#601

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memory-optimize AI Agent Skill

View Source: kochetkov-ma/claude-brewcode

Critical

Installation

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

1.2K

Installs

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`

Installs

Installs 1.2K
Global Rank #601 of 601

Security Audit

ath Safe
socket Safe
Alerts: 0 Score: 90
snyk High
zeroleaks Safe
Score: 93
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How to use this skill

1

Install memory-optimize by running npx skills add kochetkov-ma/claude-brewcode --skill memory-optimize in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.

2

No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.

3

The skill enhances your agent's understanding of memory-optimize, helping it follow established patterns, avoid common mistakes, and produce production-ready output.

What you get

Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.

Compatibility

This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.

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

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