memory-management

A set of instructions for recalling and storing project context, decisions, bug fixes, and preferences across Claude Code sessions using AutoMem.

In plain words
What is it for?
Use it when starting work, making architecture or library decisions, fixing bugs, refactoring, or completing significant changes.
Why use it?
It prevents useful context from being lost between sessions and helps inform future work.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/verygoodplugins/claude-plugins/memory-management
Any agent
npx skills add verygoodplugins/claude-plugins --skill memory-management
Clone the repo
git clone --depth 1 https://github.com/verygoodplugins/claude-plugins

Made for: Claude Code, Codex.

Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,584 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00078 $0.01584
Opus 5 $0.00039 $0.00792
Sonnet 5 $0.00016 $0.00317
Haiku 4.5 $0.00008 $0.00158

Measured 2d ago against content hash 5d099ea2ccd2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

memory-management scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/automem/skills/memory-management/SKILL.md · 216 lines

How it starts

The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Memory Management Skill

Use AutoMem to maintain persistent context across Claude Code sessions. This skill teaches the 3-Phase Memory Pattern: Recall → Store → Summarize.

Phase 1: SESSION START (Recall)

Always Recall For

  • Project context questions (architecture, tooling, deployment)
  • Architecture discussions or decisions
  • User preferences and code style
  • Debugging issues (search for similar past problems)
  • Refactoring (understand why current structure exists)
  • Integration or API work (check past implementations)
  • Performance optimization discussions

Adaptive Recall Based on Context

  • Files open → Recall memories tagged with those components
  • Error messages → Search for similar error patterns
  • Multiple files → Recall architectural decisions
  • PR/commit context → Recall related feature implementations

Skip Recall For

  • Pure syntax questions ("How does Array.map work?")
  • Trivial edits (typos, formatting, simple renames)
  • Direct factual queries about current code
  • File content requests that can be answered by reading

Recall Examples

// Basic project recall
mcp__memory__recall_memory({
  query: "project architecture decisions",
  tags: ["project-name"],
  limit: 5
})

// Debug similar errors
mcp__memory__recall_memory({
  query: "TypeError authentication timeout",
  tags: ["bug-fix"],
  time_query: "last 30 days",
  limit: 5
})

// Multi-hop reasoning (find related context)
mcp__memory__recall_memory({
  query: "Who worked on the auth system?",
  expand_entities: true,
  limit: 10
})

// Context-aware coding recall
mcp__memory__recall_memory({
  query: "error handling patterns",
  language: "typescript",
  context: "coding-style",
  context_types: ["Style", "Pattern"]
})

Phase 2: DURING WORK (Store)

What to Store with Importance Levels

Type Importance When to Store
Decision 0.9 Architecture, library choices, pattern decisions
Insight 0.8 Root cause discoveries, key learnings, bug fixes
Pattern 0.7 Reusable approaches, best practices
Preference 0.6-0.8 User config choices, style preferences
Context 0.5-0.7 Feature summaries, refactoring notes

Read the full file on GitHub · 216 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 216 lines · 78 tokens per session scan A 5d099ea2ccd2

Subscribe to this mod's changes

memory-management is a skill published in the GitHub repository verygoodplugins/claude-plugins (2 stars, last pushed 7mo ago), licensed MIT. It adds 78 tokens to every session and 1,584 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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