skillberry-store

# Skillberry-store service (a.k.a., SBS) <p> <a href="https://skillberry-ai.github.io/skillberry-store/"><img src="https://img.shields.io/badge/site-live-0066CC" alt="site"></a> <a href="https://github.com/skillberry-ai/skillberry-store/actions/workflows/push.yml"><img src="https://github.com/skillberry-ai/skillberry-store/actions/workflows/push.yml/badge.svg?branch=main" alt="CI"></a> <a href="https://github.com/skillberry-ai/skillberry-store/stargazers"><img src="https://img.shields.io/github/stars/skillberry-ai/skillberry-store?style=flat&label=stars&color=0066CC" alt="GitHub stars"></a> <img src="https://img.shields.io/badge/status-beta%20(0.x)-orange" alt="status"> <img src="https://img.shields.io/badge/python-3.11%2B-blue" alt="python"> <img src="https://img.shields.io/badge/license-Apache--2.0-informational" alt="license"> <a href="docs/plugins-installation.md"><img src="https://img.shields.io/badge/plugins-16-0066CC" alt="plugins"></a> </p> This service implements a smart skills repository for agentic workflows. Manage, execute, and organize your skills, tools and snippets with powerful search and lifecycle management.  ▶ [Watch full highlights video](https://github.com/user-attachments/assets/eab2f9f1-1196-4858-b4ca-72d6c664d9f3) ## Features ✨ - **Manage tools for agentic workloads**: Add (Persist), Remove, Update, and Delete tools. - **Tools Execution**: Invoke tools (with parameters) using Docker (sand-boxing). - **Tools Search and list**: Shortlist tools using semantic and classic search. - **Tools Life Cycle Management**: Provides tools life cycle management (state, visibility, etc.). - **Tools Persistence**: Support persistence of tools into filesystem, GitHub repos etc. - **Namespaces**: Organize and label skills, tools, and snippets using namespaces for better categorization and filtering. - **Observability**: Provide metrics and traces for operational and behavioural analysis of tools usage. - **OpenAPI frontend**: FastAPI endpoint to interact and manage tools (using tools-manifest artifacts) - **CLI Support**: Command-line interface for all API operations. - **MCP frontend**: Expose virtual [MCP](https://github.com/modelcontextprotocol) servers for any subset of the tools or all of them. - **NFS/WebDAV frontend**: Expose skills as mountable filesystems (vNFS) over WebDAV or NFSv3 — readable by any tool that can mount a network drive. - **Support Multiple MCP backends**: Consume and route additional tools from multiple backend MCP servers. - **Agentic Framework Integration**: Connect to different agentic frameworks via the MCP frontend. - **MCP control API**: Exposes an MCP server API for each of the available REST operations ( e.g., add tools, semantic search etc.) - **Plugin Architecture**: Extensible plugin system for AI-powered content generation, evaluation, and optimization. See [Plugin Installation Guide](docs/plugins-installation.md). ## Quickstart 🚀 ### Installation 📦 Install skillberry-store without plugins (minimal installation): ```bash pip install skillberry-store ``` Or install with plugins: ```bash # All plugins pip install skillberry-store[plugins-all] # Creator plugin only (AI-powered content generation) pip install skillberry-store[plugin-creator] # Evaluator plugin only (AI-powered content evaluation) pip install skillberry-store[plugin-evaluator] # Dedupe plugin only (AI-powered duplicate skill detection) pip install skillberry-store[plugin-dedupe] # Skill Optimizer plugin only (optimize existing skills using Claude Code) pip install skillberry-store[plugin-skill-optimizer] # Multiple specific plugins pip install skillberry-store[plugin-creator,plugin-evaluator,plugin-skill-optimizer] ``` For detailed plugin installation options and configuration, see the [Plugin Installation Guide](docs/plugins-installation.md). ### Run the Service with Docker or Podman 🐳 ```bash make docker-run ``` > Note: use `make help` for a complete list of options You can control where SBS stores its data by setting `SBS_BASE_DIR` (defaults to the system temp directory). ### Interacting with the UI 👨‍💻 The Skillberry Store now includes a modern web UI that starts automatically with the backend: - **Web UI**: [http://localhost:8002](http://localhost:8002) - Modern React-based interface - **API Documentation**: [http://localhost:8000/docs](http://localhost:8000/docs) - OpenAPI/Swagger interface The Web UI provides: - Visual management of Tools, Skills, Snippets, VMCP Servers, and vNFS Servers - Search and filtering capabilities - Tool execution with parameter input - Code viewing and editing - Real-time updates To disable the UI and run only the backend: ```bash ENABLE_UI=false make run ``` ## Prerequisites 🛠️ - Docker or Podman is installed on your machine. The default is `docker`. If you want to use `podman`, include this line ``` docker alias=`podman` ``` into one (or more) of the following configuration files, depending on which shell(s) you are using - `~/.zshrc` - `~/.bashrc` - `~/.bash_profile` - `~/.profile` Additional requisites for local deployment: - Your user has Docker permissions (i.e., is a member of the `docker` group). - The Docker logging driver is set to either `json-file` or `journald`. > Check the logging driver with the following command: > ```bash > docker info --format '{{.LoggingDriver}}' > ``` > If the response is not `json-file` or `journald`, configure your Docker logging as documented [here](https://docs.docker.com/engine/logging/configure/#configure-the-default-logging-driver). ## Running with podman on MacOS ⚒️ - Alias `docker` to `podman`, as explained in [Prerequisites](#prerequisites-️) - Create and start a Podman machine: ```bash podman machine init --now --cpus=4 --memory=4096 -v /tmp:/tmp podman-machine-default ``` if you already have a default Podman machine with this name, then you need to first ```bash podman machine stop podman machine rm podman-machine-default ``` Then rerun Podman machine initialization and ```bash make docker-run ``` ## Design Requirements See [DESIGN_REQUIREMENTS.md](DESIGN_REQUIREMENTS.md) ## Local installation 📦 We support Linux, macOS, and Windows. ```bash git clone git@github.com:skillberry-ai/skillberry-store.git cd skillberry-store ``` On Linux, macOS, or WSL: ```bash make install-requirements ``` On Windows (no WSL needed): ```cmd pip install -e . ``` ## Start the Service locally (alternative to docker) 🚀 On Linux, macOS, or WSL: ```bash make run ``` On Windows: ```cmd sbs-srv ``` *Notes:* * By default, SBS runs on host `0.0.0.0` and port `8000` publishing its metrics on port `8090`. To change, set the environment variables SBS_PORT/SBS_HOST/PROMETHEUS_METRICS_PORT * To disable observability all together, set environment variable `OBSERVABILITY` with `False` * The Web UI starts automatically on port `3000`. To disable it, set `ENABLE_UI=false` * On first run, the UI will automatically install its dependencies (requires Node.js 18+) ## Web UI Features 🎨 The Skillberry Store includes a modern React-based web interface with: - **Tools Management**: Create, view, execute, and delete tools with file upload support - **Skills Management**: Organize tools and snippets into reusable skills - **Snippets Management**: Store and manage code snippets with syntax highlighting - **VMCP Servers**: Create and manage virtual MCP servers for tool subsets - **vNFS Servers**: Expose skills as mountable WebDAV or NFS filesystems - **Search & Filter**: Semantic search across all resources - **Real-time Updates**: Automatic refresh of data using TanStack Query - **Responsive Design**: Built with PatternFly (IBM's design system) ### UI Technology Stack - React 18 + TypeScript - Vite (fast development server) - PatternFly (IBM design system) - TanStack Query (data fetching) - React Router (navigation) ### UI Development To work on the UI separately: ```bash cd src/skillberry_store/ui npm install npm run dev ``` The UI source code is located in `src/skillberry_store/ui/` and includes: - `src/pages/` - Page components for each section - `src/components/` - Reusable UI components - `src/services/` - API client layer - `src/types/` - TypeScript type definitions ## Engage with the Service via OpenAPI 📜 Open a browser against `http://127.0.0.1:8000/docs` . ## Engage with the Service through a Python Client 🐍 The service can be consumed via skillberry store service sdk. Refer to [skillberry-store-sdk](client/python/skillberry_store_sdk/README.md) for installation and usage. ## Engage with the Service via CLI 💻 A CLI (auto-generated) that provides command-line access to all API operations. Example usage: ```bash # Install the SDK (includes CLI) pip install skillberry-store-sdk # Use the CLI sbs --help # Show available commands sbs connect http://prod:8000 # Connect to different server sbs list-skills # List all skills sbs get-tool convert # Get a specific tool sbs create-vmcp-server # Create a VMCP server sbs search-tools "calculator" # Search for tools ``` Available command groups: | Group | Commands | |-------|----------| | **Tools** | `create-tool`, `list-tools`, `get-tool`, `get-tool-module`, `update-tool`, `delete-tool`, `execute-tool`, `search-tools`, `add-tool` | | **Skills** | `create-skill`, `list-skills`, `get-skill`, `update-skill`, `delete-skill`, `search-skills`, `detect-anthropic-skills`, `import-anthropic-skill`, `export-anthropic-skill` | | **Snippets** | `create-snippet`, `list-snippets`, `get-snippet`, `update-snippet`, `delete-snippet`, `search-snippets` | | **VMCP Servers** | `create-vmcp-server`, `list-vmcp-servers`, `get-vmcp-server`, `update-vmcp-server`, `delete-vmcp-server`, `start-vmcp-server`, `search-vmcp-servers` | | **vNFS Servers** | `create-vnfs-server`, `list-vnfs-servers`, `get-vnfs-server`, `update-vnfs-server`, `delete-vnfs-server`, `start-vnfs-server`, `search-vnfs-servers` | | **Admin** | `metrics`, `purge-all`, `health`, `health-ready` | For detailed CLI documentation, see [docs/cli.md](docs/cli.md). ## Engage with the Service via MCP 📜 Each control API function is available as an MCP tool to be used by agentic AI workflows. To access use an MCP client against `http://127.0.0.1:8000/control_sse` . ### Virtual MCP Servers (VMCP) A VMCP server exposes a single skill's tools and snippets as a standalone MCP endpoint. Create one via the UI (`Virtual MCP Servers` → `Create VMCP Server`) or the REST API: ```bash curl -X POST "http://localhost:8000/vmcp_servers/?name=my-skill&skill_uuid=<uuid>" ``` Connect an MCP client to `http://localhost:<assigned-port>/sse`. ### Support Multiple MCP Backends Follow the steps outlined in [Connecting MCP as a backend](docs/mcp-backend.md). ## Virtual NFS Servers (vNFS) 🗂️ A vNFS server exposes a single skill as a **mountable read-only filesystem** over WebDAV or NFSv3. This lets any tool — including Claude Code — read skill files directly via `mount` or `rclone`, without going through the REST API. Create a vNFS server via the UI (`Virtual NFS Servers` → `Create vNFS Server`) or the REST API: ```bash # WebDAV (default) curl -X POST "http://localhost:8000/vnfs_servers/?name=my-skill&skill_uuid=<uuid>&protocol=webdav" # NFSv3 curl -X POST "http://localhost:8000/vnfs_servers/?name=my-skill&skill_uuid=<uuid>&protocol=nfs" ``` ### Mounting — WebDAV The easiest option is `rclone` (no root required, works on Linux, macOS, WSL2): ```bash # Install: brew install rclone / apt install rclone / dnf install rclone rclone mount :webdav: /mnt/skill \ --webdav-url=http://localhost:<port>/<skill-name> \ --read-only --daemon # Unmount fusermount3 -u /mnt/skill ``` Alternatively, with `davfs2`: ```bash # Install: apt install davfs2 / dnf install davfs2 sudo mount -t davfs http://localhost:<port>/<skill-name> /mnt/skill # Unmount sudo umount /mnt/skill ``` ### Mounting — NFSv3 ```bash # Install: apt install nfs-common / dnf install nfs-utils sudo mount -t nfs localhost:/ /mnt/skill \ -o port=<port>,mountport=<port>,nfsvers=3,proto=tcp,nolock,soft # Skill files are at /mnt/skill/<skill-name>/ # Unmount sudo umount /mnt/skill ``` The UI `Virtual NFS Servers` detail page shows the exact commands pre-filled with the correct port and skill name. ## Run SBS with GitHub backend Follow the steps outlined in [Github backend](docs/github_using_hooks.md). ## Monitoring the Service 📈 ### To start a local Prometheus server execute: ```bash echo -e "global:\n scrape_interval: 5s\nscrape_configs:\n - job_name: \"skillberry-store\"\n static_configs:\n - targets: [\"localhost:8090\"]\n metric_relabel_configs:\n - source_labels: [__name__]\n regex: '.*_created'\n action: drop" > /tmp/prometheus.yml docker run --rm --name prometheus --network="host" -p 9090:9090 -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus --config.file=/etc/prometheus/prometheus.yml ``` Metrics are available in Prometheus at http://localhost:9090. > Note: Application metrics are prefixed with `SBS_`. ### To start a local Jaeger server execute: ```bash docker run --rm --name jaeger --network="host" -p 4317:4317 -p 16686:16686 jaegertracing/all-in-one:latest ``` Traces are available in Jaeger at http://localhost:16686. ## 📚 Additional documentation can be found at [docs](docs). * Customizing configurations details can be found [here](docs/config-env-vars.md) * Guthub support details can be found [here](docs/github_using_hooks.md)