Claude Skills MCP Server

# Roadmap This document outlines planned future enhancements for the Claude Skills MCP Server. ## Overview The current implementation uses RAG-based semantic search to help Claude discover relevant skills. Future versions will expand capabilities to include code execution, binary tools, and more intelligent skill selection using MCP's advanced features. ## Planned Features ### 1. Sandboxed Python Execution **Status**: Planned **Goal**: Execute Python code that is part of skill definitions in a secure, isolated environment. **Use Cases**: - Skills that include setup scripts or data processing utilities - Interactive skill demonstrations - Pre-validation of skill code before Claude uses it - Running skill-specific tests or examples **Technical Considerations**: - Container-based isolation (Docker, Podman, or gVisor) - Resource limits (CPU, memory, network, filesystem) - Execution timeout mechanisms - Python environment management (dependencies, versions) - Security sandboxing (no host filesystem access, restricted network) **Implementation Options**: - **Docker/Podman**: Industry standard, well-tested isolation - **PyPy Sandbox**: Lighter weight, Python-specific - **WebAssembly (WASM)**: Future-proof, excellent isolation - **Firecracker/gVisor**: Lightweight VM approach **Configuration Example** (proposed): ```json { "execution": { "enable_python": true, "sandbox_type": "docker", "timeout_seconds": 30, "memory_limit_mb": 512, "allow_network": false, "allowed_packages": ["numpy", "pandas", "scikit-learn"] } } ``` ### 2. Binary Execution Support **Status**: Planned **Goal**: Enable skills to include and execute compiled binaries or system tools. **Use Cases**: - Bioinformatics tools (BLAST, BWA, SAMtools) - Computational chemistry software (RDKit CLI, OpenBabel) - System utilities required for scientific workflows - Performance-critical components **Technical Considerations**: - Platform compatibility (Linux, macOS, Windows) - Binary verification and checksums - Dependency management (shared libraries) - Even stricter sandboxing than Python (system calls restrictions) - Digital signatures for trusted binaries **Security Concerns**: - Binary provenance verification - Malware scanning - Restricted syscalls (seccomp, AppArmor, SELinux) - No privilege escalation **Configuration Example** (proposed): ```json { "execution": { "enable_binaries": true, "allowed_binaries": { "blast": { "path": "/usr/local/bin/blastn", "checksum": "sha256:abc123...", "max_runtime": 300 } }, "binary_sandbox": "firejail" } } ``` ### 3. MCP Sampling-Based Skill Selection **Status**: Planned (highest priority for intelligence improvement) **Goal**: Replace or augment RAG with MCP's `sampling` feature for more intelligent, context-aware skill selection. **Current Limitation**: The RAG approach uses vector similarity on skill descriptions, which: - Misses contextual nuances from the conversation - Can't dynamically reason about skill combinations - Doesn't learn from conversation history - Limited to semantic similarity (no reasoning) **MCP Sampling Approach**: Instead of pre-computing embeddings, use MCP's `sampling` feature to let the LLM directly reason about which skills are most relevant given the full context of the conversation. **How It Would Work**: 1. Claude requests skill selection via MCP sampling 2. Server provides all available skills (or metadata) as context 3. LLM reasons about task requirements and skill applicability 4. Returns ranked, contextually-appropriate skills with justification **Benefits**: - **Contextual awareness**: Considers entire conversation, not just current query - **Reasoning-based**: Can understand complex multi-step tasks - **Combination detection**: Identifies when multiple skills should be used together - **Adaptive**: Improves as conversation progresses - **Explainable**: LLM can explain why it chose specific skills **Technical Implementation**: - Expose skill catalog as MCP resource - Use MCP sampling prompts for skill selection reasoning - Optional: Hybrid approach (RAG for initial filtering, sampling for final selection) - Cache LLM selection decisions to reduce latency **Configuration Example** (proposed): ```json { "skill_selection": { "method": "sampling", // or "rag" or "hybrid" "sampling_config": { "model": "claude-3.5-sonnet", "max_tokens": 2048, "temperature": 0.3, "system_prompt": "Select the most relevant skills for the task..." }, "hybrid_config": { "rag_prefilter": 20, "sampling_final_selection": 5 } } } ``` **References**: - [MCP Sampling Specification](https://modelcontextprotocol.io/docs/concepts/sampling) - [Using Sampling for Intelligent Tool Selection](https://modelcontextprotocol.io/docs/patterns/sampling) ### 4. Skill Composition and Workflows **Status**: Future consideration **Goal**: Enable skills to reference and compose other skills into workflows. **Use Cases**: - Multi-step scientific analysis pipelines - Data preprocessing -> analysis -> visualization chains - Conditional skill execution based on results **Technical Considerations**: - Workflow definition format (YAML, JSON) - Dependency graphs - Error handling and rollback - Parallel execution where possible ### 5. Dynamic Skill Updates **Status**: Future consideration **Goal**: Hot-reload skills without server restart. **Use Cases**: - Rapid skill development iteration - Community-contributed skill repositories - Periodic pulls from GitHub sources **Technical Considerations**: - Filesystem watching for local skills - GitHub webhooks or polling - Re-indexing strategy (incremental vs. full) - Cache invalidation ## Implementation Priorities ### Phase 1: Foundation (Current) - ✅ Vector-based semantic search - ✅ GitHub and local skill loading - ✅ Basic MCP server implementation ### Phase 2: Intelligent Selection (Next) - 🔄 MCP sampling-based skill selection - 🔄 Hybrid RAG + sampling approach - 🔄 Conversation context integration ### Phase 3: Execution Capabilities - ⏳ Sandboxed Python execution - ⏳ Security hardening and isolation - ⏳ Resource management ### Phase 4: Extended Execution - ⏳ Binary execution support - ⏳ Cross-platform compatibility - ⏳ Trusted binary registry ### Phase 5: Advanced Features - ⏳ Skill composition and workflows - ⏳ Dynamic skill updates - ⏳ Performance optimizations ## Contributing to the Roadmap We welcome community input on priorities and new feature ideas: 1. **Discuss**: Open an issue to discuss the feature 2. **Design**: Collaborate on technical approach 3. **Implement**: Submit PR with tests and documentation 4. **Review**: Maintainer review and feedback For significant features (especially execution-related), security review is mandatory. ## Timeline These features are planned but not yet scheduled. Priority will be determined by: - Community demand and contribution - Security and stability considerations - Integration with Claude and MCP ecosystem developments - K-Dense AI's commercial platform requirements ## Questions or Ideas? - Open a [GitHub Issue](https://github.com/K-Dense-AI/claude-skills-mcp/issues) - Email: [orion.li@k-dense.ai](mailto:orion.li@k-dense.ai) --- **Legend**: - ✅ Completed - 🔄 In Progress - ⏳ Planned