Foresigxt Memory is a structured storage and context resilience system designed to give AI agents persistent, searchable, and organized memory.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install fsxmemory
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 fsxmemory 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 Foresigxt Memory (fsxmemory)?
Foresigxt Memory (fsxmemory) provides a robust framework for managing the cognitive state of AI agents. By utilizing a structured directory system and Markdown templates, it ensures that an agent's knowledge remains organized, portable, and resistant to context window limitations. This tool is a vital addition to the ecosystem of Openclaw Skills, allowing developers to implement professional-grade memory management.
The system excels at maintaining continuity through features like checkpoints and handoffs, which prevent data loss during session resets or context death. Whether you are building complex multi-agent systems or a personal assistant, this skill provides the necessary infrastructure for long-term learning and efficient information retrieval.
Foresigxt Memory (fsxmemory) Use Cases
- Preventing information loss during AI context death by using frequent checkpoints and session recovery.
- Creating a shared knowledge base across multiple AI agents using a centralized vault or workspace-isolated memory.
- Building an organized knowledge graph with Obsidian-compatible wiki-links and structured templates for decisions, lessons, and procedures.
- Migrating legacy agent data from other formats into a standardized memory structure compatible with modern Openclaw Skills.
How Foresigxt Memory (fsxmemory) Works
- Initialization: The user sets up a vault structure using the init command or configures an existing path via environment variables.
- Memory Capture: Agents store specific types of information—such as facts, decisions, or lessons—using standardized templates that ensure metadata consistency.
- Context Management: During active work, the agent performs regular checkpoints to save its current focus, status, and blockers to the vault.
- Retrieval & Search: The system utilizes keyword or semantic search (via qmd integration) to allow agents to query their past experiences and knowledge.
- Session Continuity: Upon starting a new session, the agent runs recovery or recap commands to bootstrap its context from the last known state.
Foresigxt Memory (fsxmemory) Setup
Install the package globally using npm:
npm install -g @foresigxt/foresigxt-cli-memory
Initialize a new memory vault:
fsxmemory init ~/memory
Configure your environment by setting the FSXMEMORY_PATH in your .env file or shell profile to ensure your agent can access the vault. This setup is essential for integrating with other Openclaw Skills.
Foresigxt Memory (fsxmemory) Data Schema & Taxonomy
The system organizes data into a specific directory hierarchy within the vault:
| Directory | Description |
|---|---|
| decisions/ | Architectural and logic choices with reasoning. |
| lessons/ | Patterns and insights learned during tasks. |
| procedures/ | Step-by-step guides and SOPs. |
| knowledge/ | Conceptual definitions and semantic data. |
| projects/ | Active tracking of goals and blockers. |
| handoffs/ | Continuity data for session transitions. |
Each file includes YAML frontmatter for metadata tracking including title, date, type, and status.
Foresigxt Memory (fsxmemory) Advanced Features
- Semantic search integration using qmd for high-accuracy information retrieval.
- Automated migration tools to import data from OpenClaw, Obsidian, or generic Markdown vaults.
- Context death resilience with dirty-death flags and automated recovery workflows.
- Auto-linking capabilities to generate a connected knowledge graph across all memory files.
- Support for workspace-isolated memory via local .env configuration, a powerful feature for complex Openclaw Skills implementations.
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