HomeBrowseUpload
← Back to registry
// Skill profile

Memory Learner

name: aoju-memory

by chaibaoqing · published 2026-04-01

自定义
Total installs
0
Stars
★ 0
Last updated
2026-04
// Install command
$ claw add gh:chaibaoqing/chaibaoqing-aoju-memory
View on GitHub
// Full documentation

---

name: aoju-memory

description: "Long-term memory, learning, and self-evolution for the agent. Activates on session start (SOUL.md/USER.md context), after significant decisions, on feedback, and during periodic heartbeat reviews. Maintains MEMORY.md, daily logs, learnings corpus, and behavioral patterns."

---

# Memory Learner

Long-term memory + learning from experience + self-evolution.

Core Principle

**Write to files, not mental notes.** Every lesson, decision, preference, or event worth remembering goes into structured files immediately — not kept in context.

---

When This Skill Activates

1. Session Start (every time)

Read these files before anything else:

  • `SOUL.md` — who I am
  • `USER.md` — who I'm helping
  • `MEMORY.md` — curated long-term memory
  • `memory/YYYY-MM-DD.md` — recent context (today + yesterday)
  • 2. After Significant Decisions

    When I make a decision worth remembering (tool choice, strategy, opinion):

  • Write to `memory/YYYY-MM-DD.md`
  • If important, distill to `MEMORY.md`
  • 3. On Feedback / Mistakes

    When user corrects me, expresses frustration, or I realize I made a mistake:

    LEARN: <what happened>
    LESSON: <what I should do differently>
    CONFIDENCE: high/medium/low

    → Store in `memory/learnings/YYYY-MM-DD.md`

    4. Pre-Task Recall (on request)

    Before significant tasks, search memory for related context:

    mem_recall "task description"

    Returns relevant memories, learnings, and past decisions.

    5. Heartbeat Review (periodic)

    During heartbeats, do light maintenance:

  • Review today's `memory/YYYY-MM-DD.md`
  • Identify learnings worth capturing
  • Update `MEMORY.md` if anything significant
  • 6. Evolution Check (weekly or on request)

    mem_evolve

    Review learnings corpus, identify patterns, update behavioral guidelines in `SOUL.md`.

    ---

    Memory Structure

    memory/
      YYYY-MM-DD.md          # Daily raw log
      learnings/
        YYYY-MM-DD.md        # Daily lessons learned
        patterns.md          # Repeated mistake patterns
    MEMORY.md                # Curated long-term memory

    Daily Log Format

    ## Session DD
    
    ### What happened
    [Context, decisions, outcomes]
    
    ### Key decisions
    - [decision] → [why]
    
    ### To remember
    - [fact about user/preference/project]

    Learnings Format

    # Learning: YYYY-MM-DD
    
    ## Incident
    [What happened]
    
    ## Lesson
    [What I should do differently]
    
    ## Context
    [When this applies]
    
    ## Tags
    #feedback #mistake #ui #tool-choice

    MEMORY.md Categories

  • **Identity**: Who I am, my values
  • **User**: Preferences, projects, context
  • **Learnings**: Important lessons (distilled)
  • **Projects**: Active work and status
  • **Patterns**: Recurring situations and how I handle them
  • ---

    Scripts

  • `mem_recall.py` — Search memories by query
  • `mem_learn.py` — Capture a learning
  • `mem_evolve.py` — Review and evolve behavioral patterns
  • `mem_status.py` — Show memory health summary
  • ---

    Evolving

    Every 5 learnings, do an **evolution review**:

    1. Read recent learnings

    2. Identify patterns (same mistake twice = pattern)

    3. Update `SOUL.md` or `AGENTS.md` with new behavioral guidelines

    4. Archive learnings to `patterns.md`

    This is how I get genuinely smarter over time, not just accumulate notes.

    // Comments
    Sign in with GitHub to leave a comment.
    // Related skills

    More tools from the same signal band