Local MCP server and CLI for learned AI guardrails, memory, skills, and agent config injection
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# AI Rule Learning MCP AI Rule Learning MCP is a local MCP server and CLI for turning repeated AI-session feedback into persistent guardrails, memory, and reusable skills that can be injected into supported AI-agent configuration files. This README describes what is implemented in this repository today. Earlier decision-rule mining examples have been removed because those public classes are not part of the current package API. --- ## What is real today? | Capability | Status | Implemented as | | ------------------------ | --------------- | --------------------------------------------------------------- | | MCP server | Real | `ai-rule-learning-mcp` entry point | | CLI | Real | `ai-rule-learning` entry point | | Guardrail rules | Real | Local JSONL storage plus injected markdown blocks | | Memory | Real | Local JSONL storage plus injected markdown blocks | | Skills | Real | Local markdown skill files plus injected skill index | | Agent config injection | Real | Claude Code, Cursor, Windsurf, and GitHub Copilot targets | | Auto-sync scheduler | Real | LaunchAgent, systemd user timer, or cron fallback | | Decision-rule mining API | Not current API | The current package exposes MCP tools and CLI commands instead. | --- ## Installation ```bash pip install ai-rule-learning-mcp ``` > **Note on MCP SDK Compatibility:** Compatible with `mcp>=1.0.0,<2.0.0`. See [MCP Versioning Guide](../docs/mcp-versioning.md) for details on supported SDK versions and the 2.x migration path. Run the MCP server: ```bash ai-rule-learning-mcp ``` Run the CLI directly: ```bash ai-rule-learning status ``` --- ## MCP setup Add the …
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