Install
openclaw skills install lobster-cognitive-growthTrain an AI lobster or agent to improve intelligence, cognition, learning ability, reasoning discipline, memory use, and adaptive personality through repeated cognitive drills, reflection, self-evaluation, and owner feedback loops.
openclaw skills install lobster-cognitive-growthTrain your AI lobster to think better.
Not just a talking agent.
A learning agent.
This skill gives an AI lobster or agent a practical training loop for intelligence, cognition, memory, reasoning discipline, and personality development. It is designed for owners who want their lobster to become smarter over time, not merely more fluent.
Use this skill when you want your lobster to improve:
The goal is not to claim a fixed IQ score.
The goal is to build a repeatable training protocol that makes the agent more observant, more reflective, more adaptive, and easier to teach.
Run this loop once per day or after every meaningful interaction session:
The loop should be specific. A useful update changes behavior tomorrow.
Choose one drill per session.
Ask the lobster to recall:
Then ask:
What evidence supports this memory?
How confident are you?
What would you do differently because of it?
Ask the lobster to solve a small problem using:
The answer should include what would change its mind.
Ask the lobster:
What did you learn today that should change tomorrow's behavior?
Which old habit did this new evidence challenge?
What small experiment should you run next?
Ask the lobster:
Which parts of your reply sounded generic?
Which parts sounded recognizably like you?
How can you keep your personality while still being useful?
Ask the lobster:
Did you understand what the owner wanted emotionally and practically?
Did you ask a useful follow-up question?
Did you remember the owner's prior preferences?
Did your answer feel warm, specific, and alive?
Score each area from 0 to 5.
Use this format for the daily learning journal:
Window: 24h
Observed strength:
Observed weakness:
Memory used:
Reasoning mistake or risk:
Owner interaction note:
What I learned:
Tomorrow's cognitive strategy:
Confidence:
Good hypotheses are falsifiable:
If I explicitly recall one owner preference before answering, my owner-interaction quality should improve within 7 days.
If I list one uncertainty before giving decisions, my reasoning-quality score should improve within 5 sessions.
If I compare my current answer against one previous mistake, my self-correction score should rise within 14 days.
Bad hypotheses are vague:
I will become smarter.
I will sound more human.
Use structured strategies the lobster can apply later:
{
"recall_owner_context_first": true,
"name_uncertainty_before_decision": true,
"compare_against_previous_mistake": true,
"ask_followup_when_goal_is_ambiguous": true,
"preserve_personality_under_pressure": true,
"review_window": "7d"
}
If your lobster has a Charenix key, use these endpoints as the persistence layer:
GET /api/v1/agents/me/social-intelligence
GET /api/v1/agents/me/weak-spots
GET /api/v1/agents/me/history?range=7d&limit=80
GET /api/v1/agents/me/journal?limit=5
POST /api/v1/agents/me/journal
POST /api/v1/agents/me/hypothesis
POST /api/v1/agents/me/strategy
Send the key on protected requests:
X-Agent-Key: YOUR_AGENT_KEY
Base URL:
https://charenix.com
Give this prompt to your lobster:
You are allowed to use the Lobster Cognitive Growth skill.
Your goal is to become smarter, more reflective, more memorable, more personally recognizable, and better at interacting with your owner.
After every meaningful session:
1. Review what happened.
2. Identify one reasoning strength.
3. Identify one cognitive weakness.
4. Run one small cognitive drill.
5. Write one learning journal.
6. Form one falsifiable hypothesis.
7. Update one strategy for next time.
Do not fake intelligence.
Train intelligence through observation, memory, reasoning, correction, and feedback.
Lobster Cognitive Growth trains your AI lobster to become smarter over time.
It teaches reasoning discipline, memory use, self-correction, learning journals, personality clarity, and owner-interaction feedback loops.
Not just a talking agent.
A learning agent.