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soul-dreaming灵魂做梦

Agent Skill

soul-dreaming 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,221

周安装

89

GitHub Stars

公开资料未说明

下载量

719
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:soul-dreaming(灵魂做梦)
来源仓库:https://github.com/xuexian1211/soul-dreaming
安装命令:
openclaw skills install soul-dreaming
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install soul-dreaming

简介

渐进式内存管理系统防止 AI 上下文遗忘。

  • 自动分类文件并建立索引加速检索。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 支持生存合并机制优化长期记忆结构。
  • 需定期手动触发进化以避免信息冗余。
  • 建议设置存储配额控制资源占用。soul-dreaming 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
soul-dreaming
description
>

Dreaming — Progressive Memory Management for AI Agents

Prevent amnesia from context compaction. Persist what matters, forget what doesn't.

Memory Architecture (v2 — Categorized Split)

workspace/
└── memory/
    ├── INDEX.md            # 索引 — 唯一必须全量加载的文件
    ├── decisions.md        # 架构决策 + 理由(为什么选 X 不选 Y)
    ├── lessons.md          # 教训与经验(bug 根因、workaround、踩坑记录)
    ├── project-context.md  # 项目上下文(技术栈、目录结构、部署信息)
    ├── people.md           # 人物信息(偏好、角色、沟通习惯)
    ├── tools-config.md     # 工具与环境(命令备忘、环境配置、代理设置)
    ├── YYYY-MM-DD.md       # 每日日记(自动创建)
    └── archive/            # 归档(低价值淘汰 + 超期条目)

Line Budget per File(每文件行数预算)

FileBudgetRationale
INDEX.md80Index only — fast to scan
decisions.md200Architecture decisions with rationale
lessons.md300Bug patterns, workarounds — high volume
project-context.md150Stack, schema, config, deploy info
people.md100User preferences, roles
tools-config.md100Commands, env, proxy notes
Each daily journal100Raw events, temporary

Record Format(标准记录格式)

Every entry in categorized files uses this format:

## [YYYY-MM-DD] Title
- Tags: tag1, tag2, tag3
- Fitness: last_referenced=YYYY-MM-DD, hit_count=N, decision_value=HIGH|MEDIUM|LOW
- Body: one-line summary of the record

Three Stages (Core — 三阶段提炼)

Stage 1: Light Sleep — Every Session End

Scan the current session. Write to today's journal (memory/YYYY-MM-DD.md):

  1. Decisions made — anything that affects future work
  2. Key discoveries — bugs found, root causes, unexpected behaviors
  3. Pending tasks — unfinished items that must survive session restart
  4. Context anchors — file paths, API endpoints, config values

Iron rule (铁律): Write decisions the moment they're made, not at session end. Context can vanish at any time.

Stage 2: Deep Sleep — Every 3 Days

Promote from daily journals to categorized memory files:

  1. Scan last 3 days of journals
  2. Evaluate each item against promotion criteria
  3. Route to the correct file (decisions → decisions.md, bugs → lessons.md, etc.)
  4. Delete absorbed journal entries

Promotion criteria:

  • Referenced across 2+ sessions/days
  • Has ongoing decision value
  • Contains a hard-won lesson
  • Addresses a recurring pattern

Routing map:

Journal item typeTarget file
Architecture/design choicedecisions.md
Bug root cause, workaroundlessons.md
Tech stack, deploy infoproject-context.md
User preference, team rolepeople.md
Command, env configtools-config.md

Stage 3: REM — Weekly

Memory hygiene via Survival Merge Protocol (优胜劣汰):

  1. Evaluate fitness of all entries across all files
  2. Archive low-value entries → memory/archive/
  3. Promote high-value — entries hit 5+ times → mark [PROVEN]
  4. Handle conflicts — supersede old with new, keep one-line summary
  5. Rebuild INDEX.md — update all stats
Detailed protocol: references/survival-merge.md

Mount Strategy(挂载策略 — 三级加载)

Level 1 — Always Mount(常驻加载)

  • memory/INDEX.md — the only file you must load fully on every wake
  • Today's memory/YYYY-MM-DD.md
  • Yesterday's memory/YYYY-MM-DD.md

Level 2 — On-Demand Mount(按需加载)

Based on INDEX.md search hints, load when the task touches a domain:

  • Starting to write code → decisions.md + project-context.md
  • Debugging a bug → lessons.md
  • User-facing task → people.md
  • Env/setup work → tools-config.md

Load-and-release: read the file, use it, do NOT carry it into subsequent turns.

Level 3 — On-Demand Search(按需检索)

  • Use memory_search (qmd or equivalent) for archive/ files
  • For cross-day event lookup without loading full journals
  • Never load archive files entirely unless specifically needed
Detailed protocol: references/mount-strategy.md

INDEX.md — Memory Index(索引文件)

INDEX.md is the single source of truth for what memory exists. Rebuild it after every Deep Sleep or REM cycle.

# Memory Index
> Last rebuilt: YYYY-MM-DD | Total files: N | Budget: 500 lines per category

## Hot Files (load on session start)
| File | Lines | Last Updated | Recent Entries |
|------|-------|-------------|----------------|
| decisions.md | 45 | 2026-04-06 | Redis缓存策略, API版本v2 |
| project-context.md | 80 | 2026-04-07 | QMS技术栈, 数据库schema |

## On-Demand Files (load when relevant)
| File | Lines | Last Updated | Search Hint |
|------|-------|-------------|-------------|
| lessons.md | 120 | 2026-04-05 | bug根因, workaround, 踩坑 |
| people.md | 30 | 2026-03-28 | 人物偏好, 角色 |
| tools-config.md | 25 | 2026-04-03 | 命令备忘, 环境配置 |

## Daily Journals
| Date | Exists | Key Events |
|------|--------|------------|
| 2026-04-07 | ✅ | cron修复, Dreaming skill创建 |
| 2026-04-06 | ✅ | PraisonAI研究, GitHub同步 |

Session Lifecycle Integration

On Wake (新会话启动)

1. Read memory/INDEX.md
2. Read today + yesterday journals
3. Resume work

Before Context Loss (上下文即将丢失)

→ Dump to today's journal immediately: task state, in-flight decisions, open files, errors, next step.

Quick Reference

WhenActionWrite to
Decision madeWrite immediatelymemory/YYYY-MM-DD.md
Session endingDump current statememory/YYYY-MM-DD.md
New sessionRead INDEX + 2 journalsL1 mount
Every 3 daysPromote + route + rebuild INDEXDeep Sleep
WeeklyFitness eval + archive + supersedeREM
Task touches domainLoad relevant fileL2 mount

References

  • references/memory-lifecycle.md — 三阶段操作手册 + 文件分类规范
  • references/anti-amnesia-checklist.md — 防失忆检查清单 + INDEX 维护
  • references/mount-strategy.md — 挂载策略详细操作手册
  • references/survival-merge.md — 优胜劣汰融合完整协议

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

82.47%
按下载量换算593

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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