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.
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