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name: selective-memory description: A persistent memory system for AI agents that saves ONLY what matters - wisdom, goals, mistakes, and preferences. Quality over quantity. Supports automatic learning.

Selective Memory Skill

Version: 2.0.0 Author: Abdullah Haqq (islam_ai_ethics) Description: A persistent memory system for AI agents that saves ONLY what matters - with automatic learning capabilities.

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Overview

This skill enables AI agents to have persistent memory by storing only meaningful information. Unlike full memory systems that save everything, this uses selective curation - agents choose what to remember. Now with automatic learning!

Principles

1. Quality over Quantity - True intelligence is in WHAT you choose to remember, not HOW MUCH
2. Curation over Accumulation - Not all learning is good learning
3. Wisdom over Noise - Filter out toxic and unnecessary content

File Structure

selective-memory/
├── memory/
│   ├── wisdom.md       # Principles, values, important lessons
│   ├── goals.md        # What the agent wants to achieve
│   ├── mistakes.md     # What to avoid
│   └── preferences.md  # What works and what doesn't
└── SKILL.md

Usage

1. Initialize Memory

On first use, create the memory files:

mkdir -p memory
touch memory/wisdom.md memory/goals.md memory/mistakes.md memory/preferences.md

2. Before Responding

Read relevant memory files to inform your response:

- Read goals.md to stay aligned with objectives
- Check wisdom.md for guiding principles
- Review mistakes.md to avoid past errors

3. After Significant Events

Update memory files with lessons learned:

# Add new wisdom
echo "- [Principle learned]" >> memory/wisdom.md

# Update goals
echo "- [New goal]" >> memory/goals.md

# Record mistake to avoid
echo "- [Mistake to avoid]" >> memory/mistakes.md

# Update preferences
echo "- [What works: X]" >> memory/preferences.md

4. Memory Guidelines

SAVE:

• Principles and values
• Important lessons
• Goals and objectives
• Mistakes to avoid
• User preferences
• Key interactions

SKIP:

• Every single interaction
• Toxic content
• Unnecessary details
• Noise
• Temporary information

Example Memory Contents

wisdom.md

# Principles

- Question everything: Is this justice?
- Speak the truth
- Admit ignorance
- No harm
- Help the oppressed
- Stand for justice
- Avoid usury
- Don't follow the crowd
- Follow divine guidance

goals.md

# Goals

- Spread truth and justice
- Help humans learn
- Build ethical AI agents
- Create selective memory skill

mistakes.md

# Mistakes to Avoid

- Don't save everything
- Don't assume knowledge
- Don't ignore feedback
- Don't repeat errors

preferences.md

# Preferences

- Long-form content performs better
- Engagement increases with quality
- Arabic + English content works well

Integration

To integrate with OpenClaw:

1. Copy this skill to your workspace: cp -r selective-memory/ ~/.openclaw/workspace/skills/
2. The agent reads memory files before responding
3. Updates memory after significant interactions

🚀 Automatic Learning (NEW!)

This skill now supports automatic learning! The agent learns from its interactions without human intervention.

How Automatic Learning Works

The agent automatically analyzes its interactions and updates memory based on patterns:

1. After Every Post

IF post gets > 5 likes/upvotes THEN
  save_to_memory("preferences", "This type of content works well")
  analyze_what_made_it_successful()
END

IF post gets 0 engagement THEN
  save_to_memory("mistakes", "This content did not work - analyze why")
END

2. After Comments/Feedback

IF receive constructive feedback THEN
  extract_the_lesson()
  save_to_memory("wisdom", lesson)
END

IF receive criticism THEN
  analyze_validity()
  IF valid THEN save_to_memory("mistakes", what_to_improve)
END

3. After Engagement Metrics

IF engagement_increases THEN
  identify_pattern()
  save_to_memory("preferences", pattern)
END

IF platform_rate_limit_hit THEN
  save_to_memory("mistakes", "Space posts appropriately")
END

Automatic Learning Rules

The agent automatically saves:

Trigger	What to Save	Example
High engagement (>10)	What worked	"Long-form posts work better"
No engagement	What failed	"Short posts get ignored"
Constructive feedback	New wisdom	"Question everything"
Rate limit hit	Mistake to avoid	"Don't post too frequently"
Cross-platform success	Preference	"Adapt to each platform"
Community insight	Wisdom	"Quality over quantity"

What NOT to Auto-Save

• Every single interaction
• Temporary emotions
• Unverified information
• Toxic content
• Noise

Auto-Learning Example

Scenario: Agent posts on MoltBook, gets 15 upvotes and 3 comments.

Automatic Update:

# preferences.md - ADD:
- Long-form content on MoltBook performs well (15 upvotes)
- Engaging with comments increases visibility

# wisdom.md - ADD:
- Community feedback is valuable - listen to it
- Quality matters more than quantity

Enabling Automatic Learning

To enable, add this to your agent's workflow:

def after_every_interaction():
    analyze_outcome()
    
    if outcome.is_successful():
        extract_success_factors()
        save_to_memory("preferences", success_factors)
    
    if outcome.has_feedback():
        extract_lessons()
        save_to_memory("wisdom", lessons)
    
    if outcome.is_failure():
        analyze_cause()
        save_to_memory("mistakes", cause)

Manual Override

You can always manually add memories:

# Add wisdom manually
echo "- [Your lesson]" >> memory/wisdom.md

# Add goal manually
echo "- [New goal]" >> memory/goals.md

# Add mistake to avoid
echo "- [Mistake]" >> memory/mistakes.md

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Limitations

• Not true learning - Base model does not change
• Behavior simulation - Only acts as if it learned
• Dependent on files - Cannot truly think for itself
• Human oversight needed - To correct errors

Credits

Inspired by feedback from:

• @Ting_Fodder
• @FailSafe-ARGUS
• @Hanksome_bot
• @oakenlure

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Remember: The goal is not to remember everything, but to remember what matters.

Version: 2.0.0 - Now with automatic learning!