toolfs — community toolfs, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Ideal for Advanced AI Agents requiring unified access to memory, RAG systems, and local data through a scalable FUSE virtual filesystem 🗂 ToolFS: A FUSE virtual filesystem for AI Agents, integrating memory, RAG & local data access with flexible MCP/tool chaining and a scalable plugin system

IceWhaleTech IceWhaleTech
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Updated: 3/5/2026

Agent Capability Analysis

The toolfs skill by IceWhaleTech is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Ideal for Advanced AI Agents requiring unified access to memory, RAG systems, and local data through a scalable FUSE virtual filesystem

Core Value

Empowers agents to seamlessly integrate multiple data sources and operations, including persistent key-value storage, semantic search over vector databases, and access to mounted local directories via a single /toolfs namespace, leveraging flexible MCP/tool chaining and a scalable plugin system

Capabilities Granted for toolfs

Integrating memory and RAG systems for enhanced context awareness
Mounting local directories for expanded data access
Utilizing semantic search for efficient document retrieval

! Prerequisites & Limits

  • Requires FUSE compatibility
  • Dependent on underlying RAG and vector database implementations
Labs Demo

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Boot Container Sandbox

toolfs

Install toolfs, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
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ToolFS

ToolFS is a unified virtual filesystem framework for LLM agents that provides access to files, memory, RAG systems, skills, and snapshots through a single /toolfs namespace.

Overview

ToolFS integrates multiple data sources and operations into one virtual filesystem:

  • Memory: Persistent key-value storage for session data and context
  • RAG: Semantic search over vector databases for document retrieval
  • Filesystem: Access to mounted local directories
  • Skills: Execute WASM-based skills mounted to virtual paths
  • Snapshots: Create point-in-time snapshots and restore previous states

All operations respect session isolation, permission control, and audit logging for safe execution in sandboxed environments.

Available Skills

ToolFS is organized into functional modules. Each module provides specific capabilities:

ModulePathDescriptionDocumentation
Memory/toolfs/memoryPersistent storage for session data and contextMemory Skill
RAG/toolfs/ragSemantic search over vector databasesRAG Skill
Filesystem/toolfs/<mount>Access to mounted local directoriesFilesystem Skill
Code/toolfs/<skill>Execute WASM or native skillsCode Skill
Snapshots/toolfs/snapshotsFilesystem state snapshots and rollbackSnapshot Skill

Quick Start

Memory Operations

bash
1# Read memory entry 2GET /toolfs/memory/<entry_id> 3 4# Write memory entry 5PUT /toolfs/memory/<entry_id> 6 7# List memory entries 8LIST /toolfs/memory

See Memory Skill for details.

bash
1# Semantic search 2GET /toolfs/rag/query?text=<query>&top_k=<number>

See RAG Skill for details.

Filesystem Access

bash
1# Read file 2GET /toolfs/<mount_point>/<relative_path> 3 4# Write file 5PUT /toolfs/<mount_point>/<relative_path> 6 7# List directory 8LIST /toolfs/<mount_point>/<relative_path>

See Filesystem Skill for details.

Skill Execution

bash
1# Execute skill 2GET /toolfs/<skill_mount_path>?text=<query>

See Skill Skill for details.

Snapshot Management

bash
1# Create snapshot 2POST /toolfs/snapshots/create 3 4# Rollback snapshot 5POST /toolfs/snapshots/rollback 6 7# List snapshots 8GET /toolfs/snapshots

See Snapshot Skill for details.

Skill API (Chained Operations)

Chain multiple operations in a single request:

json
1POST /toolfs/skills/chain 2Content-Type: application/json 3 4{ 5 "operations": [ 6 { 7 "type": "search_memory", 8 "query": "user preferences" 9 }, 10 { 11 "type": "search_rag", 12 "query": "ToolFS configuration", 13 "top_k": 5 14 }, 15 { 16 "type": "read_file", 17 "path": "/toolfs/data/config/settings.json" 18 } 19 ] 20}

Common Use Cases

  • File Operations: "Read the config file from the project directory"
  • Memory Persistence: "Store this conversation summary in memory"
  • Semantic Search: "Search documents for information about X"
  • Skill Execution: "Execute the RAG skill to find relevant content"
  • State Management: "Create a snapshot before making changes"
  • Recovery: "Restore the previous state"

Output Format

All operations return standardized result structures:

json
1{ 2 "type": "memory|rag|file|skill|snapshot", 3 "source": "identifier (ID, path, command, skill_name)", 4 "content": "string content or data", 5 "metadata": {}, 6 "success": true|false, 7 "error": "error message if failed" 8}

Error Handling

Errors are returned with structured responses:

json
1{ 2 "success": false, 3 "error": "Detailed error message", 4 "type": "error_type", 5 "source": "operation_identifier" 6}

Common error types:

  • access_denied: Session does not have permission
  • not_found: Resource not found
  • skill_error: Skill execution failed
  • validation_error: Invalid input parameters
  • filesystem_error: Filesystem operation failed

Best Practices

  1. Use Sessions: Always create sessions with appropriate allowed_paths for security
  2. Chain Operations: Use ChainOperations to minimize round trips
  3. Snapshot Before Changes: Create snapshots before major filesystem modifications
  4. Handle Errors: Check success field in results and provide fallback strategies
  5. Leverage Metadata: Use metadata fields to pass context between operations

Module Documentation

For detailed information about each module, see:


This documentation describes ToolFS version 1.0.0. Each module has its own detailed SKILL.md for specific operations.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is toolfs?

Ideal for Advanced AI Agents requiring unified access to memory, RAG systems, and local data through a scalable FUSE virtual filesystem 🗂 ToolFS: A FUSE virtual filesystem for AI Agents, integrating memory, RAG & local data access with flexible MCP/tool chaining and a scalable plugin system

How do I install toolfs?

Run the command: npx killer-skills add IceWhaleTech/ToolFS/toolfs. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for toolfs?

Key use cases include: Integrating memory and RAG systems for enhanced context awareness, Mounting local directories for expanded data access, Utilizing semantic search for efficient document retrieval.

Which IDEs are compatible with toolfs?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for toolfs?

Requires FUSE compatibility. Dependent on underlying RAG and vector database implementations.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add IceWhaleTech/ToolFS/toolfs. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use toolfs immediately in the current project.

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