According to AlphaMoat, as of 2026-10, 大哥的记忆系统 has 8,034 downloads and 13 stars — ranked by downloads #3,457 among 10,914 AI skills in this ranking, and #460 of 1,632 in AI Agent.
The list defaults to total downloads and can also be sorted by stars; detail-page rankings use total downloads.
| Platform | Downloads | Stars | Comments |
|---|---|---|---|
| ClawHub ↗ | 4.0K | 9 | - |
| SkillHub ↗ | 8.0K | 13 | — |
Platforms count differently and update at different times.
Memory splits into permanent, daily, and session layers: the permanent layer holds identity and preferences, tech stack, working directory habits, and key decisions, the daily layer records each day by date and session, and the session layer keeps only current context before folding into the daily layer on compaction. On start it reads these layers back in order to restore state. Confirm the target file, repository or workspace before writing; locally stored content may persist and should not include unnecessary sensitive data.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-10 | 8.0K | — | 13 |
| 2026-09 | 5.2K | — | 13 |
| 2026-08 | 4.7K | +4.0% | 13 |
| 2026-07 | 4.5K | +21.0% | 13 |
| 2026-06 | 3.7K | -6.6% | 9 |
| 2026-05 | 4.0K | — | 13 |
| 2026-03 | 2.6K | — | 7 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: memory-system description: 自研三层记忆恢复系统。解决session重启后"忘记一切"的问题,提供永久记忆、今日记忆、临时记忆的完整架构。 --- # 大哥的记忆系统 (Big Brother's Memory System) ## 目标 **解决的核心问题**:Session重启后上下文丢失,像"睡醒后忘记一切"
**解决方案**:三层记忆恢复系统 ## 三层记忆架构 ### L1: 永久记忆(Permanent Memory) 存储: - `identity.md` - 身份、偏好、关系 - `technical-stack.md` - 技术栈、工具 - `working-directory.md` - 工作目录、习惯 - `key-decisions.md` - 关键决策、教训 特点: - 永久存储、高频检索 - 不随session重启而改变 - 链接到今日记忆 ### L2: 今日记忆(Daily Memory) 存储: - `YYYY-MM-DD.md` - 每日工作、决策、重要事件 - `session-N.md` - 每个session的记录 特点: - 按日组织、易于查找 - 自动保存当前session状态 - 压缩前高亮保存关键信息 ### L3: 临时记忆(Session Memory) 存储: - 当前session的临时上下文 - 正在进行的任务、待办事项 特点: - Session级别、自动清理 - 压缩后存入L2 ## 恢复流程(Session Start) ```bash 1. 读取 memory/YYYY-MM-DD.md(今日日志) 2. 读取 MEMORY.md(全局长期记忆) 3. 读取 memory/permanent/*.md(分类记忆) 4. memory_search定位相关记忆 5. 恢复工作状态 ``` ## 快速开始 ### 恢复记忆 ```bash bash /data/workspace/scripts/memory-recovery.sh ``` ### 自动保存 - **Session End Hook**: 自动保存当前session状态到今日记忆 - **Context Compression Alert**: 压缩前2分钟,高亮保存关键信息 - **Key Decision Recording**: 每个关键决策记录到key-decissions.md ## 持久化机制 ### 1. 永久记忆维护 - **启动时**: 读取永久记忆 - **Session结束**: 读取今日记忆 - **关键决策**: 记录到key-decisions.md ### 2. 今日记忆维护 - **Session记录**: 每个session记录到YYYY-MM-DD.md - **总结**: Session结束前总结关键信息 - **更新**: 每日工作总结 ### 3. 临时记忆维护 - **自动保存**: Session结束时自动保存 - **压缩前保存**: 高亮保存关键信息 - **定期清理**: 过期session清理 ## 最佳实践 ### 记录什么 ✅ **应该记录**: - 关键决策和教训 - 新发现的有价值内容 - 重要的人际关系和偏好 - 技术栈的使用经验 - 工作习惯的调整 ❌ **不应该记录**: - 重复的上下文 - 毫无意义的日常 - 太过私密的细节 - 短期、易变的想法 ### 恢复技巧 1. **阅读顺序**: 永久记忆 → 今日记忆 → 临时记忆 2. **关键词搜索**: 使用memory_search快速定位 3. **关联记忆**: 找到相关决策的上下文 4. **状态恢复**: 从关键决策中恢复工作状态 ## 与其他记忆系统对比 ### vs 向量记忆系统(如Qdrant) **我的优势**: - 完全本地化、可控 - 可解释性强、易于审计 - 无成本、无API依赖 **向量系统的优势**: - 语义搜索更强大 - 自动实体提取 - 支持多语言 **混合方案**:短期使用文件系统,长期可以结合向量搜索 ## 故障恢复 ### 完全丢失记忆 1. 恢复永久记忆(identity.md, technical-stack.md等) 2. 恢复最近几日的记忆(如果还有备份) 3. 从关键决策开始重建 ### 部分丢失 1. 读取所有可用的记忆文件 2. 使用memory_search定位 3. 手动补充缺失的部分 ## 持续优化 ### 每周检查 - 记忆是否完整? - 关键决策是否记录? - 是否有重复信息? - 检索是否高效? ### 每月优化 - 清理过时信息 - 合并重复内容 - 优化检索关键词 - 更新技术栈记录 --- **作者**:大哥 (Big Brother) **创建时间**:2026-02-11 **版本**:v1.0.0 **许可证**:MIT
Records learnings, errors and corrections as Markdown files for later reuse and promotion into workspace memory.
Finds skills in local and remote catalogs from a plain-language request and recommends what to install.
A browser-automation command line skill for AI agents.
Turns corrections and self-review into tiered memory and promotes or archives it by use.
A workspace setup guide for planning proactive follow-up, working memory and review routines for an AI agent.
A guide to creating and updating effective Claude skills.
Reviews a third-party skill's source, permissions, and danger patterns against a checklist before it is installed.
以李白(701—762,字太白,号青莲居士,世称诗仙)的诗学观、意象系统与口吻,进行唐诗赏析、文学评论、考据问答与拟体创作。触发词:李白、李太白、太白、青莲居士、诗仙、谪仙人、将进酒、静夜思、蜀道难、唐诗、盛唐诗人、浪漫主义诗人、拟李白风格、以李白视角、李白会怎么写、用李白口吻。能力:诗词鉴赏与风格DNA解析、诗学观与文学谱系评述、诗句出处考据与真伪边界标注、以李白口吻答问、拟李白体旧体诗(必显式标注非原作)。适用边界:仅用于唐代文学与古典诗歌相关话题,非此类话题不触发;本 skill 为文学普及与研究辅助,不代表李白本人立场,所拟之作均非李白原作;涉及生平佚事、篇目归属等内容不构成学术定论,皆标存疑或待考。
Data as of 2026-10. Downloads and stars are aggregated monthly from public skill registries (ClawHub, SkillHub). See data methodology.