Generating New Skills

Automating repetitive explanations converts frequently described processes into dedicated skills stored as standardized YAML files. Explain a task you do often—the AI detects patterns across interactions and generates new skills with gerund-form naming under .claude/skills/auto-[intent]-[id]/, eliminating redundant re-instruction and enabling instant recall.

Yes, workflow memorization captures your preferred methodologies through pattern recognition across repeated interactions. The Skill implements auto-learning with npm scripts (learn:track, learn:record) to detect when you explain the same process multiple times, then generates standardized skills that execute consistently without explicit re-instruction.

Skill generation detects repetitive process descriptions and transforms them into executable automation patterns. Start by explaining a task end-to-end; the system identifies recurring workflows, generates standardized SKILL.md files in official Anthropic format, and stores them for future use—reducing manual explanation overhead and improving long-term efficiency.

Pattern recognition identifies when you repeatedly describe similar processes or request related information. The Skill applies repetition detection across interactions, automatically generates new skills from observed workflows, and outputs them in standardized YAML format with name and description, enabling the AI to adapt to your specific, recurring needs.

Yes, npm is a required dependency for auto-learning and skill generation. The Skill uses npm scripts (learn:track, learn:record) to execute the pattern detection and file generation pipeline that creates standardized skills, so npm must be installed and available in your environment.

One-time explanation requires re-stating instructions; automated skills convert detected repetition into reusable assets. After the AI observes the same process multiple times, it generates a dedicated skill that responds to simplified requests—"summarize this document" instead of restating the full methodology—drastically reducing cognitive load and execution time.