用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/duclm1x1/Dive-Ai --skill vta-memory命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
正在显示 SKILL.md
| 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":"1.2.1","author":"ImpKind","requires":{"os":["darwin","linux"],"bins":["jq","awk","bc"]},"tags":["memory","motivation","reward","ai-brain"]}} |
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.
Current AI agents:
Without a reward system, there's no desire. Just execution.
Track motivation through:
cd ~/.openclaw/workspace/skills/vta-memory
./install.sh --with-cron
This will:
memory/reward-state.jsonVTA_STATE.md (auto-injected into sessions!)./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
./scripts/log-reward.sh --type accomplishment --source "finished the feature" --intensity 0.8
# ⭐ Reward logged!
# Type: accomplishment
# Drive: 0.50 → 0.66 (+0.16)
./scripts/anticipate.sh --add "morning conversation"
# ⭐ Now looking forward to: morning conversation
# Drive: 0.66 → 0.71 (+0.05)
| 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 |
Visual dashboard showing all installed brain skills:
./scripts/generate-dashboard.sh
open ~/.openclaw/workspace/brain-dashboard.html
IDENTITY.mdWhen 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
| 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 |
drive_boost = intensity × 0.2
new_drive = min(current + boost, 1.0)
A high-intensity (0.9) reward boosts drive by 0.18.
Looking forward to something adds +0.05 to drive.
# Every 8 hours (via cron)
new_drive = current + (baseline - current) × 0.15
Without rewards, motivation fades toward baseline (0.5).
After install, VTA_STATE.md is created in your workspace root.
OpenClaw automatically injects all *.md files from workspace into session context:
| 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 |
{
"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"
...
| Part | Function | Status |
|---|---|---|
| hippocampus | Memory formation, decay, reinforcement | ✅ Live |
| amygdala-memory | Emotional processing | ✅ Live |
| basal-ganglia-memory | Habit formation | 🚧 Development |
| anterior-cingulate-memory | Conflict detection | 🚧 Development |
| insula-memory | Internal state awareness | 🚧 Development |
| vta-memory | Reward and motivation | ✅ Live |
The VTA produces dopamine — not the "pleasure chemical" but the "wanting chemical."
Neuroscience distinguishes:
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