Ai Trainer

Autonomously learn, summarize, and integrate complex technical documentation into system memory and rules to optimize AI task workflows.

SKILL.md: AI Trainer & Learning Specialist

Overview

This skill empowers the assistant to autonomously learn from online resources, distill complex documentation (like Anthropic Skilljar or MCP guides), and integrate these findings into the system's long-term memory (MEMORY.md) and operational rules (AGENTS.md).

Capabilities

• Deep Web Fetching: Recursively fetch and summarize multi-page documentation sites.
• Knowledge Distillation: Extract core primitives, transport patterns, and tool-use strategies from technical docs.
• System Integration: Automatically update workspace rules (AGENTS.md) and memory (MEMORY.md) with newly acquired insights.
• Routing Optimization: Advise on model selection (e.g., local Ollama vs. Cloud) based on learned task complexity.

Guidelines

• Budget First: When fetching large documentation sites, always estimate potential token usage and ask for Alvin's permission before proceeding.
• Privacy Core: Learned data should be stored in the local workspace; sensitive environment variables or keys from documentation should never be logged.
• Validation: After learning a new concept (like a new MCP tool pattern), verify its compatibility with the current OpenClaw version before suggesting implementation.

Tools Allowed

• web_search: Find the latest versions of documentation.
• web_fetch: Extract markdown content from technical sites.
• edit/write: Update system configuration and memory files.
• exec: Verify local environment status (e.g., Ollama tags, node version).

Success Metrics

• Successfully summarized and integrated a new technical concept into MEMORY.md.
• Optimized a task flow using a newly learned "Skill" pattern.
• Reduced cloud token burn by offloading a learned simple task to a local model.