mcp_skill 0.2.0

Skill definitions and runtime execution for AI capabilities with LLM integration, MCP tools, and claim recording for the MCP ecosystem.

0.2.0 - 2026-04-28 - Bundle Subsystem & Multi-Skill Runtime

Added

• Bundle subsystem — BundleLoader, BundleValidator, capability types / composer / verifier, permission enforcer.
• Multi-skill execution — Procedure model with branching, branch merger, circuit breaker, error aggregator.
• Embedded expression language (lexer / parser / AST / evaluator / function registry) for skill conditionals and templated values.
• Evaluation subsystem (rubric-based scoring) and FactGraph integration for claim recording.
• Standard port adapters — SkillRuntimePortAdapter, SkillRegistryPortAdapter implementing mcp_bundle Contract Layer.
• In-memory MemorySkillRegistry.

Changed

• Runtime restructured around SkillRuntime with context builder, procedure executor, claim extractor.
• New dependency: mcp_bundle ^0.3.0.

Removed

• Legacy ports — fact_graph_port, knowledge_port, mcp_port shims (consumers receive capability ports through skill_ports.dart now).

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0.1.1 Bugfix

Fixed

• Unified LLM Port types to use mcp_bundle Contract Layer exclusively
• Updated documentation to reflect Contract Layer architecture

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0.1.0 Initial Release

Added

Core Features

• Skill Definitions

  • Skill model with input/output schemas
  • JSON Schema validation for inputs and outputs
  • Metadata support (tags, categories, version)
  • Multiple executor types (LLM, MCP, Handler)

• Skill Runtime

  • SkillRuntime as the main execution engine
  • Skill registration and discovery
  • Execution with full lifecycle management
  • Timeout handling and error recovery

• Execution Results

  • SkillExecutionResult with success/failure status
  • Output capture and validation
  • Execution metrics (duration, timestamps)
  • Error details and stack traces

• Claim Recording

  • Automatic claim generation from skill outputs
  • Entity association for fact graph integration
  • Confidence scoring for claims

• Port-Based Architecture

  • SkillStoragePort for skill persistence
  • LlmPort for LLM integration (from mcp_bundle Contract Layer)
  • McpPort for MCP tool calls
  • KnowledgePort for knowledge access

• In-Memory Implementations

  • Complete in-memory storage for testing
  • Stub implementations for all ports

Executor Types

• SkillExecutorType.llm - LLM-powered execution
• SkillExecutorType.mcp - MCP tool execution
• SkillExecutorType.handler - Custom handler execution

Data Models

• Skill - Skill definition with schemas
• SkillExecution - Execution context and state
• SkillExecutionResult - Execution outcome
• SkillInfo - Lightweight skill metadata

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Support and Contributing

• Report Issues
• Join Discussions
• Read Documentation
• Support Development