meta-session-wrapper

To extract reusable patterns from a completed session, submit the transcript to identify multi-step workflows with repeated elements. The tool formalizes inputs, outputs, trigger conditions, and domain knowledge into a generalizable pattern for automation.

Turning a session transcript into a formal feature request requires documenting high-level steps, side effects, and a reusability assessment. This abstraction process converts ad-hoc session decisions into generic, automated patterns suitable for generation.

You can generate an LLM structure from sessions containing multi-step workflows with clear variation in inputs and repeated elements. Sessions lacking these generalizable patterns or concrete decisions are not suitable for formal skill conversion.

Documenting trigger conditions and side effects during pattern extraction involves identifying concrete actions from a session and abstracting them. This structured approach ensures all domain knowledge and variations are captured to support formal generation.

You should not use session analysis for skill generation when the session lacks multi-step workflows or repeated elements. Without clear variation in inputs and generalizable patterns, the extracted feature request will not support effective automation.