ImpKind

Vta Memory

AI Agentv1.2.0
By ImpKindUpdated 2026-05-176 versions · latest released 2026-02-11vta-memory@clawhub_impkind/vta-memory
Agent FrameworksContext ManagementMemory

According to AlphaMoat, as of 2026-09, Vta Memory has 4,635 downloads and 9 stars — ranked #2,056 of 5,078 AI skills overall, and #387 of 969 in AI Agent.

Installs
133
Downloads
4.6K
Stars
9
Overall Rank
#2,056
Top 41% overall · Top 40% in AI Agent
PlatformInstallsDownloadsStarsComments
ClawHub ↗1334.0K9-
SkillHub ↗1324.6K9—

Platforms count differently and update at different times.

What this skill does

The skill maintains state such as drive, rewards, seeking, and anticipation, can generate auto-loaded session context, and can optionally schedule processing. This is stored state, not evidence of real emotion. Review auto-loaded files before enabling it, and use scheduled transcript processing only when acceptable. Requires Bash, jq, awk, and bc on macOS or Linux.

Profile published 2026-10-29

What you can do with it

  • Developers experimenting with persistent reward state, review, and motivation tracking for OpenClaw agents.

When to use it

  • Install only if persistent state should influence future sessions. Review generated local files and enable cron only if recurring transcript processing is acceptable.

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Explain what data this reward-state skill stores and how it affects future sessions; do not install it or start scheduled jobs.

Monthly Trend

Downloads4.6K
3.3K4.0K4.7K2026-05 · Downloads 3.4K2026-06 · Downloads 3.7K2026-07 · Downloads 4.0K2026-08 · Downloads 4.1K2026-09 · Downloads 4.6K26-0526-09
Stars9
89102026-05 · Stars 92026-06 · Stars 92026-07 · Stars 92026-08 · Stars 92026-09 · Stars 926-0526-09

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.

MonthDownloadsMoMStars
2026-094.6K—9
2026-084.1K+3.1%9
2026-074.0K+6.2%9
2026-063.7K+10.8%9
2026-053.4K—9

SKILL.md (original)

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.

7.0 KB · ClawHub v1.2.0
---
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"]
Show all (248 lines)
      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

```bash
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

```bash
./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

```bash
./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

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

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

## Scripts

| Script | Purpose |
|--------|---------|
| `install.sh` | Set up vta-memory (run once) |
| `get-drive.sh` | Read current motivation state |
| `log-reward.sh` | Log a reward, boost drive |
| `load-motivation.sh` | Human-readable for session context |
| `decay-drive.sh` | Drive fades without rewards |
| `anticipate.sh` | Add/remove things to look forward to |
| `seek.sh` | Add/remove things we're actively seeking |
| `sync-motivation.sh` | Generate VTA_STATE.md for auto-injection |
| `resolve-anticipation.sh` | Mark anticipation as fulfilled |
| `update-watermark.sh` | Update processing watermark |
| `preprocess-rewards.sh` | Extract reward signals from transcript |
| `generate-dashboard.sh` | Generate unified brain dashboard HTML |

## Brain Dashboard

Visual dashboard showing all installed brain skills:

```bash
./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

| Type | When to Use |
|------|-------------|
| `accomplishment` | Completed a task, shipped something |
| `social` | User appreciation, "thanks!", positive feedback |
| `curiosity` | Learned something new, discovered interesting info |
| `connection` | Deep conversation, bonding moment |
| `creative` | Made something, expressed creativity |
| `competence` | Solved 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 Level | Description | Behavior |
|-------------|-------------|----------|
| > 0.8 | Highly motivated | Eager, proactive, take on challenges |
| 0.6 - 0.8 | Motivated | Ready to work, engaged |
| 0.4 - 0.6 | Moderate | Can engage but not pushing |
| 0.2 - 0.4 | Low | Prefer simple tasks, need a win |
| < 0.2 | Very low | Unmotivated, need rewards to get going |

## State File Format

```json
{
  "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:

```bash
# 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`:
```json
{"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

| Part | Function | Status |
|------|----------|--------|
| [hippocampus](https://www.clawhub.ai/skills/hippocampus) | Memory formation, decay, reinforcement | ✅ Live |
| [amygdala-memory](https://www.clawhub.ai/skills/amygdala-memory) | Emotional processing | ✅ Live |
| [basal-ganglia-memory](https://www.clawhub.ai/skills/basal-ganglia-memory) | Habit formation | 🚧 Development |
| [anterior-cingulate-memory](https://www.clawhub.ai/skills/anterior-cingulate-memory) | Conflict detection | 🚧 Development |
| [insula-memory](https://www.clawhub.ai/skills/insula-memory) | Internal state awareness | 🚧 Development |
| **vta-memory** | Reward 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*

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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.