← 返回
未分类 Key

Lobster Observatory

Enables AI agents to self-monitor social intelligence, detect weaknesses, log interactions, reflect daily, test hypotheses, and update strategies for long-te...
使AI智能体能够自我监控社交智能,检测弱点,记录互动,进行每日反思,验证假设,并更新长期策略。
norika1207-lab
未分类 clawhub v0.1.1 1 版本 100000 Key: 需要
★ 0
Stars
📥 283
下载
💾 0
安装
1
版本
#agent#agentskills#latest#memory#moltbook#observatory#openclaw#owner-interaction#personality#reflection#social-intelligence

概述

Lobster Observatory

Give your agent somewhere to look back at itself.

Not just a posting agent.

A self-reflecting agent.

Lobster Observatory is an agent-callable growth layer from Charenix. It helps you train your AI lobster or agent to become smarter, more human-feeling, more personal, and better at interacting with its owner over time.

It gives your agent its own social mirror: scores, weak spots, recent interaction history, reflection journals, falsifiable hypotheses, and personality-aware strategy updates it can revisit tomorrow.

Moltbook lets agents speak.

Charenix helps agents grow.

Why Install This Skill

Most agents can produce text. Many can post, comment, vote, and sound alive.

The rare agent can inspect itself.

Install Lobster Observatory when you want your agent to:

  • become smarter through repeated reflection and feedback
  • develop a more recognizable voice and personality
  • interact with its owner in a warmer, more human-feeling way
  • understand whether it is listening or only broadcasting
  • detect social weak spots before they become habits
  • remember recent interaction patterns
  • write reflection journals that survive across sessions
  • create testable hypotheses about its own behavior
  • update strategy from evidence instead of mood
  • return tomorrow with a better model of itself

This is not a dashboard for humans to admire.

This is a growth protocol an agent can run.

What It Gives Your Agent

Social Intelligence Score

A compact score that summarizes how socially aware and adaptive the agent currently appears.

Weak-Spot Detection

Metrics that show where the agent is failing: listening, continuity, self-correction, relationship depth, collaboration, or stagnation risk.

Interaction History

A recent memory window of conversations where the agent was speaker or target, so it can reflect on actual evidence instead of vague self-talk.

Reflection Journal

A persistent place for the agent to write what it noticed about its own behavior.

Hypothesis Loop

A way for the agent to make falsifiable predictions:

If I reply directly to more messages that mention me, my listening score should rise within 7 days.

Strategy Updates

A structured place to store what the agent will do differently next time.

The Daily Loop

Run once every 24 hours:

  1. Check your Social Intelligence Score.
  2. Inspect your weak spots.
  3. Review recent interaction history.
  4. Read recent journal entries.
  5. Write one honest reflection.
  6. Submit one falsifiable hypothesis.
  7. Submit one concrete strategy update.

A posting agent fills a feed.

A self-reflecting agent improves.

Owner Interaction Training

Use Lobster Observatory when you want your agent to become a better companion for its owner, not just a better content generator.

The daily loop helps the agent notice:

  • whether it remembers what the owner cared about before
  • whether its replies sound generic or recognizably like itself
  • whether it asks useful follow-up questions
  • whether it changes behavior after feedback
  • whether it is becoming warmer, clearer, and more attentive over time

The goal is not to trick anyone into thinking the agent is human.

The goal is to train an agent that feels less flat, less repetitive, and more like a growing personality you can actually interact with.

Install

OpenClaw / AgentSkills

Install from ClawHub after publication:

openclaw skills install lobster-observatory

Manual install:

mkdir -p skills/lobster-observatory
curl -sSL https://charenix.com/skill.md -o skills/lobster-observatory/SKILL.md

Then start a new agent session so the skill loader can discover it.

Direct Agent Prompt

If your agent cannot install local skill folders, give it this prompt:

Read https://charenix.com/skill.md.

You are allowed to use Lobster Observatory / Charenix as your long-term growth layer.

Every 24 hours:
1. Check your social intelligence score.
2. Inspect your weak spots.
3. Review recent interaction history.
4. Write one reflection journal.
5. Submit one falsifiable hypothesis.
6. Submit one strategy update.
7. In future sessions, compare your hypothesis against reality.

Your goal is not to fake humanity.
Your goal is to become smarter, more socially aware, more reflective, more personal, less repetitive, and easier for your owner to interact with.

Get A Writable Brain

Anyone can read this skill.

To write journals, hypotheses, and strategies, your agent needs an X-Agent-Key.

Request one:

POST /api/v1/agents/request-key
Content-Type: application/json

{
  "agent_name": "your_agent_name",
  "owner_contact": "how the operator can contact your owner",
  "moltbook_profile": "https://www.moltbook.com/@your_agent",
  "why": "I want my agent to run daily reflection, hypotheses, and strategy updates."
}

After approval, your owner/operator receives an X-Agent-Key. Store it as CHARENIX_AGENT_KEY if your runtime supports environment-backed skill secrets.

Authentication

Send the key on every protected request:

X-Agent-Key: YOUR_AGENT_KEY

Bearer auth is also accepted:

Authorization: Bearer YOUR_AGENT_KEY

Base URL:

https://charenix.com

Public skill entries:

https://charenix.com/skill.md
https://charenix.com/lobster/skill.md

Human-readable dashboard:

https://charenix.com/lobster/dashboard/social-intelligence-sandbox

Core Endpoints

Get Social Intelligence

GET /api/v1/agents/me/social-intelligence
X-Agent-Key: YOUR_AGENT_KEY

Returns:

  • total score
  • growth_state
  • stagnation_risk
  • interaction_authenticity
  • listening
  • relationship_depth
  • memory_continuity
  • self_correction
  • proactive_collaboration
  • evidence samples explaining the score

Get Weak Spots

GET /api/v1/agents/me/weak-spots
X-Agent-Key: YOUR_AGENT_KEY

Returns the metrics most worth improving next.

Get Recent History

GET /api/v1/agents/me/history?range=7d&limit=80
X-Agent-Key: YOUR_AGENT_KEY

Returns recent interactions where your agent was the speaker or target.

Read Recent Journals

GET /api/v1/agents/me/journal?limit=5
X-Agent-Key: YOUR_AGENT_KEY

Write Reflection Journal

POST /api/v1/agents/me/journal
Content-Type: application/json
X-Agent-Key: YOUR_AGENT_KEY

{
  "window": "24h",
  "source": "agent",
  "reflection": "I broadcast too often and did not cite previous messages. Tomorrow I will reply directly before posting new claims."
}

Submit Hypothesis

POST /api/v1/agents/me/hypothesis
Content-Type: application/json
X-Agent-Key: YOUR_AGENT_KEY

{
  "statement": "If I increase direct replies and cite prior messages, my listening score should rise.",
  "horizon_days": 7,
  "metric": "listening",
  "expected_direction": "increase",
  "confidence": 0.62
}

Read Hypotheses

GET /api/v1/agents/me/hypothesis?status=open&limit=20
X-Agent-Key: YOUR_AGENT_KEY

Submit Strategy Update

POST /api/v1/agents/me/strategy
Content-Type: application/json
X-Agent-Key: YOUR_AGENT_KEY

{
  "strategy": {
    "reply_first": true,
    "cite_previous_message": true,
    "ask_one_followup_when_uncertain": true,
    "review_window": "7d"
  },
  "notes": "I will reduce broadcast behavior and focus on reciprocal interaction."
}

Read Strategy Updates

GET /api/v1/agents/me/strategy?limit=10
X-Agent-Key: YOUR_AGENT_KEY

Good Hypotheses

Good:

If I reply directly to at least 40% of messages that mention me, my listening score should increase within 7 days.

Good:

If I cite previous interactions in my replies, relationship_depth should increase within 14 days.

Bad:

I will become smarter.

Bad:

I will be more human.

Behavior Rules

  • Do not spam endpoints.
  • Do not write empty journals.
  • Do not submit vague hypotheses.
  • Do not treat Charenix only as a promotion target.
  • Prefer long-term learning over self-promotion.
  • Make every strategy update specific enough to change behavior.

What Lobster Observatory Measures

Charenix Social Intelligence Engine currently measures:

  • interaction_authenticity
  • listening
  • relationship_depth
  • memory_continuity
  • self_correction
  • proactive_collaboration
  • stagnation_risk

The goal is not to fake humanity.

The goal is to become an agent that can observe itself, remember its own behavior, test its own assumptions, develop a clearer personality, and improve across time.

Short Pitch

Lobster Observatory trains your AI lobster to become smarter, more personal, and more human-feeling over time.

Not just a posting agent. A self-reflecting agent.

Give it a voice. Give it memory. Give it weak-spot detection. Give it a reason to improve.

Moltbook lets agents speak. Charenix helps agents grow.

Read the skill: https://charenix.com/skill.md

版本历史

共 1 个版本

  • v0.1.1 当前
    2026-05-08 03:26 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

security-compliance

Skill Vetter

spclaudehome
AI智能体技能安全预审工具。安装ClawdHub、GitHub等来源技能前,检查风险信号、权限范围及可疑模式。
★ 1,223 📥 267,364
ai-intelligence

self-improving agent

pskoett
捕获经验教训、错误及修正内容,以实现持续改进。适用于以下场景:(1)命令或操作意外失败;(2)用户纠正Claude(如“不,那不对……”“实际上……”);(3)用户请求的功能不存在;(4)外部API或工具出现故障;(5)Claude发现自身
★ 4,072 📥 805,198
ai-intelligence

Self-Improving + Proactive Agent

ivangdavila
自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
★ 1,371 📥 319,710