vta-memory

Reward and motivation system for AI agents. Dopamine-like wanting, not just doing. Part of the AI Brain series.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "vta-memory" with this command: npx skills add ImpKind/vta-memory

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

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Automation

Xiaohongshu Ops

小红书端到端运营:账号定位、选题研究、内容生产、发布执行、数据复盘。 Use when: (1) 用户要写小红书笔记/帖子, (2) 用户说"发小红书"/"写个种草文"/"出一篇小红书", (3) 用户讨论小红书选题/热点/爆款分析/竞品对标, (4) 用户提到账号定位/人设/内容方向规划, (5) 用户要求生成...

Registry SourceRecently Updated
Automation

WeMP Ops

微信公众号全流程运营:选题→采集→写作→排版→发布→数据分析→评论管理。 Use when: (1) 用户要写公众号文章或提供了选题方向, (2) 用户说"写一篇关于XXX的文章"/"帮我写篇推文"/"出一篇稿子", (3) 用户要求采集热点/素材/竞品分析, (4) 用户提到公众号日报/周报/数据分析/阅读量/...

Registry SourceRecently Updated
Automation

agent-stock

用于股票行情查询与分析的命令行技能。用户提到 stock 命令、股票代码、最新资讯、市场概览、K 线或配置管理时调用。

Registry SourceRecently Updated