agent-memory
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Best use case
agent-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "agent-memory" skill to help with this workflow task. Context: Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-memory/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-memory Compares
| Feature / Agent | agent-memory | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Agent Memory
A persistent memory space for storing knowledge that survives across conversations.
**Location:** `.claude/skills/agent-memory/memories/`
## Proactive Usage
Save memories when you discover something worth preserving:
- Research findings that took effort to uncover
- Non-obvious patterns or gotchas in the codebase
- Solutions to tricky problems
- Architectural decisions and their rationale
Check memories when starting related work:
- Before investigating a problem area
- When working on a feature you've touched before
Organize memories when needed:
- Consolidate scattered memories on the same topic
- Remove outdated or superseded information
## Folder Structure
When possible, organize memories into category folders. No predefined structure - create categories that make sense for the content.
Guidelines:
- Use kebab-case for folder and file names
- Consolidate or reorganize as the knowledge base evolves
Example:
```text
memories/
├── file-processing/
│ └── large-file-memory-issue.md
├── dependencies/
│ └── iconv-esm-problem.md
└── project-context/
└── december-2025-work.md
```
This is just an example. Structure freely based on actual content.
## Frontmatter
All memories must include frontmatter with a `summary` field. The summary should be concise enough to determine whether to read the full content.
**Required:**
```yaml
---
summary: "1-2 line description of what this memory contains"
created: 2025-01-15 # YYYY-MM-DD format
---
```
**Optional:**
```yaml
---
summary: "Worker thread memory leak during large file processing - cause and solution"
created: 2025-01-15
updated: 2025-01-20
tags: [performance, worker, memory-leak]
related: [src/core/file/fileProcessor.ts]
---
```
## Search Workflow
Use summary-first approach to efficiently find relevant memories:
```bash
# 1. List categories
ls .claude/skills/agent-memory/memories/
# 2. View all summaries
rg "^summary:" .claude/skills/agent-memory/memories/ --no-ignore --hidden
# 3. Search summaries for keyword
rg "^summary:.*keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 4. Search by tag
rg "^tags:.*keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 5. Full-text search (when summary search isn't enough)
rg "keyword" .claude/skills/agent-memory/memories/ --no-ignore --hidden -i
# 6. Read specific memory file if relevant
```
**Note:** Memory files are gitignored, so use `--no-ignore` and `--hidden` flags with ripgrep.
## Operations
### Save
1. Determine appropriate category for the content
2. Check if existing category fits, or create new one
3. Write file with required frontmatter (use `date +%Y-%m-%d` for current date)
```bash
mkdir -p .claude/skills/agent-memory/memories/category-name/
# Note: Check if file exists before writing to avoid accidental overwrites
cat > .claude/skills/agent-memory/memories/category-name/filename.md << 'EOF'
---
summary: "Brief description of this memory"
created: 2025-01-15
---
# Title
Content here...
EOF
```
### Maintain
- **Update**: When information changes, update the content and add `updated` field to frontmatter
- **Delete**: Remove memories that are no longer relevant
```bash
trash .claude/skills/agent-memory/memories/category-name/filename.md
# Remove empty category folders
rmdir .claude/skills/agent-memory/memories/category-name/ 2>/dev/null || true
```
- **Consolidate**: Merge related memories when they grow
- **Reorganize**: Move memories to better-fitting categories as the knowledge base evolves
## Guidelines
1. **Write for your future self**: Include enough context to be useful later
2. **Keep summaries decisive**: Reading the summary should tell you if you need the details
3. **Stay current**: Update or delete outdated information
4. **Be practical**: Save what's actually useful, not everythingRelated Skills
memory-init
在当前目录下初始化记忆系统,生成 CLAUDE.md(可选 AGENT.md 给 Cursor 用)、MEMORY.md 和 memory/ 目录。当用户说"初始化记忆"、"搭建记忆"、"memory init"、"/memory-init"时触发。
memory-safety-patterns
Implement memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, managing resources, or preventing memory bugs.
memory-forensics
Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzing memory dumps, investigating incidents, or performing malware analysis from RAM captures.
conversation-memory
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
ai-runtime-memory
AI Runtime分层记忆系统,支持SQL风格的事件查询、时间线管理,以及记忆的智能固化和检索,用于项目历史追踪和经验传承
memory-orchestration
Analyze context management, memory systems, and state continuity in agent frameworks. Use when (1) understanding how prompts are assembled, (2) evaluating eviction policies for context overflow, (3) mapping memory tiers (short-term/long-term), (4) analyzing token budget management, or (5) comparing context strategies across frameworks.
memory-management
Context tracking and decision logging patterns for intentional memory management in Claude Code Waypoint Plugin. Use when you need to remember user preferences, track decisions, capture context across sessions, learn from corrections, or maintain project-specific knowledge. Covers when to persist context, how to track decisions, context boundaries, storage mechanisms, and memory refresh strategies.
agentdb-persistent-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants
session-memory
Manages cross-session learning and memory persistence. Use when user mentions 前回何をした, 履歴, 過去の作業, セッション記録, continue from before, session history. Do NOT load for: 実装作業, レビュー, 一時的な情報.
memory
Manages memory, SSOT files, and Plans.md operations. Use when user mentions メモリ, memory, SSOT, decisions.md, patterns.md, マージ, merge, Plans.md, 移行, migrate. Do NOT load for: 実装作業, レビュー, 一時的なメモ, セッション中の作業記録.