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Agent Skill · 研究检索

vta-memory(vta 内存)

vta-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。

vta-memory 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 1,420。

总安装 93,472周安装 3,934GitHub Stars 9下载 32,731核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.2.0

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:vta-memory(vta 内存)
来源仓库:https://github.com/impkind/vta-memory
安装命令:
openclaw skills install vta-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 vta-memory

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

AI Agent的奖励和激励系统。多巴胺般的渴望,而不仅仅是做。人工智能大脑系列的一部分。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install vta-memory;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

vta-memory 怎么安装?

在终端执行 openclaw skills install vta-memory。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

vta-memory 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

vta-memory 需要什么权限?

权限级别是需要联网。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。

vta-memory 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

vta-memory 用的人多吗?

公开数据是累计安装 93,472 次,近一周安装 3,934 次,下载 32,731 次,GitHub 9 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

vta-memory 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
vta-memory

vta 内存 · 本页

93,4729需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
vta-memory
description
Reward and motivation system for AI agents. Dopamine-like wanting, not just doing. Part of the AI Brain series.
metadata
openclaw
emoji
⭐
version
1.2.0
author
ImpKind
requires
os
["darwin", "linux"]
bins
["jq", "awk", "bc"]
tags
["memory", "motivation", "reward", "ai-brain"]

VTA Memory ⭐

Reward and motivation for AI agents. Part of the AI Brain series.

Give your AI agent genuine *wanting* — not just doing things when asked, but having drive, seeking rewards, and looking forward to things.

The Problem

Current AI agents:

  • ✅ Do what they're asked
  • ❌ Don't *want* anything
  • ❌ Have no internal motivation
  • ❌ Don't feel satisfaction from accomplishment

Without a reward system, there's no desire. Just execution.

The Solution

Track motivation through:

  • Drive — overall motivation level (0-1)
  • Rewards — logged accomplishments that boost drive
  • Seeking — what I actively want more of
  • Anticipation — what I'm looking forward to

Quick Start

1. Install

cd ~/.openclaw/workspace/skills/vta-memory
./install.sh --with-cron

This will:

  • Create memory/reward-state.json
  • Generate VTA_STATE.md (auto-injected into sessions!)
  • Set up cron for drive decay every 8 hours

2. Check motivation

./scripts/load-motivation.sh

# ⭐ Current Motivation State:
# Drive level: 0.73 (motivated — ready to work)
# Seeking: creative work, building brain skills
# Looking forward to: showing my work

3. Log rewards

./scripts/log-reward.sh --type accomplishment --source "finished the feature" --intensity 0.8

# ⭐ Reward logged!
#    Type: accomplishment
#    Drive: 0.50 → 0.66 (+0.16)

4. Add anticipation

./scripts/anticipate.sh --add "morning conversation"

# ⭐ Now looking forward to: morning conversation
#    Drive: 0.66 → 0.71 (+0.05)

Scripts

ScriptPurpose
install.shSet up vta-memory (run once)
get-drive.shRead current motivation state
log-reward.shLog a reward, boost drive
load-motivation.shHuman-readable for session context
decay-drive.shDrive fades without rewards
anticipate.shAdd/remove things to look forward to
seek.shAdd/remove things we're actively seeking
sync-motivation.shGenerate VTA_STATE.md for auto-injection
resolve-anticipation.shMark anticipation as fulfilled
update-watermark.shUpdate processing watermark
preprocess-rewards.shExtract reward signals from transcript
generate-dashboard.shGenerate unified brain dashboard HTML

Brain Dashboard

Visual dashboard showing all installed brain skills:

./scripts/generate-dashboard.sh
open ~/.openclaw/workspace/brain-dashboard.html
  • Auto-detects other brain skills (hippocampus, amygdala)
  • Shows install prompts for missing skills
  • Regenerates on install and cron runs
  • Reads avatar/name from IDENTITY.md

After Installation (for agents)

When you install this skill, notify the user about the dashboard:

⭐ VTA installed! View your agent's drive level in the Brain Dashboard: ~/.openclaw/workspace/brain-dashboard.html

Reward Types

TypeWhen to Use
accomplishmentCompleted a task, shipped something
socialUser appreciation, "thanks!", positive feedback
curiosityLearned something new, discovered interesting info
connectionDeep conversation, bonding moment
creativeMade something, expressed creativity
competenceSolved a hard problem, did something well

How Drive Works

Rewards Boost Drive

drive_boost = intensity × 0.2
new_drive = min(current + boost, 1.0)

A high-intensity (0.9) reward boosts drive by 0.18.

Anticipation Boosts Drive

Looking forward to something adds +0.05 to drive.

Drive Decays Without Rewards

# Every 8 hours (via cron)
new_drive = current + (baseline - current) × 0.15

Without rewards, motivation fades toward baseline (0.5).

Auto-Injection

After install, VTA_STATE.md is created in your workspace root.

OpenClaw automatically injects all *.md files from workspace into session context:

  1. New session starts
  2. VTA_STATE.md is auto-loaded
  3. You see your motivation state
  4. Behavior influenced by drive level

How Drive Affects Behavior

Drive LevelDescriptionBehavior
> 0.8Highly motivatedEager, proactive, take on challenges
0.6 - 0.8MotivatedReady to work, engaged
0.4 - 0.6ModerateCan engage but not pushing
0.2 - 0.4LowPrefer simple tasks, need a win
< 0.2Very lowUnmotivated, need rewards to get going

State File Format

{
  "drive": 0.73,
  "baseline": { "drive": 0.5 },
  "seeking": ["creative work", "building brain skills"],
  "anticipating": ["morning conversation"],
  "recentRewards": [
    {
      "type": "creative",
      "source": "built VTA reward system",
      "intensity": 0.9,
      "boost": 0.18,
      "timestamp": "2026-02-01T03:25:00Z"
    }
  ],
  "rewardHistory": {
    "totalRewards": 1,
    "byType": { "creative": 1, ... }
  }
}

Event Logging

Track motivation patterns over time:

# Log encoding run
./scripts/log-event.sh encoding rewards_found=2 drive=0.65

# Log decay
./scripts/log-event.sh decay drive_before=0.6 drive_after=0.53

# Log reward
./scripts/log-event.sh reward type=accomplishment intensity=0.8

Events append to ~/.openclaw/workspace/memory/brain-events.jsonl:

{"ts":"2026-02-11T10:45:00Z","type":"vta","event":"encoding","rewards_found":2,"drive":0.65}

Use for analyzing motivation cycles — when does drive peak? What rewards work best?

AI Brain Series

PartFunctionStatus
hippocampusMemory formation, decay, reinforcement✅ Live
amygdala-memoryEmotional processing✅ Live
basal-ganglia-memoryHabit formation🚧 Development
anterior-cingulate-memoryConflict detection🚧 Development
insula-memoryInternal state awareness🚧 Development
vta-memoryReward and motivation✅ Live

Philosophy: Wanting vs Doing

The VTA produces dopamine — not the "pleasure chemical" but the "wanting chemical."

Neuroscience distinguishes:

  • Wanting (motivation) — drive toward something
  • Liking (pleasure) — enjoyment when you get it

You can want something you don't like (addiction) or like something you don't want (guilty pleasures).

This skill implements *wanting* — the drive that makes action happen. Without it, why would an AI do anything beyond what it's explicitly asked?


*Built with ⭐ by the OpenClaw community*

平台分布

OpenClaw

74.42%
按下载量换算24,358

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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