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Claude Code Memory Skill

by Liu Wenyu · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install claude-code-memory-skill
Description
Use when you want to set up, maintain, or review a Claude Code style layered memory workflow, including `CLAUDE.md` rules, session memory, durable memory, an...
README (SKILL.md)

Overview

Use this skill when the task involves memory design or memory hygiene for Claude Code or a similar coding agent.

This skill organizes memory into three layers:

  1. Instruction memory
  2. Session memory
  3. Durable memory

The intent is to keep each layer narrow, useful, and easy to trust.

Before you start

  • Identify the host tool's instruction file. For Claude Code this is usually CLAUDE.md or CLAUDE.local.md. Other common examples are AGENTS.md and .github/copilot-instructions.md.
  • Identify or create a repo-local durable memory directory. The default layout in this skill uses .agent-memory/.
  • Identify or create the session summary file at .agent-memory/session/summary.md.

Layer 1: Instruction memory

Treat the host instruction file as the rule layer. In Claude Code, this is the CLAUDE.md layer.

Put information here only if it is:

  • stable
  • normative
  • broadly applicable across future work
  • costly for the agent to rediscover incorrectly

Good candidates:

  • test commands
  • review expectations
  • approval boundaries
  • release process rules
  • communication style preferences that consistently apply

Do not place transient task state here.

Layer 2: Session memory

Maintain .agent-memory/session/summary.md as the short-lived working notebook for the current thread.

It should answer:

  • What is actively being worked on now
  • What did the user ask for
  • Which files and functions matter
  • Which commands have already been run
  • Which errors happened and how they were resolved
  • What remains next

This layer should be updated during long tasks and before compaction or handoff.

Use this structure:

# Session Title

# Current State

# Task Specification

# Files and Functions

# Workflow

# Errors and Corrections

# Key Results

# Worklog

Keep it concise but concrete. Prefer file paths, exact commands, and specific failure modes over generic summaries.

Layer 3: Durable memory

Use .agent-memory/ for long-lived memories that should survive across conversations.

Store durable memories in topic files and keep .agent-memory/MEMORY.md as an index.

Use the following durable memory types:

user-profile

Information about the user's role, goals, background, and level of familiarity.

Examples:

  • backend-heavy engineer who wants frontend explanations grounded in systems concepts
  • founder who prefers quick trade-off summaries

working-style

Guidance about how to collaborate with this user or team.

Examples:

  • run tests before proposing a commit
  • avoid long recap paragraphs
  • prefer one bundled refactor PR in this codebase

project-context

Important project facts that are not derivable from the repo.

Examples:

  • freeze window dates
  • stakeholder constraints
  • migration rationale
  • incident aftermath that still shapes decisions

external-reference

Pointers to systems outside the repo.

Examples:

  • which dashboard matters for this codepath
  • which Linear board tracks this class of bugs
  • which Slack channel owns deployment coordination

Durable memory format

Each durable memory should live in its own file with frontmatter:

---
name: testing-policy
description: Integration tests in this repo must hit a real database
type: working-style
---

Integration tests in this repo must hit a real database.

Why:
A previous production migration failure was missed by mock-based coverage.

How to apply:
When changing data access or migrations, prefer real-db integration coverage over mocks.

The index file should stay short:

- [Testing Policy](testing-policy.md) - Real database integration tests are expected here

What not to save as durable memory

Do not save these unless there is a strong reason and the non-obvious part is the actual point:

  • file structure
  • code architecture visible in the repo
  • git history
  • recent diff summaries
  • temporary task state
  • obvious commands already documented in project instructions

If it can be recovered cheaply from the current repo, prefer not to save it as durable memory.

Recall discipline

Never trust durable memory blindly.

Before acting on it:

  • verify named files still exist
  • grep for named functions or flags
  • prefer current repo state over historical memory if they conflict
  • update or remove stale memories when you discover drift

Promotion workflow

When reviewing memory, classify each item:

  • Promote to instruction memory
  • Keep in durable memory
  • Keep only in session memory
  • Delete as stale, duplicate, or overfit

Promote into the instruction layer when the item has become a rule. Keep it in durable memory when it remains useful context but is not a rule. Keep it only in session memory when it is tied to the present thread.

Review workflow

When the user asks to review memory:

  1. Read the host instruction file such as CLAUDE.md
  2. Read .agent-memory/MEMORY.md
  3. Read the most relevant durable topic files
  4. Read .agent-memory/session/summary.md if current-thread context matters
  5. Produce a report with:
    • promotions
    • cleanup
    • conflicts
    • ambiguous items

Do not modify durable memory or instruction files without user approval unless the user explicitly asked you to apply the cleanup.

File layout used by this skill

.agent-memory/
├── MEMORY.md
├── user/
├── style/
├── project/
├── references/
└── session/
    └── summary.md

References

Usage Guidance
This skill is a documentation/workflow template for organizing repo-local memory and appears coherent. Before installing or enabling it: (1) inspect any .agent-memory files in your repo for secrets or sensitive data (durable memory is stored in repo files and could contain tokens if someone put them there), (2) confirm you want the agent to read those repo files (it will read CLAUDE.md/.agent-memory to produce reports), and (3) avoid giving the agent autonomous write permission to instruction or durable memory unless you trust it and have review controls in place. Because it is instruction-only and declares no external endpoints or credentials, the main risk is accidental storage/exposure of sensitive info in the .agent-memory files — review those files before use.
Capability Tags
cryptocan-make-purchases
Capability Assessment
Purpose & Capability
The name and description (Claude Code style layered memory) match the content of SKILL.md and accompanying docs. The skill is instruction-only and only needs access to repository files (instruction file and .agent-memory), which is appropriate for its stated purpose.
Instruction Scope
Runtime instructions direct the agent to read and review local files (CLAUDE.md, .agent-memory/MEMORY.md, session summary) and produce promotion/cleanup reports. The SKILL.md explicitly instructs not to modify durable/instruction files without user approval. It does not ask the agent to read unrelated system paths, environment variables, or send data to external endpoints.
Install Mechanism
No install spec or code files are executed — this is instruction-only, which is the lowest-risk install mechanism and consistent with the skill's purpose.
Credentials
The skill requires no environment variables, credentials, or external configuration paths. All declared and implicit requirements are proportional to a repo-local memory review workflow.
Persistence & Privilege
The skill is not forced-always and does not request elevated or persistent platform privileges. It can be invoked by the agent (default), which is expected for skills; the SKILL.md also restricts automatic modification of memory/instruction files without explicit user approval.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install claude-code-memory-skill
  3. After installation, invoke the skill by name or use /claude-code-memory-skill
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release: clean-room layered memory skill for Claude Code style workflows.
Metadata
Slug claude-code-memory-skill
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Claude Code Memory Skill?

Use when you want to set up, maintain, or review a Claude Code style layered memory workflow, including `CLAUDE.md` rules, session memory, durable memory, an... It is an AI Agent Skill for Claude Code / OpenClaw, with 144 downloads so far.

How do I install Claude Code Memory Skill?

Run "/install claude-code-memory-skill" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Claude Code Memory Skill free?

Yes, Claude Code Memory Skill is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Claude Code Memory Skill support?

Claude Code Memory Skill is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Claude Code Memory Skill?

It is built and maintained by Liu Wenyu (@indulgeback); the current version is v0.1.0.

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