A sophisticated memory recall system that ensures AI agents associate previous experiences and data before consuming tokens on new tasks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-association
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install memory-association using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Memory Association?
Memory Association is a core architectural pattern for AI agents within the Openclaw Skills ecosystem. It operates on the fundamental principle of associate first, act later, ensuring that the agent retrieves relevant past context from a centralized index before initiating any high-resource operations. By referencing the MEMORY_INDEX.md, the skill allows agents to maintain continuity across sessions, project phases, and diverse task types.
This skill transforms the AI from a stateless executor into a context-aware collaborator. It manages a multi-tiered storage strategy—ranging from hot memory for immediate tasks to archived dream-state memory—ensuring that every decision is backed by historical data, previous lessons learned, and specific project constraints discovered in earlier sessions.
Memory Association Use Cases
- Initializing new development tasks or project phases where historical context is vital.
- Analyzing complex requirements by recalling similar past implementations.
- Troubleshooting recurring issues using indexed error logs and previous solutions.
- Responding to user queries about past actions or deployment configurations.
- Automating session startups to synchronize the agent's current state with previous work logs.
How Memory Association Works
- Index Scanning: The agent reads the memory/MEMORY_INDEX.md file to match the current task type against established association chains.
- Precision Retrieval: Utilizing the memory_get function, the system pulls specific line-level segments from relevant historical markdown files.
- Evaluation and Branching: The agent determines if the recalled memory is sufficient to proceed, needs updating due to being outdated, or if a new learning entry is required.
- Task Execution and Logging: After completing the task, the agent records key decisions and summaries back into the daily memory file to maintain the cycle.
Memory Association Setup
To integrate this recall system into your environment, ensure your workspace follows the Openclaw Skills directory structure. Use the following commands to initialize the memory directory:
mkdir -p memory/projects
mkdir -p references
touch memory/MEMORY_INDEX.md
Configure your agent's system prompt to prioritize reading the MEMORY_INDEX.md before executing any file-modifying tools.
Memory Association Data Schema & Taxonomy
The system utilizes a hierarchical data taxonomy to manage information density and retrieval speed:
| Level | Path | Frequency | Purpose |
|---|---|---|---|
| HOT | MEMORY.md | As needed | Core long-term memory (200-line limit) |
| WARM | memory/YYYY-MM-DD.md | Daily | Current work logs and session state |
| COLD | memory/projects/*.md | Project end | Permanent project archives |
| ARCHIVE | memory/.dreams/ | Periodic | Cold storage of historical sessions |
All entries are indexed in references/MEMORY_INDEX.md by project, theme, and keywords.
Memory Association Advanced Features
- Multi-tiered memory layers (Hot, Warm, Cold, Archive) for optimized token management.
- Association Chains that map specific task types to preferred recall files.
- Automated cross-referencing with .learnings/LEARNINGS.md for global rule updates.
- Support for granular retrieval using line-specific memory_get calls to minimize context window clutter.
SKILL.md
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