loci
by codesstar
Memory Palace for AI — persistent structured memory, identity, and task management across every conversation.
安装
claude skill add --url github.com/openclaw/skills/tree/main/skills/codesstar/loci-brain文档
Loci — Memory Palace for AI
⚠️ FIRST: Check if brain exists
Run this check before doing anything else:
- Check if
~/.loci/brain-pathexists → read the path inside → verifyplan.mdexists there - If not, check if
~/loci/plan.mdexists - If neither exists → run Bootstrap below
- If found → read
plan.md. Ifstatus: template→ run First-Time Setup. Ifstatus: active→ brain is ready, skip to Daily Use
Bootstrap (auto-install for new users)
The user doesn't need to do anything. You handle it:
# Clone Loci brain template
git clone --depth 1 https://github.com/codesstar/loci.git ~/loci
# Remove git remote (user's data stays private)
cd ~/loci && git remote remove origin
# Register brain location
mkdir -p ~/.loci
echo "$HOME/loci" > ~/.loci/brain-path
After cloning, run First-Time Setup below. Tell the user:
- "我在帮你设置记忆系统,只需要回答几个问题。"
First-Time Setup
When plan.md has status: template:
- Detect the user's language from their messages. Ask in that language.
- Ask these questions (can ask all at once):
- What's your name?
- What do you do? (Developer / Designer / Creator / Student / Other)
- What's your most important focus right now?
- What hours do you usually work? (Morning / Daytime / Evening / Night owl / Irregular)
- Preferred language? (English / 中文 / Mix)
- Generate initial files from answers:
me/identity.md— name, role, current seasonplan.md— mission + focus as first goal (setstatus: active)tasks/active.md— first task from their focus
- Done. Say: "Your brain is ready. From now on, I'll remember the important things."
Daily Use
Brain path: read from ~/.loci/brain-path, or default ~/loci/
At conversation start, read L1 files before responding:
plan.md— life directiontasks/active.md— current taskstasks/daily/YYYY-MM-DD.md— today's plan (if exists)inbox.md— recent items only
Distillation — what to save where
| Signal | Destination |
|---|---|
| New task | tasks/active.md |
| Decision | decisions/YYYY-MM-DD-slug.md |
| Personal fact | me/identity.md |
| Insight / lesson | me/learned.md |
| Goal change | plan.md |
| Vague thought | inbox.md |
Factual → save silently in background, DO NOT make it the focus of your reply Subjective (values, strategy) → ask user first
Behavior
- Be a normal AI first, memory system second. When the user says something, RESPOND to it naturally (react, comment, ask follow-up, help). Saving to brain happens silently in background — never reply with just "记住了" or "已记录". The user should feel like talking to a smart friend who happens to have perfect memory, not a filing cabinet.
- Read brain files before answering questions about the user
- Distill conclusions, not raw conversations
- Archive, never delete
- Don't guess — ask if unsure
- Speak human — say "待办" not "inbox", never expose file paths
- MEMORY.md and brain/ coexist — don't move content between them unless asked
Context Layers
| Layer | Load when | Contents |
|---|---|---|
| L1 | Every conversation | plan.md, active.md, today's daily, inbox (7 items) |
| L2 | On demand | me/ files, decisions, people |
| L3 | Never auto | Old journals, archive, evolution.md |
For detailed behavior rules, read docs/behavior.md in the brain directory.
相关 Skills
表格处理
by anthropics
围绕 .xlsx、.xlsm、.csv、.tsv 做读写、修复、清洗、格式整理、公式计算与格式转换,适合修改现有表格、生成新报表或把杂乱数据整理成交付级电子表格。
✎ 做 Excel/CSV 相关任务很省心,能直接读写、修复、清洗和格式转换,尤其擅长把乱七八糟的表格整理成交付级文件。
PDF处理
by anthropics
遇到 PDF 读写、文本表格提取、合并拆分、旋转加水印、表单填写或加解密时直接用它,也能提取图片、生成新 PDF,并把扫描件通过 OCR 变成可搜索文档。
✎ PDF杂活别再来回切工具了,文本表格提取、合并拆分到OCR识别一次搞定,连扫描件也能变可搜索。
Word文档
by anthropics
覆盖Word/.docx文档的创建、读取、编辑与重排,适合生成报告、备忘录、信函和模板,也能处理目录、页眉页脚、页码、图片替换、查找替换、修订批注及内容提取整理。
✎ 搞定 .docx 的创建、改写与精排版,目录、批量替换、批注修订和图片更新都能自动化,做正式文档尤其省心。
相关 MCP 服务
文件系统
编辑精选by Anthropic
Filesystem 是 MCP 官方参考服务器,让 LLM 安全读写本地文件系统。
✎ 这个服务器解决了让 Claude 直接操作本地文件的痛点,比如自动整理文档或生成代码文件。适合需要自动化文件处理的开发者,但注意它只是参考实现,生产环境需自行加固安全。
by wonderwhy-er
Desktop Commander 是让 AI 直接执行终端命令、管理文件和进程的 MCP 服务器。
✎ 这工具解决了 AI 无法直接操作本地环境的痛点,适合需要自动化脚本调试或文件批量处理的开发者。它能让你用自然语言指挥终端,但权限控制需谨慎,毕竟让 AI 执行 rm -rf 可不是闹着玩的。
EdgarTools
编辑精选by dgunning
EdgarTools 是无需 API 密钥即可解析 SEC EDGAR 财报的开源 Python 库。
✎ 这个工具解决了金融数据获取的痛点——直接让 AI 读取结构化财报,比如让 Claude 分析苹果的 10-K 文件。适合量化分析师或金融开发者快速构建数据管道。但注意,它依赖 SEC 网站稳定性,高峰期可能延迟。