ImpKind

Vta Memory

AIエージェントv1.2.0
作者:ImpKind更新日 2026-05-17全 6 バージョン · 最新版の公開日 2026-02-11vta-memory@clawhub_impkind/vta-memory
エージェントフレームワークコンテキスト管理メモリ強化

AlphaMoatによると、2026-09時点でVta Memoryのダウンロード数は4,635、スター数は9で、AIスキル全体5,078件中2,056位、AIエージェントでは969件中387位です。

インストール数
133
ダウンロード数
4635
スター数
9
総合順位
#2,056
全体の上位 41% · AIエージェントの上位 40%
プラットフォームインストール数ダウンロード数スター数コメント
ClawHub ↗13340479-
SkillHub ↗13246359—

プラットフォームごとに集計方法と集計時点が異なります。

このスキルでできること

drive、報酬、求めるもの、期待などの状態を保持し、自動読み込みされるセッション文脈を生成できます。定期処理も設定できます。これは保存された状態であり、実際の感情を示すものではありません。自動読み込みファイルを確認し、会話記録の定期処理を許容できる場合にのみ cron を有効にしてください。 macOS または Linux で Bash、jq、awk、bc が必要です。

資料公開日 2026-10-29

できること

  • OpenClaw エージェントに永続的な報酬状態、振り返り、動機追跡を試験的に追加したい開発者向けです。

使う場面

  • 永続状態を後続セッションに反映させたい場合にのみ導入してください。生成されたローカルファイルを確認し、会話記録の定期処理を許容できる場合に限り cron を有効にしてください。

プロンプト例

スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。

この報酬状態スキルが保存するデータと、後続セッションへの影響を説明してください。インストールや定期ジョブの開始はしないでください。

月次の推移

ダウンロード数4635
3273400547362026-05 · ダウンロード数 33742026-06 · ダウンロード数 37372026-07 · ダウンロード数 39682026-08 · ダウンロード数 40912026-09 · ダウンロード数 463526-0526-09
スター数9
89102026-05 · スター数 92026-06 · スター数 92026-07 · スター数 92026-08 · スター数 92026-09 · スター数 926-0526-09

2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。

月ダウンロード数前月比スター数
2026-094635—9
2026-084091+3.1%9
2026-073968+6.2%9
2026-063737+10.8%9
2026-053374—9

SKILL.md 原文

作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 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"]
すべて表示(全 248 行)
      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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ImpKindのその他のスキル

データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。