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.
