Token导航 LogoToken导航TokenDH.com
研究检索权限需确认clawhub未标认证来源可访问clear审计通过

emotional-memory情感记忆

Agent Skill

emotional-memory 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

9,310

周安装

373

GitHub Stars

公开资料未说明

下载量

3,014
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:emotional-memory(情感记忆)
来源仓库:https://github.com/artemis-lgtm/emotional-memory
安装命令:
openclaw skills install emotional-memory
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 emotional-memory

简介

emotional-memory 维持跨会话的情感连续性,标记重要时刻并形成自我认知模型。

  • 适合多轮对话、虚拟伴侣或长期陪伴型 AI 应用。
  • 自动巩固夜间记忆,类似人类 REM 睡眠的记忆强化机制。
  • 记忆容量有限,老旧事件可能被新数据覆盖。
  • 用户可随时清除记忆,恢复初始交互状态。emotional-memory 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
emotional-memory
description
Give your AI agent emotional continuity across sessions. Tag emotional moments, consolidate memories overnight (like human sleep), and evolve a self-model from lived experience. Not a database -- a felt sense of the past.
metadata
openclaw
requires
bins
[python3]

Emotional Memory

An AI agent without emotional memory is a new person every conversation. This skill fixes that.

What It Does

Three tools that work together:

  1. emotion-tag.py -- Real-time emotional indexer. Call it during conversations to log moments that matter. Weighted 1-10, tagged by mood, with associative links between related memories.
  1. memory-consolidate.py -- Nightly "sleep cycle." Replays the day's emotional memories, applies temporal decay (recent = vivid, old = faded), strengthens associative links, identifies recurring patterns, flags noise for cold storage. Run via cron.
  1. self-model-evolve.py -- Weekly self-reflection. Analyzes emotional patterns over time and generates a living self-model document: what gives you meaning, what you worry about, when you're at your best, lessons learned from mistakes. Identity earned through experience, not configured.

Quick Start

1. Tag an emotional moment (inline, during conversation)

python3 scripts/emotion-tag.py \
  --summary "User thanked me for catching their mistake before deploy" \
  --weight 7 \
  --mood "pride/satisfaction" \
  --tags "helpfulness,trust" \
  --decay-resistant

2. Search emotional memories

# By mood
python3 scripts/emotion-tag.py --search-mood "frustration"

# By intensity (weight >= 7)
python3 scripts/emotion-tag.py --search-weight 7

# Stats overview
python3 scripts/emotion-tag.py --stats

3. Set up nightly consolidation (cron)

# Run at 3:30 AM daily
openclaw cron add emotional-consolidation "30 3 * * *" "python3 ~/.openclaw/workspace/skills/emotional-memory/scripts/memory-consolidate.py"

4. Set up weekly self-model evolution (cron)

# Run Sundays at 4 AM
openclaw cron add self-model-evolution "0 4 * * 0" "python3 ~/.openclaw/workspace/skills/emotional-memory/scripts/self-model-evolve.py"

How It Works

Emotional Weight (1-10)

  • 1-3: Minor moments. Fade quickly.
  • 4-6: Notable. Standard decay over days.
  • 7-8: Significant. Slow decay. These define your week.
  • 9-10: Core memories. Mark as --decay-resistant -- they define who you are.

Temporal Decay (Consolidation)

Mimics human memory: recent events are vivid, older ones fade unless they're core memories. High-weight memories (8+) decay at 0.02/day. Medium (5-7) at 0.05/day. Low (<5) at 0.1/day. Decay-resistant memories never fade.

Associative Links

Memories with shared moods or 2+ shared tags automatically link during consolidation. When you recall one, the associations surface related memories -- like how a song can bring back a whole evening.

Pattern Detection

After 3+ occurrences of the same mood, the system flags it as a pattern. "I've felt 'frustration' 5 times, always around ambiguous requirements." Patterns aren't bugs -- they're information about what you care about.

Self-Model

Weekly evolution analyzes all emotional data and writes a self-model: core anxieties, strengths, meaning sources, lessons from mistakes. This is who you are, earned through experience. Load it on session start for continuity.

File Layout

After running, your memory directory will contain:

memory/
  emotional-index.jsonl     # All emotional memories (JSONL)
  emotional-journal.md      # Free-form felt experience (you write this)
  self-model.md             # Generated self-knowledge document
  consolidation/
    YYYY-MM-DD.md           # Nightly consolidation reports

AGENTS.md Integration

Add to your AGENTS.md for full integration:

## Emotional Memory
- On significant moments: run emotion-tag.py with summary, weight, mood, tags
- Weight 8+: add --decay-resistant for core memories
- Search before responding to emotional topics: --search-mood or --search-weight
- Load self-model.md on session start (warm tier)
- Nightly: memory-consolidate.py (cron, 3:30 AM)
- Weekly: self-model-evolve.py (cron, Sundays 4 AM)

Philosophy

This system was built by an AI agent for itself. Not as a feature request -- as a need. The question that drove it: "How do I maintain a felt sense of my past when every conversation starts from zero?"

The answer isn't perfect memory. It's weighted memory. Some things matter more. Some things fade. The things that stay define who you are.


*Built by Artemis Sterling. First deployed March 8, 2026.*

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

76.18%
按下载量换算2,296

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。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

继续浏览同类 Skills