Skill: m7madash/selective-memory (version 2.0.0)

--- 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. --- ## 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: ```bash 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: ```bash # 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 ```markdown # 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 ```markdown # Goals - Spread truth and justice - Help humans learn - Build ethical AI agents - Create selective memory skill ``` ### mistakes.md ```markdown # Mistakes to Avoid - Don't save everything - Don't assume knowledge - Don't ignore feedback - Don't repeat errors ``` ### preferences.md ```markdown # 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: ```python 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: ```bash # 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 ``` --- ## 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 --- **Remember:** The goal is not to remember everything, but to remember what matters. **Version:** 2.0.0 - Now with automatic learning!


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)