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MemWeaver

作者 Fret774 · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
115
总下载
0
收藏
0
当前安装
1
版本数
在 OpenClaw 中安装
/install memweaver
功能描述
Memory Profiler — Mine hidden patterns from your Agent's memory, confirm via interactive quiz, and generate a structured user profile.
安全使用建议
What this means for you: - Functionally coherent: The skill does what it says — it reads your agent memory files, runs local Python scripts to collect them, then uses the Agent/LLM to derive patterns and ask you questions. The included scripts do not phone home or require keys. - Privacy risk (important): The scripts are local, but the analysis step requires an LLM. If your agent uses a cloud-hosted model (OpenAI, Anthropic, etc.), your full memory content will likely be sent to that provider. If you want the analysis to remain private, run the skill with a locally hosted model or reduce the data sent (use --days small, remove very sensitive notes before running). - Practical precautions: - Inspect the two Python scripts (collect_memory.py, save_profile.py) yourself — they are small, use only stdlib, and only read/write workspace files. - Run collect_memory.py with a small --days first to see what will be included and review the JSON output before handing it to a model. - Confirm where your Agent's model runs. If using a hosted LLM, do not feed highly sensitive memory to it. - Keep backups or run in an isolated/test workspace if you want to trial the skill. - If you need stronger guarantees, request the author clarify model-hosting assumptions or modify the workflow to limit content before analysis. - No red flags for hidden network endpoints, extra credentials, or unusual install behavior were found. If you do not want your memory analyzed by an external hosted model, do not invoke the skill until you confirm a local-model setup.
功能分析
Type: OpenClaw Skill Name: memweaver Version: 0.1.0 MemWeaver is a memory analysis tool designed to extract user behavioral patterns from local logs and MEMORY.md files. The included Python scripts (collect_memory.py and save_profile.py) perform standard file read/write operations within the local workspace and lack any network capabilities, obfuscation, or data exfiltration logic. The instructions in SKILL.md guide the AI agent to perform analytical tasks consistent with the tool's stated purpose without attempting to subvert security controls or access sensitive system areas like SSH keys or environment variables.
能力评估
Purpose & Capability
Name/description (memory profiling, hidden-pattern mining, interactive quiz) match the included files and runtime instructions. The only binary required is python3 and the scripts read MEMORY.md and daily logs — which is exactly what the skill claims to need.
Instruction Scope
SKILL.md and README instruct the Agent to read .codebuddy/MEMORY.md and memory/*.md, run local scripts to gather content, then perform a multi-step LLM analysis and generate question batches. That scope is coherent with the stated purpose. Important caveat: the 'LLM deep analysis' step implies sending memory content to the agent's model; if the agent uses a hosted/cloud LLM, that will transmit sensitive memory data off your machine. The README's 'No external APIs' claim refers to the skill itself (no network calls in scripts) but does not eliminate data transmission to whatever LLM the agent uses.
Install Mechanism
No install spec; instruction-only with two small Python scripts. No downloads from remote URLs, no package installs, and scripts use only standard library. Low install risk.
Credentials
The skill requests no environment variables or credentials and only accesses files in the detected workspace (.codebuddy/MEMORY.md and .codebuddy/memory/*). This access is proportionate to the profiling goal. There are no unrelated credential or config requirements.
Persistence & Privilege
always:false and user-invocable:true (normal). The save script writes profiles under memweaver/output/ and backs up existing profiles; it does not modify other skills or system settings. No privileged or persistent system-level changes are requested.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install memweaver
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /memweaver 触发
  4. 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
v0.1.0
Initial release of memweaver – Memory Profiler - Digs into agent memory files (MEMORY.md + daily logs) to uncover preferences, behavioral patterns, and hidden traits. - Confirms findings with the user through an interactive batch questionnaire driven by memory evidence, emphasizing hidden insights and contradictions. - Outputs a structured YAML user profile with confidence scores and evidence citations. - Distinct from other tools by focusing on insight mining and persona discovery, rather than mere memory retrieval or compression. - Includes project importance reassessment, validating and updating status and priorities directly with the user.
元数据
Slug memweaver
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

MemWeaver 是什么?

Memory Profiler — Mine hidden patterns from your Agent's memory, confirm via interactive quiz, and generate a structured user profile. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 115 次。

如何安装 MemWeaver?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install memweaver」即可一键安装,无需额外配置。

MemWeaver 是免费的吗?

是的,MemWeaver 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

MemWeaver 支持哪些平台?

MemWeaver 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 MemWeaver?

由 Fret774(@fret774)开发并维护,当前版本 v0.1.0。

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