdown/JSON memory, conversation archiving, nightly 'dream' cycles). However the registry metadata claims no required env vars while README/SKILL.md explicitly require OPENROUTER_API_KEY or OPENAI_API_KEY for the summariser — a mismatch between declared requirements and actual behavior.
Instruction Scope
Runtime instructions and scripts will read OpenClaw session stores and workspace files, write archives and memory files, and (for summarisation) send conversation text to external LLM APIs. The summariser reads .env files and environment variables for API keys and applies regex-based redaction before sending — redaction can miss secrets. The SKILL.md also includes cron payload examples and agent-oriented instructions; a scanner flagged a 'system-prompt-override' pattern inside SKILL.md (see scan findings).
Install Mechanism
This is instruction-only (no install spec). Files are plain JS scripts copied into the workspace; no remote downloads or installers are executed by the skill itself. Risk from install mechanism is low, but running the scripts will write and modify files in the workspace.
Credentials
The skill actually requires an LLM API key for conversation summarisation (OPENROUTER_API_KEY or OPENAI_API_KEY) but registry metadata lists none. The summariser inspects local .env files and environment variables to find keys. That credential use is functionally justified for summarisation, but the mismatch in metadata and the broad file access (session stores, .env) merit caution.
Persistence & Privilege
The skill does not request always:true or other elevated platform privileges. It writes files in the workspace and can be scheduled via cron/heartbeat as documentation describes; this is expected for a memory/archiving tool and limited to its own files.
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 openclaw-memory-dreaming - 安装完成后,直接呼叫该 Skill 的名称或使用
/openclaw-memory-dreaming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
v0.1.2
Security hardening: built-in secret redaction in summariser (strips API keys, tokens, passwords, AWS creds before sending to LLM). Added Security & Privacy section to README. Declared source/homepage URLs.
v0.1.1
Added credentials & privacy section addressing ClaWHub security scan findings. Declared source/homepage. Explicit documentation of what data is read, written, and sent externally.
v0.1.0
Initial release: biologically-inspired memory framework with dream-cycle consolidation, conversation archiving, and temporal fact chains. All Markdown + JSON — no vector DB required.
元数据
常见问题
Memory Dreaming 是什么?
A Markdown + JSON memory framework with conversation archiving for AI agents. Provides persistent long-term memory with biologically-inspired decay, recall b... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 112 次。
如何安装 Memory Dreaming?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install openclaw-memory-dreaming」即可一键安装,无需额外配置。
Memory Dreaming 是免费的吗?
是的,Memory Dreaming 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Memory Dreaming 支持哪些平台?
Memory Dreaming 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 Memory Dreaming?
由 Peter Rossi(@ptburkis)开发并维护,当前版本 v0.1.2。
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