#601

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memory-optimize AI Agent Skill

Quellcode ansehen: kochetkov-ma/claude-brewcode

Critical

Installation

npx skills add kochetkov-ma/claude-brewcode --skill memory-optimize

1.2K

Installationen

Plugin: kochetkov-ma/claude-brewcode

Memory Optimizer

Optimizes Claude Code auto-memory files in 4 interactive steps: removes duplicates, migrates rules to proper config files, compresses remaining entries, validates the result.
Typical reduction: 30–50% token count in memory files.

Auto-memory stores context across sessions in ~/.claude/projects/**/memory/MEMORY.md.
Enable: CLAUDE_CODE_DISABLE_AUTO_MEMORY=0 · Disable: CLAUDE_CODE_DISABLE_AUTO_MEMORY=1

Benefits: faster context loading · no duplicate rules · cleaner instructions · lower API cost

Usage:

/memory-optimize          # no args — starts 4-step interactive workflow

Skill text is written for LLM consumption and optimized for token efficiency.


Memory Optimizer

Optimizes Claude Code memory files through 4 interactive steps.

No context: fork — must run in main conversation to spawn agents.

Phase 0: Load Context

  1. Glob all memory files: ~/.claude/projects/**/memory/*.md
  2. Read ~/.claude/CLAUDE.md and project CLAUDE.md (if exists)
  3. Glob .claude/rules/*.md — read all project rules
  4. Read ~/.claude/rules/*.md — read all global rules

Build context map:

memory_files: [paths]
claude_md_sections: [sections]
rules_files: [paths with content]

Step 1: Analysis — Remove Duplicates (Interactive)

Goal: Find memory entries that duplicate content already in CLAUDE.md or rules.

  1. Spawn Explore agent to cross-reference all loaded files
  2. Identify entries where:
    • Same rule already in CLAUDE.md
    • Same pattern already in a rules file
    • Contradicts CLAUDE.md (CLAUDE.md wins)
  3. Show analysis:
    Found X duplicate/redundant entries (Y% of memory):
    | Entry | Memory File | Already In | Action |
    |-------|-------------|------------|--------|
    | "Use grepai first" | MEMORY.md:5 | rules/grepai-first.md | DELETE |
    ...
  4. AskUserQuestion: "Delete X duplicate entries (Y% of memory)? This is safe — content exists elsewhere."
    • Options: "Yes, delete all" / "Review each" / "Skip this step"
  5. Apply deletion using Edit tool if approved

Step 2: Migration — Move to Rules/CLAUDE.md (Interactive)

Goal: Identify remaining memory entries better suited to persistent config files.

Decision tree (per entry):

  • Applies to ALL projects + IS a rule/constraint → ~/.claude/rules/
  • Applies to THIS project only + IS a rule → .claude/rules/
  • IS an architectural decision → project CLAUDE.md
  • IS a fact/pattern reusable across sessions → KEEP in memory
  1. Show categorization:
    X entries suitable for migration:
    | Entry | Current Location | Target | Reduction |
    |-------|-----------------|--------|-----------|
    | "Always use BD_PLUGIN_ROOT" | MEMORY.md:12 | .claude/rules/brewdoc.md | 15 tokens |
    ...
    Total: X entries → ~Y tokens saved
  2. AskUserQuestion: "Migrate X entries to rules/CLAUDE.md?"
    • Options: "Yes, migrate all" / "Review each" / "Skip this step"
  3. If approved:
    • Create/append to target rule files via Edit
    • Remove migrated entries from memory via Edit
    • If target file doesn't exist, create it

Step 3: Compression (Interactive)

Goal: Compress remaining entries using LLM-efficient formatting.

Compression techniques:

  • Prose → table row
  • Multiple related entries → single table
  • Verbose description → imperative one-liner
  • List of examples → pattern + one example
  1. Show compression preview:
    Compression opportunities found:
    | Before | After | Savings |
    |--------|-------|---------|
    | "When you need to... always use..." | "Use X for Y" | 8 tokens |
    ...
    Total: ~Y% token reduction (~Z tokens)
    Show 2-3 specific before/after samples.
  2. AskUserQuestion: "Compress remaining memory? (~Y% reduction)"
    • Options: "Yes, compress all" / "Skip compression"
  3. Apply compression via Edit (bottom-up order to preserve line numbers)

Step 4: Validation (Automatic)

Goal: Verify final state and clean orphaned references.

  1. Spawn reviewer agent to verify:
    • No broken file path references in memory files
    • No contradictions between memory and CLAUDE.md
    • Memory files are well-formed markdown
  2. Clean broken references (Edit tool)
  3. Check for orphaned memory files (files in ~/.claude/projects/**/memory/ with no MEMORY.md reference)
  4. Report orphaned files and ask to delete

Final Report:

## Memory Optimization Complete

### Summary
| Metric | Before | After | Saved |
|--------|--------|-------|-------|
| Total entries | X | Y | Z |
| Duplicate entries | X | 0 | — |
| Migrated entries | — | — | X |
| Token estimate | ~X | ~Y | ~Z (~P%) |

### Changes Made
- Step 1: Deleted X duplicate entries
- Step 2: Migrated X entries to rules/CLAUDE.md
- Step 3: Compressed X entries (Y% reduction)
- Step 4: Fixed X broken references, removed X orphaned files

### Final Memory Structure
{directory listing of ~/.claude/projects/.../memory/}

---

**Part of brewdoc:** [brewcode](https://github.com/kochetkov-ma/claude-brewcode) — docs tools: memory optimization, auto-sync, Claude installation docs, Markdown to PDF.
Install: `claude plugin marketplace add https://github.com/kochetkov-ma/claude-brewcode && claude plugin install brewdoc@claude-brewcode`

Installationen

Installationen 1.2K
Globales Ranking #601 von 601

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So verwenden Sie diesen Skill

1

Install memory-optimize by running npx skills add kochetkov-ma/claude-brewcode --skill memory-optimize in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

2

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

3

Der Skill verbessert das Verständnis Ihres Agenten für memory-optimize, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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